rivista semestrale | half-yearly journal 04 15jcolore.gruppodelcolore.it/numeri/pdf/R0415_v04_11_2015_secured.pdf · 04 15 rivista semestrale | half-yearly journal Rivista dell’Associazione

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Rivista dell’Associazione Italiana Colore www.gruppodelcolore.it

CULTURA E SCIENZA

DEL COLORE

COLOR CULTURE AND SCIENCE

http://www.gruppodelcolore.it

CULTURA E SCIENZA DEL COLORECOLOR CULTURE AND SCIENCERivista dell’Associazione Italiana Colorehttp://jcolore.gruppodelcolore.it/ISSN 2384-9568

DIRETTORE RESPONSABILE | EDITOR-IN-CHIEF Maurizio Rossi

VICEDIRETTORE | DEPUTY EDITOR Davide Gadia

COMITATO SCIENTIFICO | SCIENTIFIC COMMITTEEJohn Barbur (City University London, UK)Berit Bergstrom (NCS Colour AB, SE)Giulio Bertagna (B&B Colordesign, IT)Janet Best (Natific, UK)Aldo Bottoli (B&B Colordesign, IT)Patrick Callet (École Centrale Paris, FR)Jean-Luc Capron (Université Catholique de Louvain, BE)Osvaldo Da Pos (Università degli Studi di Padova, IT)Bepi De Mario (CRASMI, IT)Hélène DeClermont-Gallernade (Chanel Parfum beauté, FR)Reiner Eschbach (Xerox, USA)Alessandro Farini (INO-CNR, IT)Christine Fernandez-Maloigne (University of Poitiers, FR)Renato Figini (Konica-Minolta, EU)Davide Gadia (Università degli Studi di Milano, IT)Robert Hirschler (Serviço Nacional de Aprendizagem Industrial, BR)Sandra Krasovec (Fashion Institute of Technology, USA)Francisco Imai (Canon, USA)Lia Luzzatto (Color and colors, IT)Kevin Mansfield (UCL, UK)Veronica Marchiafava (IFAC-CNR, IT)Gabriel Marcu (Apple, USA)Manuel Melgosa (Universidad de Granada, ES)Anna Grazia Mignani (IFAC-CNR, IT)Annie Mollard-Desfour (CNRS, FR)Maria Luisa Musso (Universidad de Buenos Aires, RA)Claudio Oleari (Università degli Studi di Parma, IT)Laurence Pauliac (Historienne de l’Art et de l’Architecture, Paris, FR)Marcello Picollo (IFAC-CNR, IT)Renata Pompas (AFOL Milano-Moda, IT)Boris Pretzel (Victoria & Albert Museum, UK)Noel Richard (University of Poitiers, FR)Katia Ripamonti (Cambridge Research System, UK)Alessandro Rizzi (Università degli Studi di Milano, IT)Maurizio Rossi (Politecnico di Milano, IT)Jodi L. Sandford (Università di Perugia, IT)Raimondo Schettini (Università degli Studi di Milano Bicocca, IT)Gabriele Simone (ST Microelectronics, IT)Andrea Siniscalco (Politecnico di Milano, IT)Ferenc Szabó (University of Pannonia, HU)Mari UUsküla (Tallinn University, EE)Francesca Valan (Studio Valan, IT)Ralf Weber (Technische Universität Dresden, DE)Alexander Wilkie (Charles University in Prague, CZ)

COLLABORATORI | CONTRIBUTORSWalter Arrighetti, Simone Bianco, Rossella Catanese, Gloria Menegaz, Emanuela Orlando, Giulia Paggetti, Renata Pompas, Jodi L. Sandford, Raimondo Schettini, Jada Schumacher, Arvi Tavast, Mari Uusküla, Veronica Valdegamberi, Paola Valentini

REDAZIONE | EDITORIAL STAFFAldo Bottoli, Daria Casciani, Davide Gadia, Veronica Marchiafava, Francesca Valan

EDITORE | PUBLISHERGruppo del Colore – Associazione Italiana Colorewww.gruppodelcolore.it

04NUMERO 04 - OTTOBRE 2015 NUMBER 04 - OCTOBER 2015

GRUPPO DEL COLOREASSOCIAZIONE ITALIANA COLORE

CULTURA E SCIENZA DEL COLORECOLOR CULTURE AND SCIENCE

Rivista dell’Associazione Italiana ColoreRegistrazione presso il Tribunale di MIlano

al n. 233 del 24.06.2014


SOMMARIO | SUMMARY

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Repainting the mechanical ballet. Restoration of colours in ‘Ballet mécanique’ by Fernand Léger by Rossella Catanese

On the perceptual/linguistic origin of the twelfth basic color term in the Italian color lexiconby Veronica Valdegamberi, Giulia Paggetti, Gloria Menegaz

Motion Picture Colour Science and film ‘Look’: the maths behind ACES 1.0 and colour gradingby Walter Arrighetti

A Cognitive Linguistic Usage Perspective: What is Italian Blu, azzurro, celeste? Do English agree on BLUE semantics?by Jodi L. Sandford

Society in colour: Italian television and the switch to colours by Paola Valentini

Color, Mon Dieu: A Case-Study Comparison Between The Church Of The Epiphany (New York City) and Kresge Chapel (Cambridge, Massachusetts)by Jada Schumacher

How blue is azzurro? Representing probabilistic equivalency of colour terms in a dictionary by Arvi Tavast, Mari Uusküla

Adaptive Illuminant Estimation and Correction for Digital Photographyby Simone Bianco, Raimondo Schettini

I colori di Gualtiero Marchesi | Gualtiero Marchesi’s colours by Emanuela Orlando

Jean-Gabriel Causse, Lo stupefacente potere dei colori, Ponte alle Grazie, Milano, 2015a cura di Renata Pompas

RUBRICA | COLUMN

4 Cultura e Scienza del Colore - Color Culture and Science | 04/15

1Rossella [email protected]

1Dipartimento Storia dell’Arte e dello Spettacolo, Sapienza Università di Roma

Repainting the mechanical ballet. Restoration of colours in ‘Ballet mécanique’ by Fernand Léger

1. INTRODUCTION

This paper comes from a research [1] carried out two years ago during the Haghefilm Foundation internship programme. Haghefilm Foundation was a non-profit international organization created by the laboratories Haghefilm Conservation in order to improve research and support activities related to techniques and technologies of film preservation and restoration [2]. Into this framework, Haghefilm Foundation’s main goal was to promote results of the research, but this often leads to deal with copyright holders instead of screening and proposing experimental researches in educational contexts just for free knowledge.

2. FILM RESTORATION

Since the Eighties of the last century, after UNESCO conference [3], film has been institutionally considered as cultural, rather than a commercial item. It has been given a systematic, academic and ethical disposal to film conservation and restoration practices. Films required conservation policies and cold storage for their structural fragility [4]. They have got a number of unchanged features and other elements that we have tried to “improve” in order to preserve them. The films have a part called photosensitive emulsion, from the early years, when the supports were made of flammable cellulose nitrate films, and then when they were made of safety (non-flammable) cellulose acetate, until polyester bases introduced in the Eighties and currently used in the production-distribution market.This emulsion may have a wide range of components, including salts such as silver nitrate or chromogenic couplers in the emulsions of colour film, but it is interesting to know that the function of emulsifier is given by the gelatine, an organic product. This “organic” dimension in the film, susceptible to degeneration, further guarantees its syncretic charm. Film is therefore an art that tremble of vivid emotions and has got in its own materials an animated, living element.Restoring an audiovisual text is always an operation on the formal setting and every different medium introduces different formal connotations. The restoration of an audiovisual support is the re-formalization

and the reactivation of the text according to its structure, examination of the text and of its gaps, hermeneutic activity which consents to recreate an identity opening to possible further interpretative operations [5]. Actually restoration is always an exegesis work, deep-rooted in practice, but there is always a philological research, exploring efficient methodologies to understand and interpret the work.The problem of modernity, as historical dialogue and methodological customs, is a basic principle in any restoration. In fact resetting a text is always a dialogue with a past, near or far, and it becomes a comparison between two different ages. Every restoration is a liaison with a memory. A useful definition for the audiovisual restoration is proposed by Paolo Cherchi Usai:

«Restoration is the set of technical, editorial and intellectual procedures aimed at compensating for the loss or degradation of the moving image artefact, thus bringing it back to a state as close as possible to its original conditions» [6].

The audiovisual support is radically different from the artistic unicum, so also the idea of original can change if you consider a philological-textual level or a material level. It will be different also the gaps treatment: the video, as film and photography, has got intrinsically the concept of copy, related to the idea of technical reproducibility, giving also a different perception, not one and synchronous. The introduction of the numeric information among the images system allowed a wide flexibility of action on images, but a digital medium implies a trans-coding process which uses calculation by discrete, discontinue values, turning light waves into numeric units (from digit = decimal number). Even if digital system is a completely different structure of representation, these media are now in a transition age [7]. Thorburn and Jenkins wrote:

«in our current moment of conceptual uncertainty and technological transition, there is an urgent need for a pragmatic, historically informed perspective that maps a sensible middle ground between the euphoria and the panic surrounding new media […]» [8].

504/15 | Cultura e Scienza del Colore - Color Culture and Science

Digital technology ensures data repeatability without alterations and a high potential of intervention on data. The technological advances, as the realisation of processors and more powerful computer buffer, make the digital arriving in cinema in higher standards.

3. A CASE STUDY: BALLET MÉCANIQUE

3.1 THE FILMThe EYE Film Institute decided to commission the restoration of its hand coloured copy of Ballet mécanique (1924), Fernand Léger’s masterpiece of the European Twenties Avant-garde, with Kiki de Montparnasse, Dudley Murphy and Katherine Murphy.For EYE, the restoration lab Haghefilm acquired a 2k digital scan in order to produce a digital intermediate for both the colour and the black and white parts (requiring positive editing for the projection prints). But another experimental project has been proposed, i.e. to produce another, unique black and white positive copy with hand-painted or tinted sections matching, as closely as possible, the original nitrate. My duties consisted in running colour tests under the supervision of Paolo Cherchi Usai, Ulrich Ruedel and Daniela Currò. Furthermore, I pursued research about the history of this film and its moot authorship.Sought by many film archives in all the world (New York, Paris, Mexico, Montreal, Bucharest, Havana, London, München, Wien, Berlin, Los Angeles, Berkeley, Belgrade, Stockholm, Turin, Cambridge, Washington, Canberra, Roma, Ottawa, Copenhagen), there are several versions of Ballet mécanique. According to Giovanni Lista, this film has to be regarded as a work in progress, whose drafting continued for years, without an original [9]. The nitrate coloured print owned by the Dutch Filmmuseum came from property of Filmliga, a cultural association born in 1927 in Amsterdam, focused on the collection and the diffusion of Avant-garde movies; it had a great season between the 1928 and the 1930, and broke up in the 1931. Then the collection of the association has been acquired by the Filmmuseum, that still keep those films and among them Ballet mécanique [10]. Lawder defined Ballet mécanique as

«the classic example of a fully developed painting aesthetic transposed into film by a modern artist» [9].

The film is one of the most contested artworks of the Twentieth century. Its genesis involved different persons: Fernand Léger, Dudley Murphy, Man Ray, Ezra Pound, Georges Antheil. Every

one of them worked on the film giving an artistic contribution. It has been a collaborative work but the question of the authorship is still now open to debate. Some art historians and film scholars have different interpretations: Standish Lawder considers Léger as the creator, and Murphy as an assistant; Judi Freeman and Susan Delson emphasize the crucial contribution of Murphy and involves Man Ray in the authorship. William Moritz minimizes Léger’s contribution, ascribing the creation mainly to Murphy and Man Ray [11-14].In the earliest known print of the film, the opening credit reads “F. Leger and Dudley Murphy present Ballet Mechanique”; and Léger’s notes on the film, prepared as it was nearing completion in 1924, call it a “Film by Fernand Léger and Dudley Murphy” [14-15]. In Ballet mécanique’s film credits Murphy’s contributions disappeared. Neither Man Ray nor Pound nor Antheil was even appointed. Man Ray in his autobiography Self Portrait affirmed:

«and that is how Dudley realized the Ballet mécanique, which had a certain success, with Léger’s name» [16].

3.2 THE RESTORATION PROJECTHaghefilm scanned both the Dutch 309m positive nitrate copy, coloured, and the Cinémathèque Française 291m positive black and white print, in order to get the best results comparing editing and sequences; the French 291m copy is denser, darker and less clear than the Dutch one, probably because the French print comes from later generation elements. The editing order is slightly different. Even if it is black and white, the French copy presents sections coming from a coloured source, with clearly denser frames. I checked the entire Dutch copy shot by shot and frame by frame and I wrote accurate descriptions about the content of the shots, the frame-counting, the evenness of saturation, the kinds of applied colour, the splices and the damages. I found that every tinted section has been spliced and a lot of hand-painted sections were not spliced, but directly painted with many overlappings of colours at the frame lines. According to Paul Read, during the hand-painting period the dye used for hand colouring were quite similar to those used for tinting: paints or acid dyes in water, they were absorbed easily by the gelatine of the emulsion side [17].Then we attended the digital grading. The Filmmuseum asked for a higher contrasted look in order to recreate the shocking effect of that famous and charming avant-garde feature. The experimental project proposed by Filmmuseum provided us with the opportunity to paint the coloured frames like they look on the nitrate copy. In the chemical lab of the Haghefilm I have tried many different tinting experiments in order


to learn how to work with tinting processes. I ran 62 tests in tinting and hand-painting, testing hues, concentrations, dipping time, mixtures, applications. But the positive black and white print made by the digital intermediate presented a bad grey density which, overlapping the applied colours, looks quite different from the original. We thought it was possible to work digitally taking out the colours and the brushstrokes, in order to underline the black and white image and to get a clear and vivid picture where applied colours could be visible and faithful to the original. Basically using the Hue/Saturation software tools for the RGB channels might work like a “reverse filter” for showing the colour subtraction. In this process, theoretically correct, we were not aware that the applied dyes were not pure colours. Furthermore, we did not consider the overlapping of colours, which mixes hues regardless of the frame line. Some colour data were mixed in the brushstroke marks and we were able to find it by looking at the Magenta sections visible in the Red frame, also visible by the Blue channel. So we thought to compare the coloured sections, triangles and circles, of the Dutch copy with the ones seen on the black and white French element, in order to use those sections in the new black and white print that we will have to paint and tint. Those sections have been already scanned and we checked frame by frame analyzing the two copies in comparison and producing a new record about the differences. Many sections have been taken by this element reducing the digital intervention in compositing and the time spent in the digital work on the singles frames.This restoration has not been finished: one of Haghefilm’s requests (improving of research on film restoration and dissemination of its results) was that the hand coloured print of the film had to be made available by EYE to the Haghefilm Foundation for exhibition in educational, research, and outreach activities with mention of EYE as the source of the material. EYE could not agreed to this claim because of the “copyright dilemma”: the Dutch cinémathèque can’t assign anyone the right to show the print because it doesn’t own that right, which is true for almost every film they preserve and restore. In some cases, like the so-called “orphan films” (works that have been abandoned by its owner or copyright holder), it wouldn’t have been a problem, but in the case of Ballet mécanique it actually was, because it is a film were the right holders are very well known, so it was impossible for Haghefilm to get the aforementioned rights. Of course this negative event should be a chance to think about the mission of films restoration, about the free spread of research and its results.

4. CONCLUSIONS

I believe that this unique experience will be useful to understand how to balance the digital technologies, helpful and favourable to get the best achievements, with the traditional and fashionable applied colours, rediscovered example of how film arts has been close to the fine pictorial art.

BIBLIOGRAPHY

[1] R. Catanese, Lacune binarie. Il resamaranta2015tauro dei film e le tecnologie digitali, Bulzoni, Rome, 2013; R. Catanese, G. Edmonds, B. Lameris, Hand-Painted Abstractions: Experimental Color in the Creation and Restoration of Ballet mécanique, in «The Moving Image» Vol. 15, Number 1, Spring 2015, pp. 92-98.

[2] G. M. Paletz, The Finesse of the Film Lab: A Report from a Week at Haghefilm, in «The Moving Image», Vol. 6, Number 1, Spring 2006.

[3] UNESCO conference, Belgrade 27th October 1980. http://portal.unesco.org/en/ev.php-URL_ID=13139&URL_DO=DO_TOPIC&URL_SECTION=201.html (last visit in 06/08/2015).

[4] C. Frick, Saving Cinema. The Politics of Preservation, Chicago, Oxford University Press, 2011;

[5] P. Bertetto, L’eidetico, l’ermeneutica e il restauro del film (1991), in S. Venturini, Il restauro cinematografico: principi, teorie, metodi, Campanotto, Udine, 2006, pp. 104-106

[6] P. Cherchi Usai, Silent Cinema. An Introduction, British Film Institute, London, 2000, p. 66

[7] G. Fossati, From Grain to Pixel. The Archival Life of Film in Transition, Amsterdam University Press, Amsterdam, 2009

[8] D. Thorburn and H. Jenkins, Rethinking Media Change: The Aesthetics of Transition, MIT Press, Cambridge, 2003, p. 2

[9] G. Lista, Léger scénographe et cinéaste, in Id., Fernand Léger et le spectacle, Editions de la Réunion des Musées nationaux, Paris, 1995

[10] G. Manduca, La lavandaia sulle scale. Una nota filologica al ‘Ballet mécanique’, in P. Bertetto e S. Toffetti, Cinema d’avanguardia in Europa, Il Castoro, Milano, 1996

[11] S. Lawder, Cubist Cinema, New York University Press, New York, 1975, p. 65

[12] J. Freeman, Bridging Purism and Surrealism: the Origins and Production of Fernand Léger’s ‘Ballet Mécanique’, in R.E. Kuenzli, Dada and Surrealist Film, Locker & Owens, New York, 1987

[13] S. Delson, Dudley Murphy, Hollywood Wild Card, Minnesota University Press, Minneapolis, 2006

[14] W. Moritz, Americans in Paris: Man Ray and Dudley Murphy, in J.-K. Horak, Lovers of Cinema: The First American Film Avant-Garde 1919–1945, University of Wisconsin Press, Madison, 1995, pp. 118-136 e p. 199

[15] Published in the original French in «L’Esprit Nouveau»


no. 28, undated (most likely November or December 1924)

[16] Man Ray, Self Potrait, Little Brown, Boston-Toronto, 1963, p. 218

[17] P. Read, ‘Unnatural Colours’: An Introduction to Colouring Techniques in Silent Era Movies, in «Film History», vol. 21, no. 1, pp. 9-46


1Veronica [email protected] [email protected] 2Gloria [email protected]

1Department of Linguistics, University of Verona, Italy2Department of Computer Science, University of Verona, Italy

On the perceptual/linguistic origin of the twelfth basic color term in the Italian color lexicon

1. INTRODUCTION

The seminal work of Berlin and Kay introduced the concept of universal basic color terms (BCTs) [1], namely red, green, blue, yellow, orange, purple, pink, brown, gray, white, and black. According to their definition, color terms are operationally defined as basic only if monolexemic and psychologically salient for all speakers, but not if restricted in application to narrow classes of objects, or included in the signification of other basic color terms. They proposed that these color words act as focal points for all the basic color categories in all the languages with a developed color vocabulary. In the last decade, the restriction to eleven universal basic colors has been put into question. Some researchers provided evidence on the existence of an additional basic term in the category of blue for the Greek, Turkish, Russian and, more recently, Maltese languages [2-6]. In the same line, our work [7-15] has emphasized the need for the Italian speakers to assign the term corresponding to light blue, azzurro, to a separate category. In particular, in the constrained color naming experiment described in [7] participants were instructed to name samples of colors using only the eleven basic terms identified by Berlin and Kay. After performing the experiment many participants reported the uneasiness on using the term blu (blue) to label colors in the lighter blue portion of the spectrum. In addition, the

lightness of the centroid for the blu category was well above the lightness of the category focal. In order to disambiguate the role of linguistic and perceptual mechanisms in color categorization and to shed light on the existence of the twelfth category in Italian, other experiments were performed and the outcomes of these confirmed our conjecture. Here we report on both an unconstrained color listing experiment and a Stroop test with (in)congruent word/ink parings [8, 16]. We refer to [7-15,19,20] for additional information. This paper is organized as follows. Section 2 describes Experiment 1 (color listing) and briefly summarizes the outcomes of Experiment 2 (Stroop test). Section 3 illustrates the results and Section 4 presents the conclusions.

2. METHODS

2.1 EXPERIMENT 1: COLOR LISTINGThe color listing experiment was designed to determine whether there is a measurable difference in the spontaneous recall frequency of names associated with basic colors with respect to other color categories. In this way, the recall frequency can be considered as a further indication of the semantic difference between basic and non-basic colors and thus of the “basicness” of a given color name. Accordingly, the recall frequency for the term azzurro was assessed by a color listing experiment.

ABSTRACT

Color categorization involves a cognitive mechanism that assigns linguistic labels to perceived colors. In their pioneering study of 1969, Berlin and Kay proposed the existence of eleven universal basic colors, founding the “universalist” theory on color naming. Since then different studies have been proposed either supporting or refusing such a conjecture based on new evidence. One of the most fascinating elements of this research is the debate on the nature of the color naming mechanism: is it a predominantly perceptual or linguistic effect? An inherent aspect stemming from Berlin and Kay’s conjecture is the “weak relativist hypothesis” concerning the emergence of additional basic color categories besides the established 11, specific to a certain language. In this paper we investigate these two issues and propose some arguments in favor of the existence of a twelfth basic color term in Italian. Following the indications of the results of our previous work, two experiments were performed, focusing on the linguistic and the perceptual aspects, respectively. Results support the hypothesis of the existence of an additional basic category named azzurro (light blue), as it is the case for Turkish, Greek, Russian and Maltese.


A questionnaire was prepared and submitted to the informants participating in the experiment. It consisted of two parts. In the first, demographic data were requested in order to enable the identification of other possible factors influencing the recall rate (e.g. gender, age). The second part was devoted to the color listing task. The requested personal data were: gender, age, education, job, hometown and knowledge of foreign languages or Italian dialects. The participants were also asked whether they had particular involvement in color management in their everyday life (either for their professional activity or in their spare time). Thirty-nine people aged between 15 and 62 volunteered for the experiment. All of them were native Italian speakers. The task consisted of listing all the known color names freelyand without constrains on the type and number of names provided. There was no time limit and response time was not recorded.

2.2 EXPERIMENT 2: STROOP TESTThe Stroop test brought a new argument in favor of the weak relativity hypothesis providing additional evidence of the existence of at least a twelfth color category in the Italian language. The Stroop test was introduced in 1953 by John Ridley Stroop [16] and is based on the assessment of the reaction time in a color naming task. Basically, color name and color ink pairs are shown in either congruent (color name corresponds to ink color) or incongruent (color name and ink color are different) parings. Stroop demonstrated that in the second case (e.g. red ink and green word) the naming of the ink color takes longer and is more prone to errors than in the first (green ink and green word).The aim of the experiment was to assess whether naming the azzurro (light blue) ink color would take longer than naming the blu (dark blue) ink color if both were paired to blu color word. Five experiments were performed for assessing the reaction times in different congruent/incongruent conditions using different combinations of color names and ink color parings in order to avoid the interference from other factors possibly biasing the results of the experiment. In particular, the linguistic nature of the mechanism was investigated by separately performing the experiment on the verde (green) and the blu (blue) categories, respectively. In the first, regarding the blu category, six ink colors were used: dark blue, light blue, red, yellow, purple and pink. The six colors were paired with four colors names rosso, (red), blu (blue), viola (purple) and giallo, (yellow), resulting in 24 possible combinations (e.g. red ink – rosso word, red ink – blu word etc.). In the second experiment, regarding the green category, the two blue ink colors were replaced

by two green colors that were equidistant in lightness and the word blu was replaced by the word verde, while keeping all the other conditions unchanged. The choice of green was motivated by the fact that the perceived colors corresponding to the green category cover a large portion of the spectrum including both high and low lightness values, as it is the case for the English blue. The underlying hypothesis was that statistically significant mean reaction time difference for low and high lightness ink colors when paired to the congruent color word (blu and azzurro, respectively, for the blue category and verde for the green category) would have provided evidence in favor of the predominance of a linguistic rather than a perceptual mechanism. Alternative: The underlying hypothesis was that low and high lightness ink colors, when paired with incongruent color words (blu and azzurro, respectively, for the blue category and verde for the green category) would provide statistically slower mean reaction times than those for the congruent pairs, thus supporting a linguistic rather than perceptual mechanism. In other words, our claim was that if the reaction time was the consequence of a predominantly perceptual effect, the same trend would have been observed for the two categories. Conversely, a different trend would have supported the linguistic hypothesis, since in this case the blu and verde categories would have revealed a different underlying linguistic mechanism affecting reaction time.The third, fourth and fifth experiments were aimed at avoiding the influence of other confounding factors. The Munsell coordinates and the lightness of the colors used were as follows: red 7.5R 5/20, L*=51.85, dark blue 5PB 1/10, L*=12.63, light blue 5PB 6/14, L*=60.91, yellow 5Y 9/12, L*=89.28, purple 2.5P 3/18, L*=30.64, pink 7.5RP 7/10, L*=44.61, dark green 10GY 1/10, L*=13.14, light green 10GY 6/14, L*=61.38. We refer to [8] for more details.

3. RESULTS 3.1 EXPERIMENT 1: COLOR LISTINGResults confirmed the psychological salience of the 11 basic color terms. As shown in Table 1, the basic terms occupy the first positions and reach very high response frequencies. The bianco (white) category was named by all 39 participants, while the basic term recalled with the lowest frequency (33 participants) was viola (purple). Interestingly, the word chosen to designate light-blue was azzurro, which at that time was not considered to be a basic term, reaching frequencies similar to those of other basic terms (35 participants). This supports the hypothesis that Italian speakers do need a term


denoting that particular area of the color space and azzurro seemed to be the best candidate. Beyond azzurro, other terms denoting approximately the same region reached good frequencies, for instance celeste and turchese. After viola there was a sharp variation in the naming frequencies. The total number of provided names was 136. The 12 colors ordered according to decreasing frequency of recall were white bianco (white), rosso (red), giallo (yellow), nero (black), verde (green), marrone (brown), blu (blue), rosa (pink), arancione or arancio (orange), azzurro (light blue), grigio (grey) and viola (purple). The light blue color comes in this list before two

basic colors, gray and purple, which highlights its psychological saliency in the Italian language. Interestingly, for other very wide regions of the color space, like that for green, just one basic term seems to be sufficient. In this experiment almost every subject (90%)

proposed an alternative name for the blue region (beside blue). Interestingly, this was not the case for green (48% only). Moreover, the additional terms proposed for blue were mainly simple terms with high consensus, while the terms for green were compound terms denoting objects and reaching very low frequencies (about 30 such names were provided).As can be seen in the table 1, agreement on an additional term denoting the green region was not reached, in contrast to the case of the blues. There are no simple names widely implemented and known, like azzurro, that might be used in order to enlarge the lexicon referring to the green

Table 1 - Frequency recall of colors Color Name Num. Color Name Num. Color Name Num.bianco 39 giallo canarino 3 bianco sporco 1rosso 37 vermiglio 3 corallo 1giallo 37 rosa cipria 2 rosa antico 1nero 37 verde oliva 2 rosso sangue 1verde 36 (rosso) mattone 2 bianco perla 1marrone 35 grigio perla 2 rame 1blu 35 ecru 2 verdemare 1rosa 35 senape 2 grigioverde 1arancio/arancione 35 blu di prussia 2 topazio 1azzurro 35 rosso scarlatto 2 giada 1grigio 34 bruno 2 carta da zucchero 1viola 33 rosa carne 2 terra 1lilla 17 prugna 2 lavanda 1(giallo) oro 17 acqua marina 2 violetto 1(grigio) argento 16 ghiaccio 2 grigio fumo 1beige 15 cobalto 2 blu aviazione 1fucsia 15 sabbia 2 rosa pallido 1(giallo) ocra 14 (verde) cromo 2 verde muschio 1celeste 13 giallo paglierino 2 verde vescica 1turchese 12 verde mela 2 verde montano 1indaco 12 grigio topo 2 inox 1bordeaux / bordo’ 12 crema 2 ottone 1(rosso) magenta 10 verde militare 2 nero grafite 1(rosso) porpora 10 verde prato 2 nero di seppia 1Blu oltreoceano-mare/oceano 8 verde marcio 2 bianco di zinco 1terra di siena (bruciata) 8 giallo arancio 1 blu ginepro 1verde smeraldo 7 aragosta 1 rosso bandiera 1(bianco) panna 7 rosso vivo 1 rosso rubino 1verde acqua 6 rosso pompadour 1 giallo di napoli 1(rosa) salmone 6 bruno giallo 1 verde bandiera 1giallo limone 6 blu di sevres 1 verde vagone 1amaranto 6 blu cina 1 blu navy 1ciano 5 pervinca 1 khaki 1ciclamino 5 verde foglia 1 testa di moro 1avorio 5 verde cinese 1 grigio polvere 1(grigio) antracite 5 verde imperiale 1 castano 1rosso carminio 5 verde nero 1 verde bosco 1bronzo 5 verde giallo 1 granata 1(verde) petrolio 4 verde ftalo 1 castagno 1pesca 4 porpora rosa 1 pero 1verde pisello 4 ocra rossa 1 ciliegio 1blu notte 4 verde reale 1 caramello 1vinaccia 3 verde mirto 1 menta 1cremisi 3 azzurro marino 1 giallo vivo 1nocciola 3 verde fluorescente 1verde acido 3 verde pino 1

region. This is probably why the few participants who used lightness modifiers (chiaro and scuro - light and dark) applied them to the verde term.A discussion on the existence of a twelfth color class was initiated by Boynton and Olson (B&O) in [17] and reconsidered by Sturges


Non-basic color terms: The results of the three studies agree in this respect and highlight similar findings despite the different paradigms and languages. An interesting point is that non-basic terms with the highest recall frequencies are the same as those in the other two studies. They seem to refer to the regions where basic color terms are rarely used, namely those between white and brown/yellow (“cream, mustard, beige” for S&W, oro, beige, ocra in our experiment) and between white and purple/red (“lilac, violet, burgundy” for S&W, lilla, fucsia in our experiment).Overall, our results provide a clear indication towards the linguistic point of view, since the name azzurro features a higher recall frequency than many other basic terms, while confirming the saliency of the eleven basic color terms.

Impact of other factorsGender - Several studies have shown that women tend to produce and use more color names than men. We tried to investigate the existence of a correlation between the participants’ gender and the number of terms provided. Our participants were 11 males and 28 females. The average number of terms named by males was 22.63, against 20.42 for females. In order to better investigate the issue, the standard deviation of the parameter was measured, resulting in 9.11 for males and 9.47 for females. The distribution of the values in the two groups is quite similar. The results of our experiment show analogous numbers of terms provided regardless of gender. However, a gender-balanced sample would have been required for robust conclusions. Statistical significance?Age - Participants were aged between 15 and 62. A correlation between age of participants and number of terms provided was observed. The participants were organized into three age groups (<30; 30-45; >45). While the first and the last ones provided approximately the same number of color names, the other one (30-45) provided a significantly lower number of terms. You have to include the p value. Color experience - Participants were also divided into two groups according to their involvement in color matters. One group included people who have had someinvolvement in color (regardless of the involvement in nature), while the second group included people who have not. Participants who reported an involvement provided a significantly higher number of color names. Results are summarized in Table 2.Finally, the recall frequency for non-monolexemic names using lightness modifiers is given in Table 3, where chiaro stands for light, scuro for dark, and vivo for vivid.Overall, the experiment confirmed the special status of the 11 basic color terms, but also

and Whitfield (S&W) in [18]. Although our experimental procedure and method were different, their main findings were confirmed as detailed in the following sections. Interestingly, the same conclusion was supported by experimental evidence obtained in a purely linguistic framework.

Comparison with B&O and S&WBasic color terms: The psychological saliency of the eleven basic color terms was confirmed by the experiment. Recall frequencies drop from 84.6% (last basic term) to 43.5% (first non-basic term). The most important difference with respect to S&W was that in our experiment a twelfth potential basic color term had appeared (azzurro). This is most probably due to the cultural difference (Italian versus English). In [17,18] the authors foresaw the existence of a twelfth basic color for the English language based on the finding that “cream” (B&O) and “peach”. (S&W) obtained low response times and a fairly high consensus with respect to the other non-basic color terms. These two hypotheses were weakened by two factors: (i) strong evidence in the data was missing (response time and consensus), and (ii) the data led to a different indication for the missing name, which is supposed to be unique.On the contrary, the current experiment has provided a clear indication from the linguistic point of view, since the name azzurro has a higher recall frequency than other basic terms. On top of this, such a term, corresponding to the light blue region, is in line with recent findings regarding other languages like Greek, Turkish, Russian and Maltese [2-6]. It is noteworthy that Berlin and Kay proposed a hierarchy in color names that could in principle enclose such a term without losing generality.The different paradigm used in our experiment highlights another important difference concerning achromatic colors. B&O did not report on achromatic colors because of their poor representation in the OSA-UCS space. For S&W achromatic colors appeared in the last positions (in terms of consensus and response time) if compared to the other basic color terms.In our experiment, bianco (white) and nero (black) lie at the top of the basic color terms (in terms of frequency of recall), in agreement with the hierarchy proposed in by Berlin [1], while this was not the case for B&O and S&W. Even more interestingly, the six Hering primaries (red, green, blue, yellow, white, black) appear shortly after, followed by the other basic color terms (gray appears in the last position). We consider this as a strong indication that our results have a solid psychological correlate that might include perception and that seems to be stronger than for the other considered studies.


provided evidence of the need of a twelfth color name, in the Italian language, at least: azzurro.

3.2. EXPERIMENT 2: STROOP TESTResults support the hypothesis that two blue basic color terms do exist. The dark and light blue colors used in the experiment, having the same hue but different lightness, appear to belong to two different color categories. A Student’s t-test (p<0.05) showed that for every possible pairing (color ink and color word) the blu color word had significantly shorter reaction times when paired with dark blue ink than in all other conditions (Fig. 1, left plot). This means that the participants name more quickly the dark blue rather than the light blue ink color if both are paired with the blu color word.In the control experiment, the t-test for each possible combination of stimuli (ink color and color word) showed that the color word verde has significantly shorter reaction time when paired either to light or dark green colors than in any other condition (Fig. 1, right plot).

4. CONCLUSIONS

Our studies brought evidence in favor of the hypothesis of the existence of a twelfth basic color class in the Italian language in the light blue (perceived) color range named azzurro, as well as in its linguistic reference to perceived colors. Both the color listing and the Stroop

tests supported such a conjecture. No other factors (age, gender, color experience) resulted in having a significant impact on the outcomes of the two experiments. However, this issue deserves further investigation.

BIBLIOGRAPHY

[1] Berlin, B., Kay, P.: Basic color terms: their universality and evolution. Berkeley, CA: University of California Press (1969).

[2] Androulaki, A., Gômez-Pestaña, N., Mitsakis, C., Jover, J., Coventry, K., Davies, I.: Basic colour terms in Modern Greek: Twelve terms including two blues. Journal of Greek Linguistics 7(1), 3–47 (2006).

[3] Özgen, E., Davies, I.: Turkish color terms: Tests of Berlin and Kay’s theory of color universals and linguistic relativity. Linguistics 36(5), 919–956 (1998).

[4] Paramei, G.: Singing the Russian blues: an argument for culturally basic color terms. Cross-Cultural Research 39(1), 10 (2005).

[5] Winawer, J., Witthoft, N., Frank, M., Wu, L., Wade, A., Boroditsky, L.: Russian blues reveal the effect of language on color discrimination. PNAS 104(19), 7780–7785, (2007).

[6] Borg A. Towards a diachrony of Maltese basic colour terms. New Directions in Colour Studies. Biggam CP, Hough CA, Kay CJ, Simmons DR, editors. Amsterdam/Philadelphia: John Benjamins; (2011).

[7] Paggetti G., Bartoli G., Menegaz G., Re-locating colors in the OSA space, Attention, Perception and Psychophysics, vol. 73, n. 2, pp. 491-503 (2011).

Gender Age Color Experience

M F <30 30-45 >45 Si No

µ 22.63 20.42 21.23 17.07 25.33 26.63 15.75

σ 9.11 9.47 8.4 6.7 12.22 10.22 3.68

Lightness modifiers

verdechiaro

verdescuro

marronechiaro

marronescuro

grigiochiaro

brunoscuro

bluchiaro

bluscuro

giallochiaro

gialloscuro

rossovivo

giallovivo

5 2 2 2 1 1 1 1 1 1 1 5

Table 2 - Mean and standard deviation of frequencies over gender, age and color experience.

Table 3 - Number of recalls of compound names using a lightness modifier. Chiaro stands for light, scuro for dark, vivo for vivid.

Figure 1 - Average reaction time for the blu (on the left) and verde (on the right) term.


[8] Paggetti, G. Human Perceptual Factors in Imaging: A Link Between Cognitive And Computer Sciences, Doctoral dissertation, Doctoral course in Computer Science, University of Verona (2011).

[9] Paggetti, G., Menegaz, G.: Is light blue (azzurro) color name universal in the italian language? Color and Reflectance in Imaging and Computer Vision Workshop, International Conference on Computer Vision. Crete (Sept 2010).

[10] Paggetti G., Menegaz G., Exact location of consensus and consistency colors in the OSA-UCS for the italian language, Color Research and Application, vol. 8, n. 6, pp. 437-447 (2013).

[11] Paramei, G. Menegaz, G. ‘Italian blues’: A challenge to the universal inventory of basic colour terms, Colore e colorimetria, Santarcangelo di Romagna (RN), Maggioli Editore, Proceedings of “IX Conferenza del Colore”, Firenze, Sept. 19-20, pp. 164-167 (2013).

[12] Paggetti G.; Menegaz G., Locating colors in the Munsell space: an unconstrained color naming experiment, Perception, Proceedings of the European Conference on Visual Perception (ECVP), Alghero, Italy, Sept. 1-6 (2012).

[13] Paramei, G. Menegaz, G., Italian Blues: A challenge to the universal inventory of basic colour terms, Journal of the Internatioal Color Association , vol. 13 pp. 27-35 (2014).

[14] G. Paggetti, G. Menegaz, G. Paramei, Color naming in Italian language, Color Research and Application, pp. 1-14, DOI:10.1002/col.21953, (2015).

[15] Valdegamberi, V., Paggetti, G., Menegaz, G. On the perceptual/linguistic origin of the twelfth basic color term in the Italian color lexicon, Colour and Colorimetry Multidisciplinary Contributions eds. Maurizio Rossi, Maggioli, Proceedings of VII Conferenza Nazionale del Colore, Roma, pp. 291-298, Sept. 15-16 (2011).

[16] Stroop, J.: Studies of interference in serial verbal reactions. Journal of experimental psychology 18(6), 643–662 (1935)

[17] Boynton, R., Olson, C.: Locating basic colors in the osa space. Color Research and Applications pp. 224–235 (1987)

[18] Sturges, J., Whitfield, T.: Salient features of munsell colour space as a function of monolexemic naming and response latencies. Vision Res. 37(3), 307–313 (1997)

[19] Sandford, J. L. Blu, azzurro, celeste - What color is blue for Italian speakers compared to English speakers?,Proceedings of the Eighth National Color Conference. Gruppo del Colore, Alma Mater Studiorum Università di Bologna, Facoltà di Ingegneria, Bologna, Italy, 13-14 settembre 2012. Colour and Colorimetry. Multidisciplinary Contributions. Vol. VIII B, (ed.) Maurizio Rossi – Dip. Indaco – Politecnico di Milano, Santarcangelo di Romagna: Maggioli Editore. pp. 281–288 (2012)

[20] Menegaz G.; Paggetti G., Is the azul class unique in the Spanish language?, Perception, vol. 40, ECVP Abstract Supplement, 2011, pp. 80-80


1Walter Arrighetti, [email protected]

1CTO | Frame by Frame Italia

Motion Picture Colour Science and film ‘Look’: the maths behind ACES 1.0 and colour grading

1. INTRODUCTION

One of the biggest colour-related problems for the film production and post-production industry is two-fold: to ensure that the creative “look” of video content, as envisioned by the cinematographer, is preserved throughout ([1]–[2]), and to be able to consistently reproduce this ([1], [3]). That also has to be independent both on digital cameras or computers generating and animating it (as input), and on finished asset specifications for the end-users to watch and enjoy it (as output) — be it either in a dark digital cinema theatre, at a home TV setting, or using a Video-on-Demand (VoD) or Internet-streaming application, in a day-lit room or even open sunlight.In recent years many proprietary/commercial tools and workflows emerged, each driven by specific, not always cross-compatible needs (e.g. on-set grading, Digital Cinema mastering, VoD, etc.). This results in proliferation of a plethora of different formats vs. the scarce number of really interoperable standards.The author has put lots of efforts to provide a unified mathematical formalism and usability to most of the colour-management technologies used in the post-production world, both on independent publications and in collaboration with several entities in the business (like the SMPTE and the AMPAS). After a minimal introduction to such colour-mathematical terminology ([4]–[6]) and ColorLUTs, two brand new colour-management techniques from high-profile moving-picture digital imaging (CDLs and ACES) will be described, as they aim at colour interoperability for the analysis and synthesis of digital ‘looks’, both on-set (production) and along the Digital Intermediate (DI) phase.ACES in particular, which the author has been active contributor to since 2012, is an Academy-originated initiative for facilitating colour interoperability across the Media & Entertainment industry.

2. COLOUR SCIENCE MATHEMATICAL FORMALIST

A gamut mapping between colour spaces ([5]–[7]) is a vector field L(c), where c∈G is the input colour in the source gamut G⊆IRm (which is, to every practical aspects, a connected, linearly-

and superficially-connected m-dimensional domain—often even a convex one), with dim L(G )=n. Let the input and output spaces be both RGB model (m=n=3) and their canonical bases be the left-handed triple {r,g,b}, so any input colour c∈IR3 is coordinated as c=rr+gg+bb and, for the input regular RGB cube, (r,g,b)∈[0,1]3. The output colour is thus L(c)=Rr+Gg+Bb; by the Hodge-Helmholtz’ theorem, the orthogonal decomposition holds ([4]–[5]):

where T(c) and H(c) are the conservative (curl-free) and the solenoidal (divergence-free) parts of the colour map, each derived from a potential field — a scalar one χ(c) for the former, and a vector one η(c) for the latter. Due to simple connectedness of G, no harmonic component is present in the above: the constant ‘lift’ term l represents an overall colour bias (either neglected or incorporated into T) for chromatically-additive colour models like sRGB, as well as ciE XYZ and dci X'Y'Z'.The gradient of the gamut mapping can also be considered, which is a more complete (and complex) mathematical object, called a (n,m)-tensor field, [4], depending on both the source m channels and the target n channels. T is the generalized tonal mapping, or transfer characteristics which, in RGB spaces, models overall colour correction (incl. lightness and saturation changes). H is the field describing local colour-component cross-talks and global hue shifts. Notably, T field too may incorporate hue shifts, especially for those colours c, where the inter-channel ratios are not preserved, i.e. R(c) : G(c) : B(c) ≠ r : g : b.Colour-correction languages often use the luma-chroma colour model (e.g. the La*b*, Y'UV, Y'cBcR and Y'cX'cZ' spaces), or the cylindrical colour model (e.g. the HSL space). In the case of HSL for example, the author usually suggests joining hue h and saturation σ together into a complex parameter called chroma ς and defined as, [5]:

(where arctan2 is the secondary arc-tangent, which is reminiscent of quadrant allocation

ˆˆ ˆ( ) ( , , ) ( , , ) ( , , )( ) ( ) ( ) ( )

R r g b G r g b B r g bχ

= + += + = ∇ + ∇× +

L c r g bT c H c c c lη


“run” the algorithm, than applying more complex mathematical formulæ.Third, a cluT can hardly be interpreted only by specific software able to read its encoding and is useful only on specific picture(s) it was intended for: quite a black-box ingredient for the motion-picture recipes. The latter aspect may have been advantageous in the past, but is now mostly a downside, when cinematographers, colourists and VFX artists really need to transfer colour corrections from the on-set pre-grading sessions throughout the whole post-production pipeline, up to the theatre room, and capable of doing so in the most advantageous and, above all, interoperable way (cfr. §5). Moreover, lots of workflows with so different and “undisciplined” uses of cluTS exist —be it either for technical and creative intents— such that no generalized use can be made of a cluT as long it is tailored for a specific project. It is hard to invert (i.e. to “reverse-engineer”) the mathematical operations baked into a cluT, especially for post/VFX labs which do not enforce a thorough colour management across their pipelines. That is even worsened when materials from different sources (camera makes, film emulsions, CGI rendering, …) come all together in place.Discrete-calculus tools allow the extraction of quantities essential for the analysis or synthesis of a colour transformations: estimating colour differences, hue shifts in degrees, boundary wedges for evaluating Out-of-Gamut (OoG) colours, etc..When technical problems of higher level arise in colour correction (e.g. colour characterization of specific input or output devices, or proper gamut mappings between footage with different colorimetry), this usually translates into more sophisticated mathematical tools to be employed, often derived from Differential Geometry, Harmonic Analysis and multi-dimensional interpolations, [4]. For example, a more careful shaping of a tone-scale curve is usually necessary when modelling the transfer characteristic of a non-digital device, e.g. sensor noise or the sought-after 35mm film print emulation (FPE, cfr. Fig.2): three control points as provided by a CDL ([11]) or a 3-way color-corrector (CC) are no more enough and the three channel functions R(r), G(g) and B(b) need to stay non-decreasing (i.e. invertible). This helps better trim the effective contrast on all the tonal ranges. When the three functions are uneven with each other, a hue shift inevitably occurs, as the hue is not preserved by the same input triple (r,g,b) any more. Imposing hue-invariance means adding constrains that need to be correctly formulated), i.e.:

ˆˆ ˆ( ) ( ) B G R B G Rg b b r r g

∂ ∂ ∂ ∂ ∂ ∂ ∇× = ∇× = − + − + − = ∂ ∂ ∂ ∂ ∂ ∂ L c H c r g b 0

for b* and a* and commonly found in all the programming languages).

3. COLOUR LOOK-UP TABLES, AKA COLORLUT, AKA CLUT, AKA SIMPLY 'LUT'

While the above formalism is useful for technical operations like colour space conversions and “overall” colour corrections not done on a scene-per-scene basis, more complex transformations may be needed, especially when creative intent is included, [8]–[10]. In this case a closed-form formula for the transform might not exist; interpolation, though existing in principle, might not be computationally compatible with the creative need of real-time playback of high-resolution (often uncompressed) image/video files (essential for evaluating and creating the so-called grades). For this reason the continuum formalism may be well abandoned at this stage, and replaced by a ColorLUT (colour look-up table, or cluT, [6]) which is a discrete-mapping representation of it on a finite, m-dimensional grid of N points per input colour channel, with each point being a n-tuple in the output colour space (e.g. in the shape of a N×N×N RGB cube). It is an explicit mapping between a sample of input code-values into output code-values (which may or may not act between the same colour space), while the result on intermediate source colours is obtained by interpolation [3]: for this reason a cluT can be also used to approximate continuum formulae as those for mapping a colour space into another (e.g. from a RGB one like Rec.709, [10], to ciE XYZ, cfr. Fig. 2c). A type of cluT approximates the mapping channel by channel (therefore called 1d-luT, or colour curve in different contexts); another type acts as a full orthogonal sampling of input colours; the latter —because it usually maps between 3-channels colour spaces— is specifically called a 3d-luT whereas, mathematically, it is a discrete n-dimensional vector field L(s), where s∈G is the input colour codevalue of the source m-channel gamut G⊆IRm (cfr. several samples in Fig.1, where m=n=3).The reason why the cluT implementation is so widely used is manifold: first of all, provided the appropriate density of N input value per channel is used (17×17×17 samples in Fig. 2 is a common, but yet not enough coarse-grained choice), it can represent any non-linearities in the colour transform (accounting from the most complex primary colour corrections, up to a 35mm film’s dye cross-talk, as is the case for 3d-luTS). Secondly, it is implemented via simple (and usually linear) interpolations on the other non-sampled colours, fairly scaling with the cluT size N, and has therefore a smaller footprint in terms of CPU power and memory size needed to


Figure 1 - Plots of the output gamut L(Γ) of 3d luts L whose input is the 173-points RGB cube G: a. identity mapping; b. colour-space conversion between HDTV’s “Rec.709” and Cineon Printing Density (CPD) “logarithmic” RGB spaces; c. from “Rec.709” (gamma γ=2.6) to Digital Cinema (DCI) CIE XYZ colour space; d. from CIE XYZ to DCI’s P3 RGB colour space (γ=2.2 – notice the clipping at the cubic gamut boundary of P3); e. from Cineon Printing Density log. RGB to CIE XYZ colour space; f. scene-specific creative Colour Grading LMT including 35mm print-film emulation.

Figure 2 – Two different views of output gamut L(Γ) of a Print-Film Emulation (PFE) clut L engineered by the author (Technicolor laboratories, Rome, 2009), showing the synthesis work done adding additional points to the gamut of a Kodak Vision film in order to expand its latitute prior to 35mm scanning.


More often simpler definitions of hue and saturation are used though, like

which allows to have simpler analytical properties like

Imposing hue-invariance, means in this case solving the algebraic equation

hue L(c) = hue (c) ⇔

Another important constrain that is sometimes necessary, is the existence-of-inverse condition. This is especially important to guarantee that, once a grade is ‘burned’ within the raster pixels, original colours can still be recovered without degradations.It’s worthwhile noting that current post-production tools only ‘burn’ a colour grade as the last stage of the process (earlier the information on the grades is carried along the pipeline as metadata-only by the colour-correction software). This is formulated as in [2]:

21sat( )2

gb rb rg= − − −c c

23( )hue( ) arctan

2g b

r g b−

=− −

c

21sat( ) 2

sat( )2

r g bg b rb r g

− − ∇ = − − − −

cc

sat( ) 3 8hue( )

sat( ) sat( )∇

∇ = =c

cc c

2 2G B g b

R G B r g b− −

=− − − −

det 0( , , )

R R Rr g bG G G

r g b r g bB B Br g b

∂ ∂ ∂∂ ∂ ∂

∂ ∂ ∂ ∂= ≠

∂ ∂ ∂ ∂∂ ∂ ∂∂ ∂ ∂

L

4. ON-SET COLOUR GRADING AND ITS COLOUR LANGUAGE (CDL)

Since things are now shot digitally and stay digital throughout the pipeline, one of the movie-chain blocks to recently take advantage of this is principal photography, where early colour correction/grading can be done effectively, on-set, just minutes after each clip is shot, cfr. [11], Fig.3.Creative colour correction (grading) information can be transported, from clip to clip, as they are originally shot, as a series of simple non-linear transformations controlled by 10 parameters (3 parameters by each of the 3 RGB channels, plus 1), each representing one degree of freedom of the creative colourist: ‘slope’, ‘offset’, ‘power’ triples, plus a ‘saturation’ parameter. A set of such quantities, transported for a whole video asset, cut per cut, makes up the 2014 OSCARS®-winning American Society of Cinematographers’ Colour Decision List (ASC CDL) and is a well-known example of simple mathematical equations at the creative service of motion picture colourists [5], [11]. This can also be re-written by means of three functions S (slope), O (offset) and P (power) and let s, o, p be the respective controlling parameters with identity values 1, 0, 1 respectively:

• Slope S(c;s)=sc (CDL analogue for colourists’ lift, despite slope fixes black point at codevalue 0.0, whereas lift fixes whitepoint at codevalue 1.0);• Offset O(c;o)=c+o (CDL analogue for colourists’ gain);• Power P(c;p)=max(0,s)p, which is the CDL analogue of a gamma-correction.

Figure 3 – Example of commercial colour grading software GUI with the main 3-way colour-corrector wheels.

( )( ) ( )( )( )( )

GR B

R R G G B B

R R R G G G

B B B

ˆˆ ˆ( ) ( ) ( ) ( )ˆ ˆ( ; ); ; ( ; ); ;ˆ( ; ); ;

pp po s r o s g o s b

P O S r s o p P O S g s o p

P O S b s o p

= + + + + +

= +

+

L c r g b

r g

b


This is a non-orthogonal decomposition in 1st- and 2nd-degree polynomials (slope + offset), plus a nonlinear function (power), thus the inner products may not “behave” well. It can be shown however (and this is in fact well-known practice done by every non-mathematician colourists working on still or moving pictures), that a sufficiently low number of such operators, governed by a few parameters (like weighting coefficients in a linear combination for Linear Algebra) allow for quite good approximation, thus leading to orthogonal decompositions of colour operators.

where all the 1-parameter vector fields lk are known a priori, whereas the coefficients and the parameters k themselves are the real descriptors of the “look”.

5. THE ACADEMY COLOR CODING SYSTEM (ACES)

The Academy of Motion Picture Arts and Sciences (AMPAS)’s Science and Technology Council has been gathering a group of variegate experts from all the top-level production, post-production facilities and software houses in the industry to put forward a solution unifying such colour management issues: ACES, [1], [12].The reason behind ACES is the need to particularly address the plethora of colorimetries set by manufacturers’ digital equipment (both image-creating and -reproducing) — even many more so in the digital era than film processes ever had in the past. A similar need has already surfaced in the Digital Cinema industry: that is why its dci X'Y'Z' colorimetry derives from the device-independent ciE XYZ colour space ([3], [10]). Unfortunately, as neither colorimetric cameras nor monitors/projectors exist as of yet, this colour-space choice has lead to reverting to a one within the RGB model, which is more practical, as well as ACES mostly pertains to TV

Figure 4 - Sketch of the ACES paradigm: the original scene is either captured by a real camera or generated in CGI. Whatever the source, the corresponding Input Transform converts the code-values into the SMPTE2065 colour space (except for the “ideal” RICD, which already produces SMPTE2065 pictures). Using the Output Transform the pictures can then be transferred to any output device, like monitors (with any technologies), projectors, TVs, etc.

and moving pictures. Every colour-correction operators in the involved pipelines (from camera controls, to colour-grading suites, to projectors’ and TVs’ balance controls) are, in fact, RGB-based.Version 1.0 of ACES, [12], whose project the author has been cooperating on with the AMPAS experts since 2012, is a framework with centralized colour-management paradigm, developed after many years of pre-testing among facilities and companies in the industry, where the image is evaluated according to its colorimetric digital representation. Please refer to Fig.4 for a schematic throughout this Chapter. First of all, ACES defines AP0 and AP1: two sets of RGB primaries for the four ACES colour spaces. AP0, whose ciE xy chromaticities are (0.73470,0.26530) for Red, (0.,1.) for Green and (0.0001,0.0770) for Blue. AP1 primaries’ chromaticities are (0.713,0.293) for Red, (0.165,0.830) for Green and (0.0128,0.044) for Blue. Both use ciE D60 illuminant (0.32168,0.33767) as white-point and physical blackpoint at ciE XYZ triple 03.Within ACES colour pipeline the image is considered as virtually captured by a Reference Input Capture Device (RICD), which is an idealized digital ‘camera’ recording in a RGB colour space called SMPTE2065 after the standard that defines it. Another important aspect is that SMPTE2065 is a scene-referred colour space, i.e. the code-values represent mean relative exposures to the one captured from a perfect reflecting diffuser — apart from a 15% glare. In AP0, this accounts for a normally-exposed 18% grey card acquired by a RICD mapped to the RGB triple (.18, .18, .18).Any real camera imagery and colorimetry is brought into the pipeline by means of a colour gamut mapping called ACES Input Transform, which basically converts all the camera’s colorimetry into SMPTE2065. Currently, Input Transforms for most of the patented, cinema-grade cameras like the ARRI AlExA, the cameras

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Figure 5 - Real-world ACES testing to compare ARRI Alexa XT (shown here), RED™ EPIC and Sony F55 cameras on the same technical set and ACES colour space (also courtesy of DIT E. Zarlenga).

Figure 6 - Main ACES v1.0 components in place: footage shot in Alexa camera’s native ARRIRAW frame-per-file format (in Log.C colour space) is technically processed by an Input Transform to become scene-referred SMPTE2065 colour space. This is where color grading is applied upon (in temporary ACEScc space). The CDL designed during principal photography (on-set) pre-grading is conceptually applied above this grade, but below optional creative-technical transforms like PFE. The file is then ready to be saved in ACES-compliatn OpenEXR sequence (SMPTE2065 colour space) or can be sent to a display device passing through an Output Transform.

by RED™, the Fx5 family by Sony, the cameras by Blackmagic Design, and the Cinema-EOS™ family by Canon, are provided; each maps the sensor’s proprietary gamut (called ARRI Log.C, RED.Log, S-Log/S-Gamut, BMD.Log and CanonLog respectively), parametrized by shooting settings like equivalent sensitivity (ISO) or correlated colour temperature (CCT), into scene-referred SMPTE2065 codevalues.The author has also been active in Italy for promoting the use of ACES with several initiatives, [1], including a real-world, on-set test to compare ACES freamework originating from different, high profile cameras, up to a full VFX and Digital Cinema mastering pipeline: Fig.5 is the result of the technical photography session.At the other end of the pipeline, SMPTE2065 colorimetry is converted to the gamut of the displaying device and chromatic adaption by means of an ACES Output Transform: among the others there is one, for example, for Digital Cinema mastering (dci P3) in a dark surround, two for standard broadcast TV gamut (Rec.709), and two for UHDTV (Rec.2020) — each having one for a bright- and one for a dark-surround adaption. From a Colour Appearance Model (CAM)’s perspective, the Output Transforms take care of the viewing environment as well: so several Output Transforms may exist for the same device, but under different chromatic adaptions. All the Output Transforms have a common first mathematical block, called the Reference Rendering Transform (RRT). Please refer to Fig.5 for a block-diagram of ACES version 1.0 main components.All in all, SMPTE2065 space uses AP0 primaries, has trivial transfer characteristics (i.e. it is

photometrically linear, i.e. “gamma-1.0”) and represents the baseline for all the ACES pipeline — and the widest gamut as well, which is also suited for long-term archiving, cfr. Fig.6. Codevalues are usually encoded as 16 bits/channel floating-points (‘half-floats’ as per IEEE 754-2008 standard), and archived in a specific frame-per-file variant of the OpenEXR file format, cfr. [14]–[15].It is within this space that images are mainly worked on, with exceptions when it is technically convenient or mandatory to use temporary, well-defined colour-spaces for specific purposes:

• ACEScc has AP1-primaries, “logarithmic” transfer characteristic, 32 bits/channel float encoding optimized for film-style colour correction, [13];• ACEScg has AP1-primaries, photometrically-linear, 16 or 32 bits/channel integer code-values, optimized for CG and painting applications that scarcely support images represented by floating-point codevalues, [16];• ACESproxy has AP1-primaries, the same logarithmic characteristic as ACEScc, 10 or 12 bits/channel integer encoding, optimized for real-time transport of images over physical links (e.g. the SDI cables family) that only support integer code-values, yet logarithmic encoding is still needed for on-set color correction applications, [17];

Similarly, output-device compatibility is provided by first mapping to another ideal


output Reference Display Device (RDD) via the aforementioned RRT, and, whence, by means of a discrete mathematical formula called Output Device Transform (ODT), which depends on the output colour space and, ultimately, on the output device’s transfer characteristics (e.g. monitors, projectors, printers, D-Cinema devices, etc.).ACES images are stored in frame-per-file ordered sequences, encoding each frame as a OpenEXR file [14], together with ACES-specific metadata optionally written as well in a “sidecar” XML file called ACES clip-container.Ideally, any sensitive colour operation (both for technical and creative intent) should take place in either the SMPTE2065 or the ACEScc colour spaces (which act like a PCS in the ICC paradigm), where any operator acts unambiguously. Creative-intent operations, in particular, are stored in the so-called Look Modification Transform(s) (LMT), which are applied before the Output Transform.

6. CONCLUSIONS: THE COLOUR 'LOOK' OF A FILM

Several professionals in the video, post-production and DI world, as well as colour scientists and vendors, have long tried to define what technically “look” means in this context. In the author’s opinion, science, experience and common practices can sum up together to the statement that currently a “look” is the ensemble of creative colour decisions made for a specific set of scenes (e.g. scenes shot to represent the same lighting and dramatic situation, if not even

possessing location/temporal unity) that neither pertain the technical properties of the colours themselves nor the devices/media used to reproduce them. In this sense “look” is different from “film look”, as the latter also includes colour characteristics due to combination of a film’s emulsion, development and printing processes (which can of course be emulated).A look applied to two differently-exposed and -coloured scenes not to chromatically match them (that’s what the same-name phase of a colour correction session is about), but rather to give both the same visual impact, in the director’s and/or cinematographer’s minds, the incisive ‘colour fingerprint’ unique to that specific product and cinematography; the ‘look development’ phase has therefore been starting earlier and earlier in the production phase, up to taking place on-set, with the help of proper pre-grading workflow (e.g. the one proposed by Technicolor DI supervising colourist Peter Doyle for Harry Potter VII, Dark Shadows and Paddington full-feature films).The lack of interoperable schemes, incompatible file formats and colour operations —even a unified terminology— has prevented many colour manipulations from currently happening, or at least made this much more difficult and prone to errors or lack of precision. This cannot be any more delayed since all film processes have turned completely digital, and this is where the author’s contributions have been focusing on in the latest years. Unifying the post-production terminology and mathematical

Figure 7 - Chromaticity comparison between ACES (SMPTE2065) gamut other well-known RGB colour spaces.


formalism (which were traditionally tied up to different post-production laboratories’ own film processing technologies and manufacturers’ secret sauces) means creating a common baseline to start from and communicate with mainstream Color Science. ACES is another step up in the attempt to create common processes and workflow to ease interoperability and help to future-proff archival footage. Of course all of this is a process, therefore it is not meant to be set in stone but rather continually progress as new technologies, methodologies and, above all, creative eyes and minds turn up bringing along their expression techniques and visions — for this process to drive them all along together.

BIBLIOGRAPHY

[1] W. Arrighetti, “The Academy Color Encoding System (ACES) in a video production and post-production colour pipeline”, Colour and Colorimetry, XI(B), Maggioli, 2015.

[2] W. Arrighetti, “New trends in Digital Cinema: from on-set colour grading to ACES”, at CHROMA: workshop on colour image between motion picture and media, chroma.di.unimi.it, Sept. 2013

[3] W. Arrighetti, “Colour Management in motion picture and television industries”, Colour and Colorimetry, VII(B), 63–70, Maggioli, 2011.

[4] W. Arrighetti, Mathematical models and methods for Electromagnetism in Fractal Geometries, Ph.D dissertation, Sapienza University of Rome, Rome, 2007.

[5] W. Arrighetti, “Colour correction calculus (CCC): engineering the maths behind colour grading”, Colour and Colorimetry, IX(B), 13–19, Maggioli, 2013.

[6] W. Arrighetti, “Moving Picture Colour Science: the maths behind Colour LUTs, ACES and film ‘Looks’”, Color and Colorimetry, VIII(B), 27–34, Maggioli, 2012.

[7] S. Westland, C. Ripamonti, Computational Colour Science using MATLAB. Wiley, 2012.

[8] Colorimetry, Second Edition, Commission Internationale de L’Éclairage, Publication 15.2, 1986.

[9] M. Petrou, C. Petrou, Image Processing: the fundamentals, Wiley, 2012.

[10] C. Poynton, Digital Video and HD: algorithms and interfaces, Morgan-Kaufman, 2012.

[11] A.B. Benitez, L. Blondé, B. Lee, J. Stauder, H. Gu., “ASC CDL: a step towards Look Management”, Proceedings of IBC 2007.

[12] Academy Color Encoding Specification (ACES), Society of Motion Picture and Television Engineers (SMPTE), Standard 2065-1, 2012.

[13] ACEScc, a Logarithmic encoding of ACES data for use with Color Grading systems, Specification S-2014-003, Academy of Motion Picture Arts and Sciences (AMPAS), v1.0, Dec. 2014.

[14] OpenEXR File Layout, OpenEXR working group, www.openexr.com, Apr. 2007.

[15] ACES Image Container file layout, SMPTE, Standard 2065-4, 2013.

[16] ACEScg, a working space for CGI render and compositing, S-2014-004, AMPAS, v1.0, Dec. 2014.

[17] ACESproxy, an Integer Log encoding of ACES image data, S-2013-001, AMPAS, v2.0, Dec. 2014.

[18] F. Pierotti, “The colour turn: l’impatto digitale sul colore cinematografico”, Bianco e nero, LXXV(580), 26–34, Carocci, Rome, Sept.–Dec. 2014.


1. INTRODUCTION

The objective of this paper is to ascertain contemporary Italian linguistic categorization of the macro-color concept BLUE, and compare the results to English interpretation of the same tasks. Native Italian speakers affirm that they habitually use three blue color terms: blu, azzurro, and celeste; often idealizing azzurro over blu, as being “more Italian”. I propose that according to the task results contemporary blu [blue] is the more primary and deeply entrenched basic color term (henceforth BCT); azzurro [azure - light blue] could also be a BCT, but should be considered a secondary BCT; and celeste [sky blue - pale blue] is a subordinate color term. English interpretation of the same color object/concept associations used in this questionnaire is different due to the lack of a second English monolexemic basic BLUE color term and to the difference in culturally specific BLUE color term collocations. What are the Italian blue term semantic relations? Do azzurro and celeste violate the criterion that a BCT not be a hyponym of another color word, i.e., blu [1]? Does the semantic relation between the terms blu and azzurro respond to the cognitive need to differentiate between the colors of the sky and the water? Is the principal task color term object/concept association, based on the cognitive linguistic approach to linguistic entrenchment, an original valid method to measure basicness? Various verification measures of basicness are employed to answer these queries.

2. BACKGROUND

The spectrum may be partitioned into different color terms according to language and the corresponding culture. Color lexemes evolve dividing the color space into more specific semantic concepts. Current research, since Berlin and Kay, generally considers BLUE to be the last primary BCT to emerge in language. Some languages present a variation of BLUE lexicon; often displaying two distinct blue terms; one for a generic BLUE -primary BCT- and one for a more specific BLUE -secondary BCT- in English recognized as a tonal variation of “blue”. The possibility of there being a twelfth BCT has been theorized by [1][2], among others. Researchers have proposed a twelfth BCT as being another “tone of blue” — also referred to as dual

1Jodi L. Sandford [email protected]

1Department of Letters, University of Perugia

A Cognitive Linguistic Usage Perspective: What is Italian Blu, azzurro, celeste? Do English agree on BLUE semantics?

lexicalization of BLUE — in different languages e.g., Italian, Maltese, Greek, Polish, Russian, and Turkish, [3][4][5][6][7][8]. BCT criteria are listed as: i. it is monolexemic, ii. its signification is not be included in that of any other BCT, iii. it must not be restricted to a narrow class of objects, iv. it must be psychologically salient, i.e., occur at the beginning of elicited lists of color terms, and have stability of reference across informants and occasion of use, v. if doubtful it should have the same distributional potential [1]. The specific BCT criteria of interest this study are: iii, it not be restricted to a narrow class of objects, (the occurrence in a large number of domains reveals the degree of entrenchment), and the problematic ii. its meaning must not be hyponymic (see Glossary for a definition of several terms).The definition of each color term provides an initial idea of what they are understood to mean, and in what contexts the meaning can vary. In some dictionaries [9] [10] blu is defined as a dark azzurro, and is used with the expressions: cielo blu [blue sky], mare blu [blue sea] | avere sangue blu [have blue blood]; though blu is translated as blue, dark blue, and navy blue. Blu is more productive, BLUE compounds in Italian are currently constructed with blu + another term, e.g.; blu marina [navy blue], which of course do not appear separately in dictionary entriesi. There are only three entries of blu lexemes in the Italian dictionary [11]. Azzurro is defined as the color of a clear sky, somewhere in between celeste and turchino; translated with blue, light blue, azure, sky blue; with expressions such as occhi azzurri [blue eyes] | principe azzurro [the ideal groom, a prince in shining armor] | gli azzurri the Italian national sport team color, | azzurro del cielo [the blue of the sky]. Azzurro is sited as synonymous with blu [blue], celeste [sky blue], turchese and turchino [turquoise], and pervinca [periwinkle]. It entered Italian before blu and is listed with over fourteen entries in the dictionary, including a verb form: azzurrare [9][10]. Celeste, translated as sky blue, light blue, baby blue, azure, is defined as analogous to azzurro, specifications of which are celestino [pale blue], and acquamarina [7][8]. There are only two dictionary entries with the color root celest- [11]. Turchino, with direct reference to the stone turquoise turchese, is described as of azzurro cupo [deep or dark], and blu [12].


The principal test of this study was developed to apply a cognitive linguistic approach to verify the level of entrenchment of the color term object/concept association. The multiple senses of color terms create a network that is accessed and elaborated online for the speaker to identify the meaning of the color term in use. The identification of color term entrenchment and distributional criterion of occurrence in a large number of domains should give us a sense and level of basicness and lexical status. This approach was developed following Langacker [13] and other cognitive linguists who sustain a functional approach to linguistic investigation. They acknowledge the grounding of language in embodied experience and social interaction, insisting that this interaction is critically dependent on conceptualization. Conceptualization is once again constrained by four aspects: human cognitive capacities, the nature of reality, convention, and context [14]. Therefore, meanings experienced more often, will be encountered more frequently in specific contexts or associations, and will in turn become more entrenched on an individual level and conventionalized in the speech community.

3. METHODOLOGY

This study was carried out in five different phases. Each phase had a specific objective and served to initially confirm or contradict the various results. The first, and most pertinent, phase was the BLUE color association test; constructed to verify color term entrenchment and occurrence in a number of domains. The second phase was the same BLUE color association test translated and presented to a pilot group of native American English speakers, to verify the cross linguistic saliency of the color object/concept associations. The third phase comprised a color-list task to confirm the three BLUE color terms’ cognitive saliency in Italian. The fourth phase was a color-patch naming task in Italian; and the fifth phase was a “kind of” survey to verify informant signification and stability of reference in Italian. The Italian informants were students at

the University of Perugia. They were from mixed regional backgrounds across Italy. The American English informants were a variegated group of native speakers.The first and second phase tasks asked informants to associate 10 BLUE color terms to 38 different Italian prototypical object/concepts. This task was carried out by two groups of Italian university students (49 and 48) for a total of 97 informants (mean age 23). A comparison group of 15 native American English speakers carried out the same task translated into English [9-10] (mean age 45). Azzurro has numerous possibilities, medium blue, sea blue, bright blue, heraldry blue, sapphire; I opted toward the phonetically more similar term azure. A paper questionnaire was handed out to the class of students. One page contained the instructions and one page the list of items with a blank box next to it, where the informant wrote one of the 10 color terms provided. There was no time limit. Informants took no more than 15 minutes.The prototypical object/concepts used as stimuli in this task were selected from online dictionaries, databases, and idiomatic expressions, which were double checked through Google and confirmed as being the most frequent. The 10 colors selected to associate with the stimuli were the three most frequent terms blu, azzurro, and celeste, with the four possible inflected color terms: blu-astro, azzurr-ognolo, azzurr-ino, and celest-ino (see endnote), with three other salient BLUE color terms, turchino, oltremarino, and indaco. All of these terms except azzurrognolo appeared later in the listing task, see Table 1.The third phase, the color-list task, was carried out by 65 university students (mean age 22). It aimed to verify the cognitive salience of the three BLUE terms. This task was based on Davies and Corbett [15]; informants were asked to write down as many color terms as they could in five minutes. The data were then analyzed following the cognitive salience index elaborated by Sutrop [16], taking into account two important aspects of BCT criteria: term frequency and mean position. The cognitive salience index is calculated: S = F/(N x mp), where S is salience,

Table 1 - The 10 BLUE names in Italian and English, color patches set to RGB coordinates indicated by Moroney in the Italian section of The Color Thesaurus, Hewlett Packard Laboratories.

English translation of Italian BLUE Color RGB color coordinates

blue blu red=20, green=15, blue=180 bluish, grey-blue bluastro red=50, green=65, blue=120 azure azzurro red=60, green=155, blue=240 light blue azzurrino red=115, green=175, blue=245light bluish azzurrognolo red=120, green=145, blue=205sky blue celeste red=129, green=225, blue=255pale blue celestino red=180, green=235, blue=255ultramarine oltremarino red=40, green=25, blue=195 turquoise turchino red=10, green=185, blue=205indigo indaco red=20, green=15, blue=120


F is frequency in the lists, N is number of informants, and mp is mean position rank in the lists provided by the informants.The fourth phase, the BLUE color-patch naming task, was carried out in a darkened room with a projection of numbered color patches presented on a screen. The stimuli were presented one at a time. They remained on the screen for 10 seconds. 30 informants (mean age 26) wrote the color names in the box next to the slide number. The 30 color patches were set to color RGB parameters distinguished by Moroney for the Italian color terms [17]. The randomized 10 BLUE color patches corresponded to the 10 color words tested in the first phase, which had also emerged in the third phase (see Table 1). The informants were tested twice on different occasions to verify the consistency of naming.The fifth phase of this study, the “Kind of BLUE” was carried out by a group of 30 informants (mean age 26). They were asked which BLUE was a kind of BLUE, combining the three terms, blu, azzurro and celeste, in couples, e.g., Is azzurro a kind of blu? The informants answered yes or no.

4. RESULTS

“Color association” task result data are presented in percentages in Figures 1 and 2. The total 3686 responses given by 97 Italian informants resulted in 30% of the objects associated with blu (blue), 7% bluastro (blue-grey), 19% azzurro (azure), 4% azzurrognolo (dull bluish), 4% azzurrino (light bluish), 11% celeste (sky blue), 5% celestino (pale blue), 5% oltremarino (ultramarine), 7% turchino (turquoise), 6% indaco (indigo), and 3% no answer. If we group the terms according to tone (dark, medium, light) the division becomes 48% blu, 34% azzurro, and 16% celeste. The spread between the three predominant BLUE color terms does not vary significantly, and the rank remains unchanged. Fig. 2 shows marked differences in English association percentages with the corresponding terms blu, bluastro, azzurro, celeste, and no answer. The color/object associations in English actually seem more evenly distributed, demonstrating the subordinate value of most of the BLUE color

terms, and the saliency of blue with a higher percentage of associations. A predominance of blu, azzurro, and celeste, in that order emerges in these results.Figure 3 presents the total Italian results, each color term and the 38 associated items. Each item is presented in Table 2 with the color term with the highest percentage of associated object/concepts agreement.The object/concepts associations with the highest percentage of agreement are: 85% sangue blu, 84% fata turchina, 82% principe azzurro, 78% caschi blu, 70% jeans blu, 64% Madonna celeste, 63% bollino blu, 63% tute blu, 59% fifa blu, 57% telefono azzurro, 55% cielo azzurro, 55% fiocco azzurro, 54% strisce blu, 52% camicia celeste, 51% machine blu, 50% mare blu, 51% pesce azzurro. Of the 38 object/concepts, the number that were mostly associated with a specific color are 19 with blu; 10 with azzurro; 2 with celeste; 2 with celestino; 2 with turchino; 1 with azzurrino, 1 with bluastro, and 1 with oltremarino. The most prototypical object for blu is sangue (85%); for bluastro is fumo (38%); for azzurro is principe (82%), for azzurrognolo is fumo (21%); for azzurrino is airone (23%); for celeste is Madonna (64%); for celestino is nuvole (23%); for oltremarino is sale (39%); for turchino is fata (84%) and for indaco is tute (12%). The English translation of the object/concepts may be found in Table 2.A significant result was also the consensus between the two Italian groups, even though within the group there were significant differences in color/object association. 59% (169 of 380 responses) of possible color-object/concept associations were made by the same number or ±1 of informants in each group separately, e.g., caschi and blu were associated by 38 people in the first group and 38 in the second group; cielo azzurro was associated by 26 and 27. And only 4% (17 of 380) of the associations made by the two groups were different by >5 of the number of informants. That is to say that the correlation coefficient was statistically relevant between the two Italian groups for blu, azzurro, azzurrognolo, celeste, celestino, turchino, and indaco (all between 0.956 and 0.720).

Figure 1 (left) - Percentage of Italian BLUE color association of 38 objects by 97 informants.

Figure 2 (right) - Percentage of English BLUE color association of 38 objects by 15 informants.


Figure 3 - Italian BLUE color term 38 object/concept associations by 97 informants (English translation of the items is indicated in Tab.2).

Table 2 also shows the percentage of association results for English. The results are significantly different. The highest agreement was 80% for blue flag, 73% for sky-blue sky, blue team, grey-blue heron, and grey-blue smoke. Only 29% (11 of 38) color object/concept associations were the same in both languages, and the percentages varied notably; the number of “no answers” is relevant in color association saliency. Table 2 lists the change of the color term associated next to the item and the percentage of agreement. The difficulty in responding by the English informants clearly indicates the lack of cultural entrenchment of the Italian prototypes.

“Color-listing” task results reveal two cognitively important aspects: the term frequency and position in the list. Following Sutrop [16] color term basicness is estimated independent of the length of any particular list; see results in Table 2. The color term is given followed by frequency

in the lists of the 65 informants, and the corresponding rank; the mean position in each list and the corresponding rank within the total list of 134 different colors listed; the cognitive salience index and the final cognitive salience rank. The cognitive salience ranking of blu in fourth position, azzurro in ninth, and celeste in thirteenth, corresponds in essence to the results acquired through the cognitive linguistic association task.

“Color-patch naming”results show that the majority of the informants called blu 6 of 10 BLUE color patches (not azzurrino, celeste – celestino, nor turchino), see Table 4. The patch considered blu was named blu by 94%, azzurro was named blu by 74%, azzurrino was named azzurro by 60%, celeste was named celeste by 80%. The half tone azzurrognolo was named blu by 80%, bluastro was named blu by 65%, oltremarino and indaco were named blu by 67%. Color


Rank Ital. Ass. 38 obj/conc.

Object/concept in Italian

Color max. agreement

% Agreement Italian

% Agreement En-glish

Object/concept in English

1 sangue Blu 85% 47% blue blood2 caschi Blu 78% 27% blue helmets3 jeans Blu 70% 47% indigo jeans4 bollino Blu 63% 47% blue sticker5 tute Blu 63% 53% blue collar6 fifa Blu 59% 33% no answer fear7 strisce Blu 54% 40% blue lines8 macchine Blu 51% 40% blue cars9 mare Blu 50% 33% azure sea

10 bandiera Blu 47% 80% blue flag11 tasto Blu 39% 27% no answer button12 freccia Blu 37% 33% no answer arrow13 cartello Blu 36% 53% blue sign14 energia Blu 33% 27% ultramarine energy15 fascia Blu 31% 60% no answer band16 occhi Blu 30% 40% pale blue eyes17 treno Blu 28% 47% no answer train18 mani Blu 27% 27% blue hands19 pellicola Blu 26% 33% no answer film1 principe Azzurro 82% 33% blue prince2 telefono Azzurro 57% 27% blue phone3 cielo Azzurro 55% 73% sky blue sky4 fiocco Azzurro 55% 33% blue bow5 pesce Azzurro 51% 40% ultramarine fish6 squadra Azzurra 47% 73% blue team7 nastro Azzurro 42% 20% sky blue ribbon8 pomeriggio Azzurro 36% 27% sky blue afternoon9 pizzeria Azzurro 23% 47% no answer pizzeria10 porto Azzurro 23% 27% ultramarine port1 Madonna Celeste 64% 27% no answer Madonna2 camicia Celeste 52% 40% blue shirt1 nuvole Celestino 23% 33% pale blue clouds2 gabbiano Celestino 18% 60% grey-blue seagull1 fata Turchina 84% 20% light blue fairy2 capelli Turchini 47% 20% blue hair1 airone Azzurrino 23% 73% grey-blue heron1 fumo Bluastro 38% 73% grey-blue smoke1 sale Oltremarino 39% 27% pale blue salt

Table 2 - The maximum percentage of informant agreement on the color associated with each of the 38 object/concepts; grey background for same color-object/concept association in both language groups; bold print for majority agreement.

Color Name FrequencyFrequency

RankMean position (mp) Mp Rank

Cognitive Salience Index

Cognitive Salience Rank

rosso 63 3 3.71 11 0.2612 1

giallo 62 4 4.70 13 0.2029 2

verde 61 5.5 5.42 14 0.1731 3

blu 60 7.5 6.03 18 0.1531 4

nero 64 1.5 7.89 22 0.1248 5

bianco 64 1.5 8.28 24 0.1189 6

arancione 58 9.5 8.48 25 0.1052 7

viola 61 5.5 9.61 30 0.0977 8

azzurro 54 11.5 9.59 29 0.0866 9

rosa 54 11.5 9.66 31 0.0860 10

marrone 60 7.5 11.16 37 0.0827 11

grigio 58 9.5 11.34 38 0.0787 12

celeste 44 13 10.63 34 0.0637 13

lillà 34 15 13.38 50 0.0391 14

fucsia 37 14 15.10 63 0.0377 15

indaco 29 18 12.96 48 0.0344 16

beige 30 16.5 15.20 64 0.0304 17

oro 30 16.5 15.93 71 0.0290 18

porpora 20 21.5 13.70 51 0.0225 19

rosso bordeaux 20 21.5 14.20 55 0.0217 20

Table 3 - First 20 colors in color listing - cognitive salience rank of 65 informants; grey background for BLUE terms


referents for the color patches used in this task also correspond to the legend in Figures 1 and 2.“Kind of BLUE” task responses show 93% of Italian informants responded affirmatively to azzurro as a kind of blu, and 100% affirmed that celeste is a type of blu, but blu is not a type of azzurro, nor celeste. Only 10% of informants claimed that celeste was a type of azzurro.

5. CONCLUSIONS

As expected BCT’s can be distinguished from non-BCTs by the high scores on the first phase task entrenchment and occurrence and the third phase task cognitive saliency, and the fourth and fifth phase task significations and stability of reference results. The use of a specific set of the 10 most common BLUE terms in Italian and the lack of restrictions between BCT and non-BCT provided a mode of testing the relationship between the three most common BLUE terms: blu, azzurro, and celeste. The subordinate terms were associated less often than the BCT term(s). Moreover, the use of all the terms determined a decrease at the expense of the more specific terms. This provided a further check on robustness and stability of the BCT terms. In the past azzurro has been used as the prototypical basic blue, Grossman [18] identifies it as the BLUE arcilessema and translates blue with it. Diagrams and color systems published in the past translated labels and indications of blue with azzurro; contemporary texts translate blue with blu, e.g., there is no change in the use of the abbreviation RGB (Red-Rosso, Green-Verde, Blue-Blu), and the respective diagrams of color spaces; identification of receptor cells R-G (Rosso-Verde), B-Y (Blu-Giallo), and primary colors (Red-Rosso, Blue-Blu, Yello-Giallo). Moreover, many recent videos and books for children teach blu as the basic color not azzurro. Quantification of a corpus analysis in this sense may be of interest for future investigation. This study lends support to the claim that there has been a semantic shift in BLUE lexicalization. I argue that azzurro was previously the Italian BLUE primary BCT. It has been present in Italian for longer and has diachronically developed a more elaborate grammaticalization (see

blu named blu by 94%

azzurro named blu by 74%

azzurrino named azzurro by 60%

celeste named celeste by 80%

azzurrognolo named blu by 80%

bluastro named blu by 65%

oltremarino named blu by 67%

indaco named blu by 67%

turchese named verde-acqua by 55%

celestino named celeste-chiaro by 50%

Table 4 - Fourth phase color patch naming in Italian.

endnote). It no longer demonstrates the same saliency, however, as it did in the near past. Summarizing, the study informants identified azzurro as a type of blu, and but not celeste as a type of azzurro. Though they may be considered synonyms, there is a different degree of inclusion in contemporary Italian; the hypernym is now shifted to blu and the hyponyms are azzurro and celeste. In this sense, according to the “non hyponym” BCT criteria azzurro would not be a secondary BCT. Considering, the first phase task, however, a more dynamic analysis of color term entrenchment, and an apparently valid measure of basicness and “application to a broad range of objects”, azzurro still has robust associations and collocations in both conceptual metaphoric and metonymic extensions. For example, the informants associated the sea with blu, the sky with azzurro, blood with blu, eyes with both blu and azzurro, and the prince with azzurro. The results of the four phases of this study converge to suggest that contemporary Italian use of BLUE color terms reveals a twelfth BCT, a tone of BLUE. Azzurro has not yet been pushed out of basicness, but may be on its way. It still has a high cognitive salience rank. Although it is evident that the number and age of the Italian and English informants differ notably in this study, the general tendency of lack of entrenchment of these color associations for the English pilot group is convincing. Furthermore, all the tasks verify current Italian conceptualization and dual lexicalization of BLUE as language specific and not corresponding to English.

AFTERWORD2015

In this revised version of my 2011 paper I have added two tables to give more visual support to the text. I have also supplemented it with some explanation of the results in Table 3, and included a brief glossary. The principle aim however is to acknowledge recent publications that have continued to address the linguistic and cognitive entrenchment and conventions for blu, azzurro, and celeste for Italian speakers [19] [20] [21] [22] [23] [24] [25]. Valdegamberi, Paggetti, Menegaz sustain that there is evidence in favor


of the hypothesis of the existence of the twelfth basic color category in the Italian language [24]. Ronga, Banzanella, Struddsholm, Salvati include azzurro in the group of BCTs analyzed in their study, and find blu rating over azzurro both in the type to token ratio and in the hapax legomena (i.e., types mentioned by a single speaker) with little variation [25]. Ronga’s earlier findings vary from these others, in that she stated

“In Italian, in fact, azzurro (light-blue) is considered a basic colour term and not part of the realm of blue” [3].

Her paper gives and excellent overview of the history of azzurro, revealing, I believe, the previous primary basicness of the term. Paramei, D’Orsi, Menegaz affirm,

“Our results provide additional psycholinguistic evidence that for Italian speakers at least two colour terms are necessary to name the BLUE area, blu ‘dark blue’ and azzurro ‘light-and-medium blue’. Both were shown to behave as basic colour terms, in linguistic and previous psycholinguistics studies” [22].

Nonetheless, a predominance of current research confirms the semantic shift of blu into a more salient role and azzurro into a more secondary position. Some researchers advance the possibility of regional influences, and/or generational differences. Thierry et al. findings [26] uncover an effect of native language on implicit color discrimination, and how language learning seems to modify the location and extent of categorial perception, which may reorganize the representation of perceptual color space. It is conceivable that the second language acquisition, which is now required in Italian elementary schools, and is usually English, may be speeding up a shift in the perception and the semantics of BLUE. The Italian informants were university students from mixed regional backgrounds across all of Italy, but all had studied scholastic English for 4-8 years. Clearly the general presence of English BLUE in Italian daily life, through computers, internet, advertisements, etc., can in itself contribute to a shift in linguistic convention. These factors may be an explanation of the displacement of a cognitive reference point, of the perceptual and conceptual variations introduced by different languages that use variations of blue. As Desgrippes states,

“the cognitive representation of a color is dynamic: it can evolve with diachronic language variation or with language shift, and both older and newer representations

remain retrievable depending on the task at hand” [27],

which seems pertinent to this case, that is, the present findings may differ from past studies. To summarize I return to the initial questions posed: What are the Italian BLUE term semantic relations? My results point to blu as the primary BCT, azzurro as a secondary BCT, and celeste as a subordinate color term. Do azzurro and celeste violate the criterion that a BCT not be a hyponym of another color word, i.e., blu? I would say that azzurro does, though it ranks high in saliency for the informants of these questionnaires, and hence, should still be considered a BCT nonetheless. Does the semantic relation respond to the cognitive need to differentiate between the colors of the sky and the water? This differentiation seems to still be pertinent in the use of blu and azzurro in Italian. In English the sky is blue or sky blue, but the water, referring more often to the ocean than the sea, may be blue, grey, or green. The sea is azure. Is the principal task color term object/concept association, based on the cognitive linguistic approach to linguistic entrenchment, an original valid method to measure basicness? Yes, it has demonstrated to be an effective method to measure basicness. This type of functional or usage-based approach to linguistic investigation is a practical means to verify use of language. Cognitive linguistics sees language as grounded in embodied experience and social interaction, which is critically dependent on conceptualization that is based on linguistic entrenchment; i.e., “the strength of conventional constraints to aspects of word meaning that have attained some sort of default status” [12]. Therefore, meanings experienced and attributed more often will be encountered more frequently in specific contexts or associations, and will become more entrenched in the individual’s conceptualization and conventionalized in the community of speakers. This paper suggests that blu, though still different than English blue, is undergoing a semantic shift that is detracting some of azzurro’s and celeste’s vigor.

GLOSSARY

Entrenchment: A color term may be associated with numerous objects, the greater the number of associations and the greater the distribution of occurrence in a large number of domain matrices, the higher the level of entrenchment of the color term object/concept association; the progressive entrenchment of configurations that recur in a sufficient number of events are then established as cognitive routines [11, 28].

Saliency: A color term is salient if it is readily elicitable, occurs in the idiolects of most speakers, and is used consistently by individuals and with a high degree of consensus among individuals [29].


Consistency: consistency of color usage relates to the probability that a color name, if used by a given subject on the first presentation, will be used again on the second one [30].

ACKNOWLEDGEMENTS

I would like to thank the anonymous reviewers who offered helpful comments and suggestions on previous versions of this paper.

NOTES

i The Italian dictionary online “Grande Dizonario Hoelpl Italiano di Aldo Gabrielli, Hoepli, 2011” and “Il Nuovo Zingarelli – Vocabolario della lingua italiana” (10th edition Nicola Zanichehlli Sp.A. Bologna, 1990, show only the following entries as derivatives, with my translations of the definitions: blu: bluastro [adj. type of blue], blucerchiato [adj. circled in blue], blue-jeans [Engl. n. blue colored cotton pants], blues [Engl. n. a type of music or dance], bluesman [Engl. n. singer or executer of blues], bluette [Fran. adj. type of blue, light turquoise, n. that color blue]; azzurro: azzurrabile [adj. being able to get on the national soccer team referred to as the “Azzurri”], azzurraggio [n. being able to make a yellowish substance white by adding that color (azzurro)], azzurramento [n. treatment of lenses, which takes on a bluish cast, to diminish reflection, making more transparent], azzurrare [v. tr. to dye that color (azzurro)], azzurrastro [adj. a color that reminds you of that color azzurro], azzurrato [v. past participle of azzurrare; adj. lenses that have been treated to diminish reflection], azzurreggiare [v. intr. that which tends to or to be that color (azzurro)], azzurrescenza [n. that which tends to that color (azzurro)], azzurriccio [adj. tending toward that color (azzurro)], azzurrigno [adj. a dull version of that color (azzurro) tending towards grey], azzurrino [adj. tending toward a light delicate version of that color (azzurro), n. the color], azzurrità [n. the quality of being that color (azzurro)], azzurrite [n. a mineral, used as a dye], azzurrognolo [a pale, greyish, or faded version of that color (azzurro)]; celeste: adj. --the first definitions regard first the sky and the heavens in reference to the supernatural, only the later entry refers to “the color of the sky free from clouds”, etc.-- the only derivative that includes its root is celestino, the first entry, [adj. a light form of that color (sky blue)]. N.B. The Nuovo Zingarelli dictionary includes a color Atlantis that lists 11 compounds with the color blu: blu notte, blu di Prussia, blu di Parigi, blu oltremare scuro, blu oltremare chiaro, blu cobalto, blu azzurro manganese, blu d’oriente, blu pavone, blu ceruleo, blu turchese, blu zaffiro; and only one term with azzurro: blu azzurro manganese, and only once celeste.

BIBLIOGRAPHY

[1] Berlin, B., P. Kay, Basic Color Terms: Their Universality and Evolution, University of California Press, Berkeley, [1969] 1991.

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[3] Ronga, I. “L’eccezione dell’azzurro. Il lessico cromatico: fra scienza e società”, Cuadernos de Filología Italiana, 16, pp. 57-79, 2009.

[4] Borg, A., Towards a diachrony of Maltese basic color terms, New Directions in Colour Studies, C.P. Biggam, C.A. Hough, C.J. Kay, D.R. Simmons (eds.), John Benjamins, pp.74-90, Amsterdam/Philadelphia, 2011.

[5] Androulaki, A., N. Gômez-Pestaña, C. Mitsakis, J.L. Jover, K. Conventry, I. Davies, “Basic colour terms in Modern Greek- Twelve terms including two blues”, Journal of Greek, Linguistics 7, pp. 3-47, 2006.

[6] Stanulewicz D., “Polish terms for ‘blue’ in the perspective of Vantage Theory”, Language Sciences 32 (2), pp. 184-195, 2010.

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[8] Rätsep, K., Preliminary research on Turkish basic colour terms with and emphasis on blue, New Direction in Colour Studies, C.P. Biggam, C.A. Hough, C.J. Kay, D.R. Simmons (eds.), Benjamins, pp.133-145, Amsterdam/Philadelphia, 2011.

[9] www.wordreference.com/iten/blu

[10] www.wordreference.com/enit/azure

[11] www.treccani.it/vocabolario/tag/blu/

[12] www.treccani.it/vocabolario/turchino/

[13] Langacker, R.W., Cognitive Grammar, Oxford University Press, Oxford, 2008.

[14] Croft, W., D.A., Cruse, Cognitive Linguistics, Cambridge University Press, Cambridge, 2004.

[15] Davies, I., G.G. Corbett, “A practical field method for identifying basic colour terms,” Languages of the World 9, pp. 25-36, 1995.

[16] Sutrop, U., “List task and Cognitive Salience Index”, Field Methods 13, pp.263-276, 2001.

[17] Moroney, N., The Color Thesaurus, Hewlett Packard Laboratories, Palo Alto, 2008.

[18] Grossmann, M., Colori e lessico: Studi sulla struttura semantica degli aggettivi di colore in catalano, castigliano, italiano, romeno, latino ed ungherese, Narr Press, Tübingen, 1988.

[19] Bimler, D., M. Uusküla, “‘Clothed in triple blues’: sorting out the Italian blues,” Journal of the Optical Society of America A 31, pp. A332-A340, 2014.

[20] Paggetti, G., G. Menegaz, G.V. Paramei, “Color Naming in Italian language”, Color Research and Application, 2015. Early View, DOI: 10.1002/col.21953.

[21] Paggetti G., G. Menegaz, “Exact location of consensus and consistency colors in the OSA-UCS for the Italian language”, Color Research and Application, 38 (6), pp. 437-447, 2013.

[22] Paramei, G.V., M. D’Orsi, G. Menegaz, “‘Italian blues’: A challenge to the universal inventory of basic colour terms”, Journal of the International Colour Association 13, pp. 27-35, 2014.

[23] Uusküla, M. Linguistic categorization of blue in Standard Italian, Colour Studies: A Broad Spectrum, C.J. Kay, C.A. Hough and C.P. Biggam (eds.), John Benjamins, pp. 67-78, Amsterdam/Philadelphia, 2014.

[24] Valdegamberi, V., G. Paggetti, G. Menegaz, “On the perceptual/linguistic origin of the twelfth basic color term in the Italian color lexicon”, Colour and Colorimetry Multidisciplinary Contributions, Vol. VII B, M. Rossi (ed.), Optics and Photonics Series Notebooks 21, pp. 291-298, 2011.


[25] Ronga I., C. Bazzanella, E. Strudsholm, L. Salvati, “Black as night or as a chimney sweep?”, Intercultural Pragmatics 11 (4), pp. 485-520, 2014. DOI 10.1515/ip-2014 - 0022.

[26] Thierry, Guillaume, Panos Athanasopoulos, Alison Wiggetta, Benjamin Deringa, and Jan-Rouke Kuipersb, “Unconscious effects of language-specific terminology on preattentive color perception”, Proceedings of the National Academy of Sciences 106 (11), pp. 4567–4570, 2009.

[27] Desgrippes, M., “Dynamic cognition: when the best example falls out the boundary, communication”, Colour Language and Colour Categorization, Conference 4-7 June 2013, International conference, Institute of the Estonian Language - Eesti Keele Sihtasutus, Tallinn, Estonia.

[28] Hampe, B., Image schemas in Cognitive Linguistics: Introduction. From perception to meaning : image schemas in cognitive linguistics, Beate Hampe and Joseph E. Grady, eds., Mouton de Gruyter, Berlin, pp. 1-14, 2005.

[29] Hardin, C. L., Luisa Maffi (eds.), Introduction, Color Categories in Thought and Language, Cambridge University Press, pp. 1-18, Cambridge, 1997. [30] Robert M. Boynton, Insights gained from naming the OSA colors, Color categories in thought and language, Clyde L. Hardin and Luisa Maffi (eds.), Cambridge University Press, pp. 135-150, Cambridge, 1997.

Original Reference: Sandford J.L. “Blu, azzurro, celeste- What color is blue for Italian speakers compared to English speakers?”, Proceedings of the Eighth National Color Conference. Gruppo del Colore – SIOF - Alma Mater Studiorum Università di Bologna, Facoltà di Ingegneria, Bologna, Italy, 13-14 settembre 2012. In Colour and Colorimetry. Multidisciplinary Contributions. Vol. VIII B, M. Rossi (ed.) – Dip. Indaco – Politecnico di Milano. Maggioli Editore, pp.281-288, Santarcangelo di Romagna (RN), 2012. ISBN 88-387-6137-x. EAN 978-88-387-6137-9.


1 Paola Valentini [email protected]

1 AGAS Department of History, Archaeology, Geography, Art and Performance Arts, University of Florence, Italy

Society in colour: Italian television and the switch to colour

While Italy was proudly celebrating the advent of its own television system on 3 January 1954 and the first official broadcasts were starting to go out, at around the same time, the United States saw the arrival of colour television. Indeed, in December 1953, the Federal Communications Commission (FCC) approved the standard for colour television set out by the National Television System Committee (the same one that established the 525 line standard in 1941). The NTSC colour television standard was chosen for its versatility over other similar experiments, including its CBS rival, since its broadcasts were perfectly compatible with the black-and-white receivers which were widespread at the time. While Italy stood out for black and white, in mid-January 1954, colour television was authorised to operate commercially in the United States, and the analogue colour standard made its entrance on screen. It was then quick to be adopted in Canada, South Korea, Japan and many other countries, and would remain in place up until the arrival of the ATSC Digital system in 2009. Italy, however, officially seemed to have to wait until 1977.

1. POLITICS AND THE COLOUR EXPERIENCE ITALY

In 1965, a RAI studio in Rome was set up with three Colour Television RCA TK40 cameras and a colour TK40 telecine [1-2]. This was the start of the first “technical broadcasting tests”, while elsewhere in Europe, Britain, France and Germany – albeit with an eight-year delay compared to the United States – introduced colour television broadcasting in 1967. In Italy, however, the process came to an abrupt halt, and only seemed to fully open up again on 1 February 1977 with the official launch of colour broadcasting. The study of colour television, which is entirely overlooked in Italy, is often merely limited to accounts of the (political) causes of this delay, which fail to deal with its full complexity. In Italy, this

“continuous succession and interweaving of speeding up and slowing down, of cutting-edge experiments and prolonged periods stagnation”

is not uncommon and, according to Peppino Ortoleva, appears somewhat to be a peculiarity of Italian history of telecommunications. Indeed,

at the very time when television may have seemed to be behind, in 1970, Italy held the record – together with Germany – for the first direct distance dialing [3]. The switch to colour television in Italy conceals something more complex, which cannot be put down merely to technological developments. It is a complex story which is very much still to be written, the main aspects of which will be outlined below.Often the delay in Italy is associated with the battle over the standards running through the world history of colour. It began in the United States, where the choice of system, despite becoming the matrix for its successors, was by no means a painless process. Yet, the contest was especially fierce in Europe, where there was clash between two systems, PAL (Phase Alternation Line) and SECAM (Séquentiel Couleur à Mémoire), between two powers (Germany and France), between two political and expansionist concepts, and indeed between two opposing worldviews [4]. Nonetheless, the impression in Italy was that the late arrival of colour was only the result of uncertainty over the standard. Accordingly, the final move to opt for PAL, approved by the Ministry on 5 April 1975, did not create any particular controversy or backlash in newspapers, where the news was reported in a cut and dry way. This is despite the fact – which has never been properly brought to light – that this decision not only threw out the French standard, but the entire Italian ISA system (Suppression Alternation Identification) patented in 1972 by the Turin-based company Indesit.Indeed, what had been occurring in Italy took the form of a confusing but lively political debate, more than an economic-industrial matter. Any attempt to untangle it is extremely difficult, as it often used as an opportunity to renew (and decide how to renew) the expiring television monopoly, a battle more of principle than substance, more of ideals than politics or economics. The picture is as hazy as the approach and stance adopted by the television phenomenon itself, between “empty and inane anti-capitalist leftism” and “over-indulgence in crass consumerism”, as stated in 1977 by Ugo La Malfa, one the fiercest opponents of colour [5]. What is remarkable when reviewing the newspapers’ coverage of colour is the disconnect between the theoretical discussion on television and the path it has taken. In 1972, in the wake of the widespread Eurovision colour broadcasts on other foreign channels, RAI decided, on an experimental basis, to broadcast the Munich Olympics in colour, alternating – somewhat paradoxically – the SECAM and


PAL standards. As soon as news was given of this initiative by the Italian Government in early August, all newspapers – including those which were left-wing and traditionally almost indifferent to what they considered to be a mere and useless appliance – devoted a great deal of space to the story and to the debate around colour television. The minute-by-minute report of the ups and downs in the political reception of this decision by the Ministry of Posts and Telecommunications follows a script that would be repeated for five years. “Waste”, “unnecessary expense” and “distortion” in the face of Italy’s crisis and real needs, were the most frequently used arguments, together with – something that was perhaps the most accurate premonition given what would take place at the end of the decade – an attempt to make people fear colour television as a “dangerous tonic”, a glare and mirage of an economic miracle that was now impossible [6]. More than the boycott of vehicle manufacturers, or than the Italian Communist Party’s invectives against the persistent misinformation of television now in colour, what emerges from the news reports of the time is the depiction of an Italian society where colour was already a reality for much of the country:

“consumers in the north [...] already have colour TV sets: those in the Lombardy and Piedmont area are served by Swiss TV from Lugano, those around Liguria and Tuscany receive French TV programmes [Telemontecarlo], and those in Adriatic areas capture Yugoslav broadcasts [Tele Koper]” [7].

Within a year, the actual framework becomes clearer: the extraordinary measures put in place on 22 November 1973 to address the oil crisis directly associated television with the politics of austerity, and the “dangerous tonic” was defeated, putting an end to Italy’s experiments in colour. Policy imposed black and white (as well as a compulsory silence as the small screen was required to be turned off early); darkness fell upon Italian cities, where shop windows and signs were turned off and public lighting was halved.There was already, however, a growing relationship between colour and the viewer. Indeed, whether directly or indirectly, colour made a significant contribution to stabilising the television picture in Italy. How the television picture was experienced by viewers in its first twenty years is extremely interesting. At least in Italy, the economic expenditure of mobile external units set up for live broadcasting and the poor performance of radio links used to transmit the signal to the production centre, meant that, from the outset, television filming was carried out with film stock and Arriflex cameras, which were much more manageable than the bulky “backpacks” for the magnetic recorder of portable cameras, and were often preferred by operators with a background in

film. Therefore, the base photographic image, albeit converted electronically, remained for a long time – at least until the mass spread of U-Matic technology introduced by Sony in 1971 but becoming massively widespread in RAI in the second half of the 1970s. This technology became the workhorse of the first private networks – the standard for filming not only documentaries and news reports, but services including the much-loved sports coverage, starting, for example, with the football match summary. Moreover, when TV serials opened up to the outdoors (being initially made entirely live in the studio using small models and effects), they opted for the more flexible film cameras, mixing cinema film and other entirely electronic footage with telecine. It is not easy to establish, in this era of craftsmanship, whether what was in play was a consolidated practice or a particular aesthetic choice of the producers and directors. For instance, already in L’isola del tesoro (1959), Anton Giulio Majano uses film to show, in just a few minutes, a pirate falling from a brig (entirely rebuilt in studio) in the real waters of Lake Fogliano. Conversely, in Il mulino del Po (1963), Sandro Bolchi shoots in film the famous film credits superimposed over the flowing river, while to achieve the famous flood, she chooses to flood studio 3 in Milan. What is certain, however, is that the eye of the viewer perceives, with some degree of clarity, the difference in texture between the two images, which opposes the above-mentioned scenes, as well as the medium close-ups of newscasts with recorded services and an entirely different lighting distance [8].Colour increases this ontological genetic instability of the television picture, which the general “overflow”, so to speak, is not fully able to conceal; in actual fact, it is fuelled by the external environment. First, the viewer’s experience starts to bring about an interaction between the black-and-white television pictures and their colour print counterparts. The advent of television runs parallel to the revival of the rotogravure, characterised by photojournalism and colour pages, such as in Epoca; in magazines with a greater focus on the television world, colour still seemed to interact directly, supplementing or perhaps attempting to overtake the small screen. For instance, in 1962, “Bolero Film” accompanies a service on Canzonissima with an array of colour shots, half a page large, on the ballet and costumes worn by Mina in the episode aired that week [9]; at the same time, the weekly publication Successo, presented an unusual interview between the famous singer and Luchino Visconti, accompanying the text not only with black-and-white feature of the restaurant encounter between the two, but also with several shots of Mina on the set, as Lina Cavalieri, during the making of a Carosello for Industria Italiana della Birra (figures 1-2) [10]. The relationship between the text in black and white and the paratext in colour also relates to cinema, where it was not uncommon for black-


and-white film to clash with the colour of flyers or posters (such as L’avventura, Michelangelo Antonioni, 1960, or Rocco e i suoi fratelli, Luchino Visconti, 1960). Nevertheless, film paratexts now have a narrative or symbolic relationship with the film subject, representing a form of novelisation. For this very reason, the preferred choice was for illustration, such as in the famous posters of Carlo Campeggi or Angelo Cesselon, and where the film frame is used, the image opts for pseudo-toning change or strong colour backgrounds charged with symbolism. Whereas, in the case of colour television paratexts, the relationship seems to be of a calligraphic and didactic nature with both a “faithful” use of colour and, at the same time, the powerful performance of four-colour coated paper, which becomes an emblem of the post-war “universe of coloured objects” [11]. As such, the colour image seems to be a complementary experience to black-and-white television. The above example of Mina is not accidental: the significance of clothing and fashion, make-up and disguise is an essential part of constructing this character, and the periodical’s images not only make the viewer dream of lavish dressing tables, but help to complete the feel of the scene.Nonetheless, through its relationship with paratexts, colour also shows a television picture which is far removed from that commonly-projected window on the world: a manufactured and unstable image, which at the time in Italy, was incomplete and, therefore, awaiting integration

2. COLOURING REALITY: SPORT AND GAMES SHOWS

In 1978, on the pages of Corriere della sera, Luca Goldoni remarked:

“Today, Italy’s television unity is a past memory, we have now returned to the Municipalities and Signorias. Every district has its own private antenna; people no longer converse on the train, but rather just brag about who gets the most channels. If two travellers realise that they have both seen the same programme the night before, they embrace one another” [12].

The commercial sale of colour TV sets led to the spread of the remote control, which came included with all of them, and the transition to colour coincided with the widest proliferation of private and commercial broadcasters and with the practice of “zapping” [13]. However, as mentioned above, for some time, viewers did not all have the same perception of the television picture, so much so that, in Italy, it somewhat questioned – or made into a reconstruction – that sense of immediacy and of living nature that would would immediately characterise it. The perspective on colour shows a complex and protean television picture, which was far from that mere framework or window onto the

world, that would only be fuelled by the meaning constructed by the live coverage from the late 1970s.The relationship and interference between the television picture in black and white and paratexts in colour soon came together with an audiovisual text identity, where the colour system varied according to its communication channel. The crisis and need to renew RAI’s production, leading to a close dialogue with cinema [14], which once again would be over-simplistic to dismiss as a generic “transition to film”. In actual fact, it was a very complex situation both in its causes and its consequences: from the inability to dub with magnetic tape, thereby excluding this option for large international co-productions (L’Odissea, Franco Rossi, 1968), through to the choice between 35 mm (La strategia del ragno, Bernardo Bertolucci, 1970 or I clowns, Federico Fellini, 1970) or “inflated” 16 mm (L’Orlando furioso, Luca Ronconi, 1975). The question of colour – whose mere adoption was the result of various and complex causes – has also been addressed with an equally simplistic approach and cannot be reduced to how close the writer was to the world of television. Whilst the choice of black and white in Francesco d’Assisi (1967) by Liliana Cavani may be traced back to her previous television reports, this is not the case of Leandro Castellani who, despite also being an exponent of television reporting, is motivated – as he himself acknowledged – by the latest developments of the American Cinéma vérité and its different use of black and white for his Le cinque giornate di Milano (1971). At this stage, colour had a fundamental importance which one must begin to reconsider. Firstly, the television picture continued to find its colour elsewhere. Often our memory fails us and we tend to forget that the greatest successes, such as Le avventure di Pinocchio by Luigi Comencini (1972), and the most daring experiments, like the aforementioned Furioso by Ronconi, were seen at the time in black and white. Yet, we also tend to forget that our colour perception of these works is not so much due to the fact that they

Figure 1 (left) – Photograph of the set of Carosello by Industria Italiana della Birra (in Successo, March 1962, p. 63).

Figure 2 (right) – Part of the television frame of Carosello by Industria Italiana della Birra


were aried in colour in following decade, but because of the concurrent availability of colour in cinema theatres: La strategia del ragno was aired on Sunday 25 October 1970 but gained its censorship certificate on 6 October the following year, embarking on an autonomous life of film; at a shorter distance apart, Comencini’s Pinocchio, aired in April 1972, hit the screens of major cities in August. In this period of revival of television narratives, things become more complicated when widening our scope to the above-mentioned foreign broadcasters in the Italian language. We might narrow it down to one case in point: from the 3 October 1971, the RAI viewer was able to see UFO in black and white, a great international success from the British channel ITV, whose episodes were aired in colour in Italian-speaking Switzerland the year before under the title Minaccia dello spazio, which could be received in a large part of Italy. That’s not all: given its success, the producer of the the television series, Kent, made feature films by assembling scenes from “Swiss” TV series in colour, making it finally possible to show everyone in colour the Fuchsia-haired female protagonists or the futuristic base of the SHADO. It was not by chance that the first of these films, Allarme rosso… attacco alla Terra! (1973), was touted as “a great colour film that you’ll never see on TV”.Last but not least, one can see how colour became a central element through a correct and thorough understanding of these particular hybrids, these products of “cinema-television” where the co-occurrence of film and television material – seen in early TV soaps – becomes an integral part of the text and subject matter and, once again, survives the apparent homogenisation caused by telecine. Indeed, the factors at play are not only the clash between cinema and television with different areas of competence in terms of viewing, or the role of the frame and its relationship between the big and small screen, but also the duality of light and colour between cinema and television. The latter is the result of the particular interplay in television between chrominance and luminance signals (used for the brightness of the image which is, in fact, the black-and-white signal) [15]. This aspect is well-known to the great cinematographers, who often work on these productions, such as Vittorio Storaro, pushing the boundaries of film photography or breaking that of television, devoid of strong tonal contrasts.Colour television was yet to triumph in the wake of audiovisual narratives and RAI’s increasingly sought-after twinning with cinema, which – as caustically observed in 1977 by Lietta Tornabuoni – was turning RAI into “an institute of historical and nostalgic studies”, but saw victory with a completely unexpected area of the state monopoly: sports. The regular broadcasts of the Yugoslav network Tele Capodistria in full colour (since 6 May 1971) not only attracted viewers with cartoons or TV series, but also through the European coverage of major sports events. The viewer alternated and soon migrated to watch

the winter Olympic Games in Sapporo (1972) on the Istrian channel, being able to see Gustav Thöni win gold in the giant slalom with his yellow skis as the Italian flag was raised triumphantly for the awards ceremony. The experience would be repeated more often, with the Olympics in Munich, the FIFA World Cup in West Germany (1974), the European Football Championship (1976) and the Olympics in Montreal (1976). Likewise, private broadcasting began in colour (reflected in the broadcaster GBR’s yellow, blue and red initials). The Rome-based broadcaster later gained international renown for its footage during the kidnapping of Moro, gaining authorisation to enter stadiums and provide live coverage in colour of those games where state television would only be able to maintain some degree of appeal thanks to the replays with Carlo Sassi in Domenica sportiva. Through sport, colour provided “real” coverage, with the emblematic example of the Sanremo festival: in 1973, the final would be televised by Eurovision in colour but could only be captured on foreign television and not by RAI repeaters; the following year a fierce TeleNapoli would offer all three events in colour.Soon after the entry into force of the official state colour television, the dichotomy of a picture whose artifice became exposed by colour was finally over, and the colour television was proven to be a key element for the success of the television picture as a “window onto the world”. Colour became the central feature when constructing the television picture, which finally displays that supposed transparency which will becomes one of its key elements.Following the dissatisfaction of its audience (although one might take the foreign example where the second international French Channel ORTF chose the opening of the Winter Olympics from Grenoble on 1 October 1967 to inaugurate their own colour broadcasting), RAI releases the colour broadcasting of the infamous 1972 Summer Olympics in Munich, where the red shirts of the Soviet basketball team overpowering the white jerseys of the US team in the last three seconds of the hotly contested final was even able to stir up excitement among Italian viewers. Yet, television failed on the real “show”, as the Fedayeen’s attack on the Israeli athletes would be covered minute by minute by radio reporters directly linked from within the Olympic village. Conversely, private televisions now commanded the film phenomenon both through companies set up specifically to distribute films to television (such as the Rusconi Audiovisual system) and through direct agreements with major production houses (as in 1979, Berlusconi with Fide and Titanus). Indeed, RAI was not only battling over film content, but over being able to offer a real and credible vision of reality: in 1977, the Giro d’Italia bicycle race was still broadcast in black and white, owing – they say – to the high costs involved in covering such a large event.RAI would later be able to address its problems and the crisis of its system, but thanks to colour, the television picture – and with it the same


media identity – would stabilise in a mix of both highly spectacular and everyday events, as a dirty mirror as it was called, which could now fully deploy its effects. The game show is the most mature and conscious platform where, once again, colour plays a central role. The move from Tele Milano to Canale 5, from a small local cable service to the core of the future Berlusconi - owned network was announced in December 1979 on a game show, I sogni nel cassetto, which only apparently copied RAI’s format – with Mike Bongiorno supported by his most loyal collaborators, such as the notary Ludovico Peregrini or Director Lino Procacci – and a traditional game mechanism focusing on general knowledge questions. However, as commented by Orietta Berti in her theme song I sogni son desideri, the prize is making a dream come true (albeit within the “limit” of 20 million lire). No one remembers what the dreams of competitors were, yet there is a clear colour and spatial translation created by the aspirational scenario where nothing is impossible: the TV set builds the communication situation with lights and colours, with a predominance of strong primary colours, and set designs with warm tones as opposed to the icy blue and grey colours of state television in Rischiatutto. The set created by Graziella Evangelista – the unparalleled set designer who would make an unforgettable mark on commercial television, from Ok, il prezzo è giusto! to Il gioco dei nove and Drive in – contained coloured flowers and rainbows, superimposed elements and dazzling psychedelic optical effects. Everything came together to make the game show space closer to the fantastic world of the Wizard of Oz, where reality and dreams, window onto the world and entertainment coexisted – a far cry from RAI’s inefficient, sterile, dull, grey cabins and theatre boxes [16].The new decade began for Italian society where this quality of colour television had now become a well-established reality. The first images of the Bologna station bombing were shown by private networks in colour, as were the desperate live images from Vermicino awaiting an impossible happy ending, the equally innovative coverage of Enrico Berlinguer’s funeral, the euphoric images of the fall of the Berlin wall, the student who was able to stop the tanks in Tiananmen, and the chilling scenes that crushed teleological ambitions of the Shuttle exploding into a thousand pieces during the festivities in Cape Canaveral. Similarly, in 1980 a television show opens on the glitzy, psychedelic TV set of Tele Milano 58, shining with thousands of colours, where Mike Bongiorno cheerfully strides on stage with his programme Allegria!

BIBLIOGRAPHY

[1]RAI Radio Televisione Italiana, Annuario 1965, Edizione Radio Italiana, Torino 1965.

[2] A. Lari, Sistemi di ripresa e registrazione in RAI dal 1950 ad oggi, Sandit, Albino (BG) 2012.[3] P. Ortoleva, Telecomunicazioni: un modello italiano?, «Memoria e ricerca», n. 5, gennaio-giugno 2000, p. 112.

[4]F. Andreas, “Politique de la grandeur” versus “Made in Germany”: politische Kulturgeschichte der Technik am Beispiel der PAL-SECAM Kontroverse, R. Oldenbourg, München 2007.

[5] A. Ronchey, Intervista sul non governo. Ugo La Malfa, Laterza, Roma-Bari 1977.

[6] G. Crapis, Il frigorifero del cervello: il Pci e la televisione da ‘Lascia o raddoppia?’ alla battaglia contro gli spot, Editori Riuniti, Roma 2002.

[7] R.R., Tv a colori per le Olimpiadi, «Corriere della sera», 2 agosto 1972, p. 7.

[8] M. Mazzanti, L’illuminazione in televisione: manuale per la progettazione e l’uso tecnico/creativo delle luci nello studio TV, Quartz color Ianiro publishing division, Roma 1979.

[9] Anon., Mina. 5 dive in una sola, «Bolero Film», 777, 25 marzo 1962, pp. 30-32.

[10] S. Benelli (a cura di), Luchino Visconti. Mina. La donna, e altre cose ancora, «Successo», marzo 1962, pp. 60-65.

[11] F. Pierotti, La seduzione dello spettro. Storia e cultura del colore nel cinema, Le Mani, Genova 2012, p. 219.

[12] L. Goldoni, Il “pirolino” che fa scoprire le mille tv private, «Corriere della sera», 3 febbraio 1978, p. 14.

[13] P. Ortoleva, Un ventennio a colori. Televisione privata e società, Giunti, Firenze 1995.

[14]F. Pinto, G. Barlozzetti, C. Salizzato (a cura di), La televisione presenta… La produzione cinematografica della Rai 1965-1975, Marsilio, Venezia 1988.

[15] A. Banfi, Corso integrale di televisione a colori, con la collaborazione di A. Nicolich, N. Stucchi, H. Salan, D.S. Fleming, 8 voll., Il rostro, Milano 1966.

[16] P. Valentini, Televisione e gioco. Quiz e società italiana, Archetipo/Clueb, Bologna 2013


presence felt more often than not. The cross-shaped architectural plans of cathedrals were oriented with the altar area space facing toward the East; this placement allowed for light to shine through the stained glass windows and illuminate the holy altar area to magnify the divine effect. This tradition of strategic lighting carries on in Modernist architecture, albeit with varied mid-century twists. So much so that long before a visitor enters the case-study Church of the Epiphany, the light scoops territorialize the exterior with their powerful presence.Walking up Second Avenue from the South, the Church of the Epiphany’s spiky tower slices into the airspace above, flanked on the lower left by a large, thick tower blunted on angle at the top. (See Figures 2 and 3.) Another robust angled mass sits to the right of viewer entry (although the visual presence of this tower is more noticeable from the Western approach, due to current landscaping conditions). The visitor discovers, once inside, that the two squat towers are the light wells for the holy niches edging the entry and sanctuary spaces: the baptismal font and side prayer chapel. The strong massing presence of the light scoops on the exterior spells out the importance of light as a design feature.The Church of the Epiphany’s light scoops emit low drama and even - in spring afternoon light - anticlimactic, rather prosaic, and ineffectual actual light. Yet, the presence of skylights (which means a visual absence of ceiling) above the baptismal font, side prayer niche, and front altar still offers a perceived closer (and less impeded) connection to the divine forces above. (See Figure 4.) The disappointing interior luminance from the light scoops may have been an intentional design choice to convey a religious message of darkness and sin. Or, more likely, the light quality may be partially due to grimy and barred skylights in need of cleaning or poor placement of the skylights themselves. The light quality also suffers from the church site - hemmed in by the tall New York City skyline on surrounding blocks – as is typical in this crowded cityscape. (See Figure 5.) The sun just can’t angle in as directly here as at Kresge Chapel where the site is much more open – even allowing light to bounce in from ground level, as will be discussed below. [The above

1. INTRODUCTION

This paper offers a case-study comparison of the atmospheric effect of the manipulation of color and light in two modernist religious spaces: The Church of the Epiphany by Belfatto and Pavarini in New York City, New York (1965-1967) and Kresge Chapel by Eero Saarinen in Cambridge, Massachusetts (1953-1955). The chosen religious compounds are located in similar climates and with geographic coordinates in close proximity; as a result, the sites have similar locations for sun angles in summer and winter. They house the worship of differing religious practices and are located in urban areas (in contrast to previously researched Midwestern case-studies). As the religious building is a genre typically designed to invoke heightened spiritual experiences, the paper examines how the design choices of each architectural team affect the color experience inside. This proposed paper specifically exposes major massing decisions (placement of mass and volume of buildings), material choice, sculpting of light, proximity of color zones, and location of colors in relation to visitor experience.[Author’s Note: The paper probes further into a topic I began to address in a case-study paper on religious structures in the Midwestern United States presented at the International Color Association AIC 2011 Interaction of Colour and Light Midterm Meeting of the International Color Association in Zurich, Switzerland on 07-10 June 2011. The buildings analyzed were Annunciation Priory by Marcel Breuer in Bismarck, North Dakota (1959-1963); Christ Church Lutheran by Eliel Saarinen and Associates in Minneapolis, Minnesota (1949); and Mount Zion Temple by Erich Mendelsohn in St. Paul, Minnesota (1950-1954).] (See Figure 1 for light parti diagrams of the five spaces mentioned above.)

2. CHURCH OF THE EPIPHANY

In a religious context, light and golden color abound - think the Egyptian Sun God, Ra, or the shining flat halos of Cimabue or gold leaf sheets caked on auspicious statues by the devout people of Thailand, to only name a few [1]. More specifically in religious architecture, controlled and golden light makes its divinely meaningful

1Jada Schumacher [email protected]

1 Communication Design Department, Fashion Institute of Technology

Color, Mon Dieu: A Case-Study Comparison Between The Church Of The Epiphany (New York City) and Kresge Chapel (Cambridge, Massachusetts)


interpretation may, in fact, be too charitable. It could also be argued that the space ineffectively makes use of the promise of light shown in the exterior massing and that the building, as a whole, fails to move visitors in a way that could be defined as positive enough to encourage a return visit - except out of religious obligation.] Belfatto and Pavarini’s church provides almost no sense of intimacy, even in the offset niches that end up feeling more like vacuous caverns or over-scaled chemistry test tubes. In this context, the word “masses” offers an interesting double meaning: the name of the scheduled religious

services and also the idea of throngs of people that function more as numbers than as shining individuals. [In fact, the church offers an emotive feeling similar to Fascist architectural masterpieces such as the Milano Centrale train station. As a traveller emerges from long escalator tube at the bustling Italian thoroughfare, the transit tunnel frames a first exterior view: a grand horse statue foaming at the mouth. Navigating the tight underground passage only to be emotionally trampled upon by a fiery horse, the visitor ingests a palpable message of powerlessness. Once past the huge, seemingly threatening equine and inside the station, the vibe of vulnerability continues for the visitor waiting for a train to arrive. The Fascist iconography and strong frieze imagery, the large shell of space at an inhuman scale, and the whirring noise of people and trains reverberating in the circulation hall encourage the tangible perception of the individual self as one meek, weak entity in a vast system.]The light inside the Church of the Epiphany, during mass on a spring Saturday afternoon, showcases – in the midst of this empty inhumane space – spasmodic glimmers of glory and a glimpse of hope with shining saturated greenish yellow rays hitting parishioners during the service. (See Figures 6 and 7.) Here, select

Figure 1 – Diagrams of the sculpting of light in the five spaces. Clockwise from top left - Kresge Chapel facing the altar, sectional view of Mount Zion Temple, sectional view of original chapel at Annunciation Monastery, Church of the Epiphany facing the altar, and Christ Church Lutheran facing the altari.


groups of parishioners are momentarily lit with the sun’s angled light, sparking a slightly upbeat note in the sanctuary space. This color moment seems to convey that the presence and prayer of individuals matters for a bit. The parishioners embalmed in the glow seem momentarily not nameless, special and bright even, in the midst of this large, dark building envelope. Yet, the chromatic light loses some momentum as it bounces off of neutral, unmemorable floor materials and on the procession of drab brickwork wrapping around the sanctuary. The cool and warm grey pavers in the walkways soak away some of the light color, as the waxy, nearly matte finish dulls this light from the window. (See Figures 8 and 9.)Note here that the yellow rays have a greenish cast. The resultant sour yellow provides just a tinge of the upbeat positivity of yellow hues. But, mixed with a dour, almost sickly green, the yellow is depleted by a wash depicting a color of physical weakness (as in nauseous or ill). Just as the repeated rituals of repentance, expected obedience, and required presence at mass of the Catholic faith provoke human guilt, so too can the yellow-going-green hue convey ideas of a flawed people, at the whim of God, clearly here to meticulously serve God’s will in hopes of a personal redemption. The color and light zones – spaced far apart and composed of matched up jagged shapes of glass in Belfatto and Pavarini’s compound - feel sparse and inadequate although they take up a significant amount of wall square footage. The windows, lighting the main space, are supplemented by the rigid grid of cross-shaped pendant lighting hung from above. Even the mundanely wide circulation routes leave the visitor lacking fulfillment. Alas, the sense of order, controlled shaping, and scale convey an inapproachable, all-powerful message of God. Gratification does not seem to be lastingly calculated into the design of this dark and hollow spatial experience. As such, the light and scale design choices in this vastly impersonal church space can be interpreted as an obvious, but important, physical manifestation of prominent views of the Catholic Church. All is not lost (with or without God), as it can be argued that the Church of the Epiphany – although miserable to visit and disappointing on the interior - paved the religious road for churches to come.

3. KRESGE CHAPEL

If the Church of the Epiphany embodies a hollowness and, at best, a lacking reward-verging-on-punishment, tidy Kresge Chapel offers a potent, radiant counterpoint. Critics argue that architect Eero Saarinen’s religious

Figure 2 – Approach to Church of the Epiphany

Figure 3 - Light wells expressed in building massing

Figure 4 - Cross-shaped skylight above altar (Church of the Epiphany)

Figure 5 - Yellow glow in upper right coming from lower left (Church of the Epiphany)


Figure 6 - Yellow glow hitting the parishioners’ bodies (Church of the Epiphany)

Figure 7 - Yellow glow on floor (Church of the Epiphany)

Figure 8 - Light from North-facing window with candle glow in entry

spaces “adopted open or centralized plans and iconic forms that unified clergy and congregation, performer and audience” [4]. This intimacy in plan is reiterated in sectional design and material choices to offer the visitor an experience akin to the comforting embrace of a close friend – an escape from worldly affairs and from the intellectual pursuits that abound on a renowned campus such as the Massachusetts Institute of Technology.A visitor approaching Kresge Chapel from the South naturally follows a prescribed path along a long walk. A strong wall along the East side of the land and a grove of small trees define and favorably isolate the chapel site from the enormous expanse of the university campus. After entering the building, the circulation route to the chapel forces the visitor to take a sharp left through a naturally lit hallway. This physical movement – a sharp turn – encourages a change in perception. A visitor can abruptly leave the day’s trivialities aside, perhaps setting the scene for shifting into a mode to ponder spiritual existence.The hallway wall is covered in nearly monochrome green abstract stained glass, seemingly verdant. Upon crossing the threshold into sanctuary space, the ceiling height rises. The area is darker than the adjoining hallway, and eyes must adjust to absorb the essence of the space. The outside goings on of undergraduates and backpacks, mere steps away, feel remote, separate from this interior cavity. Here inside, the altar area is lit strong and specific from above. The side lighting seems muted, eerie, almost intangible, and elusive in quality. In only a few paces, there has been a strong shift in light quality and spatial experience.The sanctuary volume itself rests elegantly simple and stoically geometric. The cylindrical building envelope wraps a very small space, approximately 54 feet in diameter [5]. A mere 120 or so seats are set within the space; the chairs are easily movable and, although ordered, do not seem to be ruthlessly arranged. The chairs - caned with a rough straw - remind of the casual, modest pragmatism of a visit to a Scandinavian country cottage in midsummer. The interior feels flexible, not constrained by certain religious dogma, yet still specific, spiritual, and memorable. The interior wall undulates lightly, further exposing prominent textural variations in the texture of the brickwork. With a lack of religious iconography inside the sanctuary itself, the decor consists solely of material color and light variations.Once again, as at the Church of the Epiphany, a light well is located directly above the altar. Here, however, the oculus-shaped puncture in the building envelope provides a hotter, buffered yet concentrated light. This light reflects off of the


shiny, Harry Bertoia-designed metallic sculpture streaming down from above. So, too, the marble altar, up on a platform, reflects light off of its smooth shiny whitish surface. (See Figure 10.) This combination of color, light, and material leaves the altar area highly charged, giving off a blinding glow. A series of lower light scoops surround the base of the building, allowing light to bounce off of the exterior moat and into the cylindrical volume. The lower windows themselves hide from the view of seated parishioners with a half wall, allowing for the light of the overhead oculus to take the foreground while the moat-reflected light gently illuminates lower portions of the surrounding enclosure. The oculus focuses attention; the lower light wells provide ambient

light, a sense of nurturing softness around the sides of the space.For the building’s design, architect Saarinen stated that he was looking to recreate a travelling moment in the Greek isles. On a night in Sparta, he spotted the moon above in combination with a faint illumination along the horizon. Saarinen looked to reproduce this “other-worldly sense” here in the Kresge Chapel experience [6]. The resultant experiential interiorscape shines with light from above and soft enveloping halos from the ground. The small, cylindrical volume of the interior, the quality of light, and the warmth of materials in the sanctuary space create a meditative quality and a soft attitude of inclusiveness, reinforcing the non-denominational principles of this space.

4. ANALYSIS

Comparison to contemporary religious “color containers” in varied geographical locations and time periods can identify precedents, legacies, and effects of the modernist spaces under scrutiny. The breaks in the building envelope (of both Kresge Chapel and Church of the Epiphany) clearly reference windows in early churches (from thick walled Romanesque cathedrals to lacy Gothic monstrosities). The wall punches remind, to be sure, of Modernist icons such as Le Corbusier’s Notre Dame du Haut to contemporary holy venues such as Fuksas Architects’ gem in Folignio, Italy [7]. [See Figure 11.] Indeed, the ceiling punctures at Rafael Moneo’s Iesu Church in San Sebastian, Spain – as in a myriad of sacred shelters including Belfatto and Pavarini’s New York City venue – float above to illuminate the altar and anciliary chapels [8]. And, Tadao Ando’s famed glowing gapped cross in the wall at Church of Light proves a minimalist interpretation of its precedent windowed cross on the sanctuary ceiling plane at the Church of the Epiphany. (The design also nicely incorporates the religious traditions of facing churches to the East which here “allows for light to pour into the space throughout the early morning and into the day, which has a dematerializing effect on the interior concrete walls transforming the dark volume into an illuminated box”) [9]. This combination of sacred light effects proves fruitful in an edgy, Modernist way. These buildings, and many more religious structures, access that Modernist sacred space pile of techniques with shining results.Projects such as the much lauded crape by Steven Holl in Seattle, Washington (1994-1997) blend atmospheric qualities with conceptual qualities to drag the sacred light toolbox solidly into contemporary creative space. The design of this Seattle masterpiece focuses on seven light

Figure 9 – Approach to Kresge Chapel

Figure 10 - Light wells from exterior (Kresge Chapel)

Figure 11 - Chapel of St. Ignatius by Steven Holl in Seattle, Washington)


wells. Holl slyly angles the light wells to produce an inventive atmospheric space; light dumps in from above from each of the cardinal directions. To boot, the seven light chutes directly reference the seven tenets of the chapel’s branch of Catholicism, highlighting different facets of Catholicism than the ones spatially exemplified at the Church of the Epiphany. The conceptual and experiential results are described as:

Seven bottles of light in a stone box; the metaphor of light is shaped in different volumes emerging from the roof whose irregularities aim at different qualities of light: East facing, South facing, West and North facing, all gathered together for one united ceremony. Each of the light volumes corresponds to a part of the program of Jesuit Catholic worship. The south-facing light corresponds to the procession, a fundamental part of the mass. The city-facing north light corresponds to the Chapel of the Blessed Sacrament and to the mission of outreach to the community. The main worship space has a volume of east and west light. At night, which is the particular time of gatherings for mass in this university chapel, the light volumes are like beacons shining in all directions out across the campus [10].

The chapel’s design brilliantly reinforces Jesuit Catholic religious ideologies with built form and light manipulation. Just as at Kresge Chapel, the lighting inspires in both its spirituality and its ethereality:

“What makes the interior so arresting and enigmatic are the halos of softly pigmented light sliced through by shocking patches of otherworldly color” [11].

Not only does the building’s concept intellectually captivate, but the space haptically fulfuls sensory input channels of those who inhabit it. As such, this magnificent pairing of exquisite space and idea offers intellectual and experiential rewards in divine abundance.

5. CONCLUSION

A case-study comparison of the atmospheric effect of the manipulation of color and light of these two American East Coast Modernist religious spaces provides a toolbox of techniques for use of color and light in contemporary design. (See Figure 12.) As outlined above, these techniques can create heightened experiential spaces, manifest a client’s religious ideology, and convey large scale socio-cultural messages.

Further exploration, outside of the space allotted in this particular paper, could analyse these religious spaces during many times of the day and over the course of all of the seasons in the year. This data could provide a counterpoint to possible weather patterns and varied lighting conditions that may alter visitor experience on more inconsistent visits. Exploration of the space during all hours of religious service (and even during the hours occupied by the clergy and staff) could provide a further wealth of information on ritual and use in relation to color and light decisions. Critical examination could then extend from the visual analysis methodologies presented here to scientific color and light data collection of units of luminance in conjunction with calculations compensating for sun angles, roofing material surfaces, incoming light from nearby surfaces (such as a mirrored skin of a skyscraper or a snow covered ground), and similar.More words (or possibly volumes of text) are surely needed to tackle the historical iterations of items in the toolbox, as this paper can only gloss over several rich and varied threads of thought. A thorough scouring of Western religious buildings throughout the entirety of architectural history could further enlighten on chronological developments and refinements in techniques outlined here, as well as point out some innovative one-off moments or oddities in Figure12 – Toolkit for Sacred Light


the continuum of religious space design.As more chromatic explorations launch in mainstream contemporary architecture and interiors (and as Mid-Century Modernism has become a popular contemporary cultural trend), designers need more wealth and variety in color application for the creation of emotionally loaded spaces. Technology forces our world population to have screen-based (and often hand-held) experiences of color and light at a nearly constant frequency. For fulfullment and inspiration, people need physical environments offering tactile and encompassing atmospheric experiences as well. This vein of research builds more robust repertoires of techniques for successfully manipulating color with meaningful intentionality in spiritual and secular spaces of the present day and beyond.

BIBLIOGRAPHY

[1] But, first, let us review that the long, culturally-established tradition that light = sun = divinity /god/goodness/glory. To examine just a few of the roots of this narrative tradition, remember that in fairytales, gold is the symbol of good. Heros and heroines are gold-haired, wearing gold garb and weaponry, sprinting horseback in metallic forests. Even from southern climes where towheads are less common, the fairytale protagonists are frequently blonde. The presence of the coloring and light reflectivity of these golden items symbolizes the radiance of the sun. So, too, in contemporary culture’s modes of storytelling, the Hollywood cinema technique of backlighting heros and heroines exemplifies this method of heightening a perception of a figure’s glory and/or goodness [2] [3].

[2] Harry M. Benshoff, Sean Griffin. America on Film: Representing Race, Class, Gender, and Sexuality at the Movies. Malden, MA: Blackweel Publishing, 2004, p. 236.

[3] Max Lüthi, The Fairytale: As Art Form and Portrait of Man. Translated by Joh Erickson. Bloomington, IN: Indiana University Press, 1984, pp. 13-16.

[4] Walker Art Center and Minneapolis Institute of Art, Eero Saarinen Shaping the Future, No. 72, Minneapolis, MN: Walker Art Center and Minneapolis Institute of Art, June 6, 2008, p. 3.

[5] www.kubuildingtech.org.

[6] Antonio Roman, Eero Saarinen: An Architecture of Multiplicity. New York: Princeton ArchitecturalPress, 2003, p. 14.

[7] http://www.designboom.com Andrea DB, “Doriana Fuksas Interview”, August 26, 2011.

[8] http://www.architectural-review.com/ Michael Webb. “Raphael Moneo’s Iesu Church in San Sebastian.”

[9] http://www.archdaily.com/ Andrew Kroll, AD Classics: Church of the Light/Tadao Ando, January 6, 2011.

[10] http://www.stevenholl.com/

[11] http://www.stevenholl.com/ quoting Sheri Olson from Architectural Record, July 1997.


1,3 Arvi Tavast [email protected],2 Mari Uusküla [email protected]

1 Dept. of Translation Studies, School of Humanities, Tallinn University2 Institute of the Estonian Language, Tallinn3 Dept. of Estonian, University of Tartu

How blue is azzurro? Representing probabilistic equivalency of colour terms in a dictionary

1. INTRODUCTION

Semasiological dictionaries both store and present information about source language words, including their meanings and target-language equivalents. Since this is what dictionary users are consulting a dictionary for, it is the natural way of presenting the dictionary. For compiling and storing dictionary data, however, the solution is less than optimal. The core of the semasiological dictionary data structure is a one-to-many (1:n) relation between words and meanings, i.e. one word can have several meanings, while every meaning has exactly one (source-language) word. In situations of synonymy, information (e.g. definitions and equivalents) must be repeated in each synonym entry, or synonym entries must refer to each other. For both methods or any combinations thereof, semasiological compilation has been shown to cause problems: broken references, synonym conflicts, circularity and inconsistency [1]–[3]. Many of these problems can be avoided using the onomasiological approach for compilation, even if published semasiologically. There is still a 1:n relation, but in the opposite direction: one concept can have multiple designations, while each word has exactly one concept it refers to. Of the problems listed above, the onomasiological data structure makes broken or circular references and synonym conflicts impossible. It still allows inconsistent information to be entered for similar concepts, though. Inconsistencies could only be avoided by systematic terminology work [4], [5]. In terminology, onomasiology is the preferred data structure both in the classical theory [6], [7] and many contemporary approaches [8]–[11]. Due to its limited scalability [12], systematic terminology work is less universally recommended, but has still been successfully used in specialised dictionaries [13], [14]. As a more workable alternative, a partially systematic approach has been used for smaller groups of concepts within a dictionary (many dictionaries of the TSK, e.g. [15]).

Onomasiology is much less known and understood in general lexicography (a notable exception being the Wordnet lexical database [16], [17], which is both onomasiological and partially systematic). In what follows, we argue that taking concepts into account is as feasible

and beneficial for the compilation of general language dictionaries as it is in terminology. We start by analysing the equivalents in bilingual dictionaries between Spanish, Italian, English and Estonian, finding that dictionary entries are inconsistent, circular and lacking discriminative information. Moreover, they also contradict the results of our experiments in Castilian Spanish [18] and Standard Italian [19] using the empirical-cognitive field method [20], [21], which we briefly describe in Section 4.

To conclude, we present extracts from the results of our fieldwork as probabilistic conceptual graphs, representing an n:m relation between words and concepts and encoding the likelihood of a word designating a concept. We propose this data structure as an alternative to the 1:n structures of both semasiology and onomasiology, arguing that it is more robust than the former and more intuitive for the lexicographer than the latter.

2. METHOD OF THE DICTIONARY STUDY

We analysed the dictionary equivalents for the Castilian Spanish terms violeta, morado and lila designating purplish colours, and the Italian terms blu, azzurro and celeste designating bluish colours, which are well known for their lack of direct equivalents in other languages (we purposefully avoid glossing the example terms throughout the article.). The second languages of the bilingual dictionaries were English and Estonian. The dictionary sources are listed in [22]. The procedure was the following:

1. Look up the headwords in Spanish and Italian, getting the English and Estonian equivalents for each.

2. Look up these equivalents in the opposite language direction, getting their back-translations into Spanish and Italian.

3. Present the results as a directed graph of word equivalence relations.

4. Weigh the edges of the graph according to the number of dictionaries that contained this particular relation.


Variation of hyphenation and parentheses was ignored.

3. RESULTS OF DICTIONARY STUDY

Figures 1-4 present the results of the dictionary study. Estonian dictionary data is more sparse due to the smaller number and volume of dictionaries with this language. Dictionaries between English and Spanish (Figure 1) stand out in terms of having a clear convergence of equivalent pairs across dictionaries. The remaining three language pairs exhibit much more variation. The following can be observed to some degree in all four language pairs:

• Equivalents tend to follow orthographic similarities across languages.

• Headword selection is not comprehensive, with even the most frequent colour terms sometimes missing from dictionaries.

Figure 1 – Purplish colours in Spanish-English and English-Spanish dictionaries

Figure 2 - Purplish colours in Spanish-Estonian and Estonian-Spanish dictionaries

• Dictionaries of a single language direction contradict each other and do not justify their choice of equivalents, leaving the user with a seemingly random set of equivalent candidates.

• Dictionaries of two opposite language directions contradict each other.

In Spanish-Estonian dictionaries, the term morado could be either punakasvioletne ‘reddish purple’, tume punakaslilla ‘dark reddish purple’, tumelilla ‘dark purple’ or the Estonian basic term lilla ‘purple’. The term violet could be violett, violetne or lilla (the difference between violett/violetne and lilla in Estonian could mainly be accounted for through people’s idiolects or individual preferences (see more on Estonian purple terms in [23]). The term lila could be (hele)lilla or lilla. Each of the three terms was absent from at least one of the dictionaries. According to the dictionaries, all three Spanish terms could correspond to the Estonian basic term lilla. In Estonian-Spanish dictionaries the term lilla had also a many different counterparts


(violado, violáceo, de color lila, violeta, lila, morado), without any explanation about the conceptual differences. The terms tend to get counterparts through homography, perhaps neglecting the spectrum that the colour terms represent. We could think that the similar word shape is due to the same etymological background and that assures the counterparts, but languages develop differently and words, though from the same source, develop different meanings through cultural impact [24]. For example, Spanish violeta and lila have a very transparent source. However, morado, being a loan from Latin mōrum ‘mulberry’ has a very deep cultural meaning. Perhaps the cultural importance and a different origin of morado has made the terms for purple divide differently from other languages.

4. METHOD OF THE FIELDWORK

The field data was obtained using an empirical-cognitive field method following [20], [21]. The method consists of two tasks. In the list task, the

Figure 3 – Bluish colours in Italian-English and English-Italian dictionaries

Figure4 - Bluish colours in Italian-Estonian and Estonian-Italian dictionaries

participants were asked to name all the colour terms they could think of. In the colour-naming task, 65 matt-surfaced coloured stimuli from the Color-aid Corporation 220 selection were presented to the participants one by one in a random sequence and they were asked to name the perceived stimuli with the appropriate colour term . 65 stimuli were 5x5 cm plywood squares constituting a “coarse, but evenly spread sample of colour space” ([21]; for seletion criteria see [25]). Color-aid is based on Ostwald colour system: each colour can be described by CIE coordinates available in [25]. The tiles were shown to participants in a random order in natural daylight on a neutral gray surface (comparable to Munsell N2). Lighting conditions were similar for all participants. The participants were allowed to use simple words, compounds or even phrases.

The Standard Italian data was collected between 2006 and 2008 in Florence (102 participants, 56 female, age range 11-80, mean 38.6). The Castilian Spanish data was collected in 2012 in Madrid (38 participants, 20 female, age range 22-85, mean 42.7). Participants were all


volunteers with different dialectal, educational and occupational backgrounds (for details see [19]). The interviews were carried out in Castilian Spanish or Standard Italian by a proficient L2 speaker. Estonian data was collected in 2014 (20 participants, 12 female, age range 25-48, mean 31.7). All participants had normal colour vision, as ascertained using either the City University [26] or Ishihara’s [27] colour vision tests

5. FIELDWORK RESULTS

In the list task, we calculated the naming frequency of a colour term, its mean position in the list and the cognitive salience index which unifies these two parameters [28]. In the colour naming task, we took into account the term frequency, the number of tiles assigned to each colour term, and calculated dominance and specificity indices [20] to examine the consensus rate among the participants [18], [29]. We observe which Color-aid tiles the Castilian Spanish terms for purple and Standard Italian terms for blue are attached to. Spanish morado was given with the highest frequency to tiles VBV (60%) and VRV (58%), violeta to tile VBV-T4 (42%) and lila to tile VRV-S3 (37%). Italian blu was most frequently attached to colour tile BVB (named by 54% of participants), while BGB-T3 was regarded as celeste by 57% of respondents. Azzurro was used to describe colour tile BGB by 44% of participants.

Figures 5 and 6 represent extracts from our fieldwork results. The graphs differ from the bilingual graphs of Figures 1-4 by the addition of

concepts (here represented by codes of colour stimuli) between the languages. Edge weights encode the percentage of respondents naming this stimulus with this term and were cut off at 10% (which is why the total of weights for each stimulus is generally less than 100). The graphs contain all stimuli and terms within 3 hops from the original terms, resulting in the inclusion of some terms that intuitively would not belong there. Italian verde, for instance, is included among the bluish colours on Figure 6 because 10% of Estonian respondents called the stimulus BG-S2 sinine ‘blue’ and 24% of the Italian respondents called the same stimulus verde.

6. DISCUSSION

The observed overlaps across categories and shifts across languages illustrate the inadequacy of direct univocal equivalences postulated in dictionaries. Colour terms are an exceptionally easy semantic domain to perform such analysis on, due to the relative ease of presenting colour stimuli to participants - a similar experiment with modal verbs or abstract nouns would be quite complex if not impossible.

However, the same overlaps and shifts are still there regardless of how much is known about the concept, causing the same dangers of misrepresenting linguistic reality in the dictionary. This paper suggests that words should be related to each other only through concepts, since direct relations (synonymy and equivalence) are not flexible enough to

Figure 5 – Purplish colours in Spanish and Estonian fieldwork results


represent the complexity of natural language. We hope that our solution to using an n:m relation between words and concepts instead of the 1:n of traditional onomasiology will make the approach less daunting to lexicographers by removing the need to have separate headword entries for each meaning of a word.

We used probabilistic weighting on our graphs to account for the fact that one meaning of a word can be more likely than another. Here the probabilities were obtained from elicited performance experiments, but other sources could be used as well, some of which do not depend on the semantic class, e.g. vector semantics or word-level alignment of parallel corpora.

Our fieldwork results are consistent with previous studies that underline the special status of blue category in Italian (e.g. [19], [30]–[32]). The status of the purple category has been discussed by [34] and [35]. While [35] regard morado and violeta as synonyms, our field data indicates that these colour terms have different conceptual references.

Our probabilistic conceptual graphs are similar to Dyvik’s semantic mirroring [36], [37] and the workflow of the EFNILEX project [38], [39], differing from them by the explicit addition of concepts into the graph instead of relying on isolation of subgraphs to identify synsets. The objectives of [37] do include obtaining relations between the explicit concepts of Wordnet from parallel corpora; what we add is retention of the probability information rather than reducing it to discrete relations. Finally, from systematic

terminology work ([4], [5]) we differ by the use of probabilities, but also the n:m relations between words and concepts.

ACKNOWLEDGEMENTS

We are indebted to all our Castilian Spanish, Standard Italian and Estonian test participants, and to Triin Kalda who helped to collect the Estonian data.

NOTES

1To be precise, concepts are subjective abstractions of indvidually perceived or imagined objects, dependent on a wide range of variables from medical issues to life experience to cultural norms, not the physical objects (colour cards) themselves. The experimental stimuli merely invoke the processes of perception and categorisation, which can then be followed by finding and uttering a name for the resulting concept. The variation in naming includes the variation in perception, and since the experimenter has no access to subjective processes of the participant, these two can not be separated in the current experiment. In any reference to colour (or any other phenomena) in this paper, perception is always implied to be present as an additional degree of freedom. The use of colour tiles as stimuli in this study is only motivated by the fact that there is much less room for variation in the perception of colours compared to the perception of e.g. kindness or running or even bird or table.

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[2] A. Tavast, The Translator is Human Too: A Case

Figure 6 – Bluish colours in Italian and Estonian fieldwork results


for Instrumentalism in Multilingual Specialised Communication. Tartu: Tartu Ülikooli Kirjastus, 2008.

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[11] P. Faber Benítez, A Cognitive Linguistics View of Terminology and Specialized Language. Berlin: Walter de Gruyter, 2012.

[12] H. Picht and J. Draskau, Terminology: An Introduction. Guildford: University of Surrey, 1985.

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[17] C. Fellbaum, WordNet. Dordrecht: Springer, 2010.

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455-466.

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[33] J. Sandford, “Blu, Azzurro, Celeste: what color is blue for Italian speakers compared to English speakers?” Colour and Colorimetry: Multidisciplinary Contributions, pp. 281-288, 2012.

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are lit by different illuminants. In the framework of digital imaging and computer vision, the illuminant estimation and correction is also referred to as white balance or computational color constancy. The second stage of the color correction pipeline transforms the image data into a standard color space. This transformation, usually called color space transformation or color matrixing, is needed because the spectral sensitivity functions of the sensor color channels rarely match those of the desired output color space. This transformation is usually performed by using a linear transformation matrix. Many illuminant estimation solutions have been proposed in the last few years, although it is known that the problem addressed is actually ill-posed as its solution lacks uniqueness and

1. INTRODUCTION

There are mainly two modules responsible for the color rendering accuracy in a digital camera: the former is the illuminant estimation and correction module, the latter is the color matrix transformation. These two modules together form what may be called the color correction pipeline, or color engine. A simplified processing pipeline of a typical digital camera is reported in Fig. 1, where the color engine is highlighted. The first stage of the color correction pipeline aims to render the acquired image as it was acquired under a known canonical illuminant. This is inspired by the color constancy feature of the human visual system (HVS), i.e. the ability of perceiving relatively constant colors when objects

1Simone [email protected]

1Raimondo [email protected]

1DISCo - Dipartimento

di Informatica, Sistemistica e Comunicazione

Università degli Studi di Milano-Bicocca

Adaptive Illuminant Estimation and Correction for Digital Photography

ABSTRACT

In this paper we briefly review our recent research on classification-based color constancy, where automatically extracted features are used to drive the selection and combination of the best algorithm(s) for each image We also describe how the problem of illuminant estimation and correction is deeply intertwined with the one of color space transformation. Finally, we also highlight research trends in these fields.

Figure 1 – Simplified processing pipeline of a typical digital camera


stability. To cope with this problem, different solutions usually exploit some assumptions about the statistical properties of the expected illuminants and/or of the object reflectances in the scene. In this paper we briefly review state of the art methods and our recent research on classification-based color constancy, where automatically extracted features are used to drive the selection and combination of the best algorithm(s) for each image. Then, we describe how the problem of illuminant estimation and correction is deeply intertwined with the one of color space transformation. And, finally, we highlight research trends in these fields.

2. REVIEW OF COMPUTATIONAL COLOR CONSTANCY ALGORITHMS

Computational color constancy is the process of removing unrealistic color casts from digital images, mostly due to the acquisition conditions. From a computational perspective, computational color constancy is a two-stage process: the illuminant is estimated, and the image colors are then corrected on the basis of this estimate. The correction generates a new image of the scene as if it were taken under a known, canonical illuminant (see for example Fig. 2). A generic image acquired by a

digital camera is mainly characterized by three physical factors: the illuminant spectral power distribution I(λ), the surface spectral reflectance S(λ) and the spectral sensitivities C(λ) of the sensor. Using this notation, the sensor responses at the spatial point with coordinates (x,y) can be then described as:

where w is the wavelength range of the visible spectrum, ρ and C(λ) are three-component vectors. Since the three spectral sensitivities of the sensor C(λ) are usually respectively more sensitive to low, medium and high wavelengths, the three-component vector of the sensor response ρ = (ρ1,ρ2,ρ3) is also referred to as the sensor or camera raw RGB = (R,G,B) triplet (see for example Fig. 3). Assuming that the color I of the illuminant in the scene observed by the camera only depends on the illuminant spectral power distribution I(λ) and on the spectral sensitivities C(λ) of the sensor, the computational color constancy problem is equivalent to the estimation of I by:

given only the sensor responses ρ(x,y) across the image. This is an under-determined problem and therefore cannot be solved without further assumptions and/or knowledge, such as some information about the camera being used, and/or assumptions about the statistical properties of the expected illuminants and surface reflectances. The estimation of the color of the illuminant could be performed if an achromatic patch is present in the image. This is because the spectral reflectance S(λ) of an achromatic

Figure 2 – The two stages of the automatic white balance: the illuminant is estimated, and the image colors are then corrected on the basis of this estimate. The correction generates a new image of the scene as if it were taken under a known, canonical illuminant

Figure 3 - Image formation process

∫=w

λλλλ d)(),,()(),( Cñ yxSIyxρ

∫=w

λλλ d)()( CI I


surface is approximately constant over a wide range of wavelengths, and thus the sensor response ρ is proportional to I, i.e. the RGB of the achromatic patch is proportional to that of the incident light. To reduce the dimensionality of the problem, one common method is to not estimate the whole triplet of the illuminant color, but a 2D projection of it in a chromaticity space. In fact, it is more important to estimate the chromatic components of the scene than its overall intensity.The color correction is usually based on a diagonal model of illumination change derived from the Von Kries hypothesis. This model assumes that two acquisitions of the same scene with the same imaging device but under different illuminants are related by an independent gain regulation of the three imaging channels [1][2]. A diagonal model is generally a good approximation of change in illumination, as shown by Finlayson et al.[2].The colors in a scene, acquired under an unknown illuminant U can be transformed as they were taken under the chosen canonical illuminant by:

where RGB = (R,G,B) is a color in the image acquired under the unknown illuminant, RGB' = (R',G',B') is the color in the corrected image, RGBU = (RU,GU,BU) are the sensor responses of a camera to a reference white under the unknown illuminant and RGBC = (RC,GC,BC) are the corresponding responses under the canonical illuminant. Supposing RGBC is known, to obtain the color correction matrix, we have to estimate the illuminant color RGBU. To this aim several algorithms exist in literature, each with different assumptions [3]-[11]. To improve the illuminant estimation, Schaefer et al. [12] introduced a combined physical and statistical color constancy algorithm that integrates the statistics-based Color by Correlation method with a physics-based technique, based on the dichromatic reflectance model, using a weighted combination of their likelihoods for a given illumination set and taking the maximum likelihood entry. Cardei and Funt [13] obtained good illuminant estimation by combining the results of gray world, white

=

BGR

BBGG

RR

BGR

UC

UC

UC

//

/

'''

patch and neural net methods, considering both linear and non-linear committee methods. We have investigated the idea of not relying on a single illuminant estimation method, but instead of considering a consensus decision that takes into account the compendium of the responses of several algorithms. To overcome the limitation deriving from the estimation of the illuminant color using the response of a single algorithm, methods that rely on the consensus of a set of different algorithms have been proposed [13][14]. The underlying idea is that algorithms that give similar illuminant color estimations have to be trusted more than algorithms that give estimates that are far from the others, and thus the latter ones have to be automatically discarded. A different kind of algorithms proposed in the last few years belongs to the class of classification-based color constancy. The key idea is to exploit automatically extracted information about the content of the images and intrinsic, low level properties of the images.

3. CLASSIFICATION-BASED COLOR CONSTANCY

We have in the last years proposed three different strategies of color constancy algorithm selection: a class-based (CB) [15] and a feature-based (FB) [16], and face-based (FcB) approach [17].

3.1 CLASS-BASED COLOR CONSTANCYThe class-based (CB) algorithm adopts a classification step to assign each image to either the indoor or to the outdoor class (see Fig. 4). The classifier is trained on low level features automatically extracted from the images (see [15] for a detailed description). Two different AWB algorithms have been used for the two possible classes in which the image considered can be classified: on the basis of the classification result, only the corresponding AWB algorithm selected has been applied. The algorithms for the indoor and for the outdoor class are selected from the one listed in [15], evaluating them on an independent training set.

3.2 FEATURE-BASED COLOR CONSTANCYThe feature-based (FB) algorithm is based on five independent AWB algorithms and a classification step that automatically selects which AWB

Figure 4 – Pipeline of the Class-based color constancy


Figure 5 - Pipeline of the Feature-based color constancy

algorithm to use for each image (see Fig. 5). The classifier is trained on low level features automatically extracted from the images (see [16] for a detailed description). The features on which the classification-based color constancy methods rely for the image representation can be divided into two groups: general purpose and specifically designed features. The use of low level features for the automatic selection and combination of the best color constancy algorithm (or combination of multiple algorithms) permitted to outperform existing state of the art algorithms on a widely used benchmark dataset [15],[16]. The class-based and the feature-based color constancy algorithms can be thought as exploiting respectively high-level and low-level features: the CB works on the output of a scene classifier, more precisely an indoor/outdoor classifier; the FB works directly on the low-level features extracted from the image. The use of medium-level features has also been investigated [18][19]: they are used in region-based color constancy algorithms which are able to automatically select (and/or blend) among different color corrections, including a conservative do nothing strategy.A new class of feature-based algorithms replaces the use of hand-crafted features with an end-to-end learning of features and classifiers using Convolutional Neural Networks (CNNs) [37].

3.3 FACE-BASED COLOR CONSTANCYMemory colors could be used as hints to give a more accurate estimate of the illuminant color in the scene. For example, Moreno et al. [20] obtained memory colors for three different objects (grass, snow and sky) using psychophysical experiments. They then used a supervised image segmentation method to detect memory color objects and exploit them to color correct the image using a weighted Von Kries formula. A different approach has been used in [17] where a face detector was used to find faces in the scene, and the corresponding skin colors were used to estimate the chromaticity of the illuminant. The method was based on two observations: first, skin colors tend to form a cluster in the color space, making it a cue to estimate the illuminant in the scene;

second, many photographic images are portraits or contain people. An extension of this idea could be to use “memory objects” in the scene for color constancy. If we are able to automatically recognize objects and logos that have intrinsic colors, we can use them for color constancy. The performances of the proposed color constancy algorithms are very good and they are fully detailed in [14]-[17]. The performance measure adopted is the median angular error between the estimated and measured illuminant. The datasets used are standard and widely for benchmarking algorithms as they are with ground truth illuminant measurements [32].

3.4 ADAPTIVE FACE-BASED COLOR CONSTANCYThe face-based color constancy algorithm described in the next subsection has been extended in different ways [33]. Since one of the assumptions that is often violated in color constancy is the presence of a uniform illumination in the scene, we have extended the applicability of the face-based algorithm to the case of non-uniform illumination. The method is adaptive, being able to distinguish and process in different ways images of scenes taken under a uniform and those acquired under non-uniform illumination. This was the first algorithm which automatically modifies its behavior from global to local color correction according to the analysis of the image content.Furthermore, we designed a more efficient algorithm to estimate the scene illuminant from extracted skin regions using only their mean color value instead of the whole gamut. This algorithm is more suitable for resource-limited camera devices, such as consumer digital cameras and camera phones.

4. COLOR SPACE TRANSFORMATION

The second stage of the color correction pipeline is the device chromatic response characterization and transforms the image data into a standard RGB color space (e.g. sRGB, ITU-R BT.709). This transformation, usually called color matrixing, is needed because the spectral sensitivity functions of the sensor color


channels rarely match those of the desired output color space. Typically this transformation is a 3-by-3 matrix with 9 variables to be optimally determined, and both algebraic [21] and optimization-based methods [22] exist to find it. The typical color correction pipeline can be thus described as follows:

where RGBin are the camera raw RGB values, a is an exposure compensation common gain, the diagonal matrix diag(rawb,gawb,bawb) is the channel-independent gain compensation of the illuminant, the full 3-by-3 matrix a(i,j),(i,j) = {1,2,3}2 = {1,2,3}G{1,2,3} is the color space conversion transform from the device-dependent RGB to the sRGB color space, γ is the gamma correction defined for the sRGB color space (where for abuse of notation it is intended to be applied component-wise), and RGBout are the output sRGB values. Usually the color matrix transform is optimized for a single illuminant and is applied as it is for all the illuminants that can occur. This could lead to high colorimetric accuracy if the occurring illuminant is the one for which the matrix has been derived (assuming that it is correctly compensated by the AWB module), and low colorimetric accuracy for different illuminants. In [23] we have shown how to compute a combined matrix for different classes of commonly occurring illuminants. If only a-priori probability distribution about the illuminant occurrences is known, the best color matrix can be found offline and applied as it is for all the shots; if the AWB is able to give a probability distribution about the illuminant in the scene (as color-by-correlation [9] does), an adaptive optimal matrix transform could be found for each shot. Starting from the observation that the illuminant estimation is not

error free and being an ill-posed problem [14] a perfect algorithm does not exist, color correction matrices, in addition to color space conversion, can incorporate information about the illuminant estimation process in order to compensate for its possible errors [23],[34] (see for example Fig. 6).

4.1 ADAPTIVE COLOR SPACE TRANSFORMATIONIn [35] we designed and tested extended color correction pipelines for digital cameras able to obtain a higher color rendering accuracy. The pipelines proposed in [23],[34] have been further improved in two ways: i) in the illuminant estimation and correction stage, the traditional diagonal model of illuminant change has been replaced by a generalized diagonal transform found by optimization; ii) the color matrixing stage, usually performed using a linear transformation matrix optimized assuming that the illuminant in the scene has been successfully estimated and compensated for, has been extended exploiting polynomial color space conversions incorporating knowledge about illuminant estimation module behavior. [35]

5. CONCLUSIONS AND FUTURE WORKS

Both the illuminant estimation process and the color correction matrix concur in the formation of the overall perceived image quality. We have briefly summarized our research activities concerning them. Interested readers may refer to the cited works to have a detailed description of the algorithms as well as an exhaustive comparison of experimental results with respect to other algorithms in the state-of-art. Independently from the merits of the single algorithms, we would like to point it out that illuminant estimation and color correction have

Figure 6 - Computation of color correction matrices which, in addition to color space conversion can incorporate information about the illuminant estimation process in order to compensate for its possible errors


been always studied and optimized separately, thus ignoring the interactions between them. Our works, to the best of our knowledge, are the first ones that have investigated their interactions and how to optimize them for the overall color accuracy. One of the possible further extension of these works regards the illuminant correction step. Once the scene illuminant has been estimated the scene is usually corrected in the RGB device dependent color space using the diagonal Von Kries model [2]. Several studies have investigated the use of different color spaces for the illuminant correction [24],[25],[26] as well as non-diagonal models [26]. A different approach could be to use chromatic adaptation transforms (CATs) [27] to correct the scene illuminant. CATs are used in color science and color imaging to model illumination change given the source and target illuminants. Since in computational color constancy the source illuminant is unknown, the development of more accurate color constancy algorithms will allow to use more performing CATs [28]. A different strategy for improving the reliability of color constancy algorithms is to increase the quantity of information available for the color constancy by taking two pictures of each scene [29]: the first is taken as normal, while a specially chosen colored filter is placed in front of the camera when capturing the second image. The filter is chosen so that the combined image makes color constancy, or white point estimation easier to solve. The availability of more color information available could be also used to increase the colorimetric accuracy of the device [30] or even for a spectral reconstruction of the scene [31]. The recent development of 3D cameras will surely boost these approaches.

BIBLIOGRAPHY[1] M. D. Fairchild, Color Appearance Models, Addison Wesley, Boston, Mass. 1997.

[2] G. D. Finlayson, M. S. Drew, and B. Funt, “Diagonal transform suffice for color constancy,” in Proc. IEEE Int. Conf. Computer Vision, pp. 164–171, 1993.

[3] K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms; part one: methodology and experiments with synthetic images,” in IEEE Trans. Image Process. 11(9), 972–984, 2002.

[4] K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms; part two: experiments with image data,” in IEEE Trans. Image Process. 11(9), 985–996, 2002.

[5] A. Bruna, F. Gasparini, F. Naccari, and R. Schettini, “Multidomain pixel analysis for illuminant estimation and compensation,” in Proc. Digital Photography II, Proc. SPIE 6069, 60690C, 2006.

[6] V. Cardei, B. Funt, and K. Barndard, “White point estimation for uncalibrated images,” in Proc. IS&T/SID 7th Color Imaging Conf., pp. 97–100, 1999.

[7] H. H. Chen, C. Shen, and P. Tsai, “Edge-based automatic white balance with linear illuminant constraint,” in Visual Communications and Image Processing 2007, Proc. SPIE 6508, 65081D, 2007.

[8] G. D. Finlayson, “Color in perspective,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 1034–1038, 1996.

[9] G. D. Finlayson, P. Hubel, and S. Hordley, “Color by correlation,” in Proc. IS&T/SID 5th Color Imaging Conf.: Color Science, Systems and Applications, pp. 6–11, 1997.

[10] G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221, 2001.

[11] D. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–36, 1990.

[12] G. Schaefer, S. Hordley, and G. Finlayson, “A combined physical and statistical approach to colour constancy,” in Proc. Int. Conf. Comput. Vis. Pattern. Recogn., pp. 148–153, 2005.

[13] V. C. Cardei and B. Funt, “Committee-based colour constancy,” in Proc. IS&T/SID 7th Color Imaging Conf., pp. 311–313, 1999.

[14] S. Bianco, F. Gasparini, and R. Schettini, “Consensus based framework for illuminant chromaticity estimation,” Journal of Electronic Imaging, vol. 17(2), pp. 023013, 2008.

[15] S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Transactions on Image Processing, vol. 17(12), pp. 2381–2392, 2008.

[16] S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Automatic color constancy algorithm selection and combination,” Pattern Recognition, vol. 43, pp. 695–705, 2010.

[17] S. Bianco, R. Schettini, “Color constancy using faces”, Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 65-72, 2012.

[18] F. Gasparini and R. Schettini, “Color balancing of digital photos using simple image statistics,” Pattern Recognition, vol. 37(6), pp. 1201–1217, 2004.

[19] S. Bianco, F. Gasparini, and R. Schettini, “Region-based illuminant estimation for effective color correction,” Proceedings of 15th International Conference on Image Analysis and Processing (ICIAP 2009), LNCS, vol. 5716/2009, pp. 43–52, 2009.

[20] A. Moreno, B. Fernando, B. Kani, S. Saha, and S. Karaoglu, “Color correction: A novel weighted von kries model based on memory colors,” Proc. of the 2011 Computational Color Imaging Workshop (CCIW’11), vol. LNCS 6626/2011, 2011.

[21] P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew, “Matrix calculations for digital photography,” Proceedings of the IS&T/SID Fifth Color Imaging Conference, pp. 105–111, 1997.

[22] S. Bianco, F. Gasparini, A. Russo, and R. Schettini, “A new method for RGB to XYZ transformation based on pattern search optimization,” IEEE Trans. Cons. Electr., vol. 53(3), pp. 1020–1028, 2007.

[23] S. Bianco, A. Bruna, F. Naccari, R. Schettini, “Color space transformations for digital photography exploiting


information about the illuminant estimation process”, Journal of the Optical Society of America A, vol. 29(3), pp.374-384, 2012.

[24] K. Barnard, F. Ciurea, and B. Funt, “Sensor sharpening for computational color constancy,” Journal of the Optical Society of America A, vol. 18, pp. 2728–2743, 2001.

[25] F. Xiao, J. E. Farrell, J. M. Dicarlo, and B. A. Wandell, “Preferred color spaces for white balancing,” Proceedings of the SPIE-IS&T, Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications IV, vol. 5017, pp. 342–350, 2003.

[26] B. Funt and H. Jiang, “Nondiagonal color correction,” Proceedings of the 2003 International Conference on Image Processing (ICIP 2003), pp. 481–484, 2003.

[27] CIE 160:2004, “A review of chromatic adaptation transforms,” ISBN: 9783901906305, 2004.

[28] S. Bianco and R. Schettini, “Two new von kries based chromatic adaptation transforms found by numerical optimization,” Color Research and Application, vol. 35(3), pp. 184–192, 2010.

[29] G. D. Finlayson, S. D. Hordley, and P.Morovic, “Colour constancy using the chromagenic constraint,” Computer Vision and Pattern Recognition (CVPR’05), pp. 1079–1086, 2005.

[30] S. Bianco, F. Gasparini, R. Schettini, and L. Vanneschi, “Polynomial modeling and optimization for colorimetric characterization of scanners,” J. of Electronic Imaging, vol. 17(4), pp. 043002, 2008.

[31] R. Shrestha, J. Y. Hardeberg, and A. Mansouri, “One-shot multispectral color imaging with a stereo camera,” Proceedings of the SPIE-IS&T, Digital Photography VII, vol. 7876, 2011.

[32] http://colorconstancy.com/

[33] S. Bianco, R. Schettini, “Adaptive color constancy using faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36(8), pp. 1505-1518, 2014.

[34] S. Bianco, A. Bruna, F. Naccari, R. Schettini, “Color correction pipeline optimization for digital cameras.” Journal of Electronic Imaging, vol. 22(2), pp. 023014, 2013.

[35] S. Bianco, R. Schettini, “Error-tolerant color rendering for digital cameras.” Journal of Mathematical Imaging and Vision, vol. 50(3), pp. 235-245, 2014.

[36] G. D. Finlayson, M. S. Drew, B. V. Funt. “Color constancy: generalized diagonal transforms suffice.” Journal of the Optical Society of America A, vol. 11(11), pp. 3011-3019, 1994.

[37] S. Bianco, C. Cusano, R. Schettini, “Color constancy using CNNs”, IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.


La prima edizione del Premio Colore della Associazione Italiana Colore ha visto rendere onore al lavoro di un grande artista che usa sapientemente sapori e colori in cucina.Le motivazioni raccontano la scelta di premiare Gualtiero Marchesi, un cuoco, ma che cuoco!

“Il premio colore 2015 va a un grande Maestro, al cuoco italiano più famoso nel mondo, a colui che ha saputo rendere il colore un tratto caratteristico e distintivo di ogni sua preparazione, in una sintesi perfetta tra gusto ed estetica, tra sensi e bellezza.

Nelle sue creazioni il colore è un messaggio chiaro, diretto, quasi ancestrale, una parte complementare ed imprescindibile nell’armonia complessiva dei sensi. La sua tavolozza è misurata per ogni piatto, a voler esaltare al massimo l’intenzione poetica senza fraintendimenti, in un prezioso lavoro di sintesi.

Ha saputo tradurre il suo amore per l’arte in piatti memorabili plasmando le materie prime in forme e colori che diventano a loro volta arte, prima che cibo. Composizioni che rendono omaggio alla tradizione dei grandi Maestri da lui amati, uscendo dalle tele, dai musei, per diventare esperienza multisensoriale.

I colori di Gualtiero Marchesi nutrono gli occhi di bellezza, nutrono di emozioni, di cultura, di memorie … nutrono la nostra anima prima del nostro corpo, grazie Maestro.”

Le motivazioni del riconoscimento ci raccontano quanta parte il colore abbia nella cucina, arte sinestetica che coinvolge tutti i sensi a partire dalla vista, il primo a darci informazioni sulla qualità degli alimenti, sulla loro edibilità, ma anche il primo senso a mostrarci la bellezza.Marchesi cita spesso con orgoglio una frase della figlia: “il bello puro è il vero buono” e questo premio rende omaggio ad una sapienza composta da immaginazione, cultura e abilità, alla grande sensibilità artistica che Marchesi ha saputo trasmettere ai suoi figli, nipoti e ai tanti alunni spesso diventati cuochi famosi a loro volta.

Emanuela Orlando www.orlandoprogettazione.it

I COLORI DI GUALTIERO MARCHESI

RUBRICA | COLUMN

GUALTIERO MARCHESI’SCOLOURS

RUBRICA COLUMN

The first edition of the Colour Price of the Italian Colour Association has honoured the work of a great artist who wisely uses flavours and colours in the kitchen.The reasons tell the choice to reward Gualtiero Marchesi, a chef… and what a chef!!

“The 2015 colour award goes to a great Teacher, to the most famous Italian chef of the world, to he who knew how to render colours a distinct characteristic in every step of his preparations, a perfect combination between taste and aesthetics, between senses and beauty.

In his creations, colours always have a direct, clear, and nearly ancestral message that are complementary and essential parts to the overall harmony of senses.His palette is measured in every plate, in order to enhance the poetic intention without misunderstandings, in a precious synthetic work.

He managed to translate all his love of the art into memorable dishes, moulding his raw materials in shapes and colours that become first art, then food. Compositions that render homage to the tradition of his beloved Masters, coming out from canvases, museums, that become an multisensory experience.

The colours of Gualtiero Marchesi nourish the eyes of beauty, emotions, culture, memories.. nourish our soul before our body, thank you Master.”

These motivations show us the importance of colours in the kitchen, a synesthetic art that involves every sense starting from sight, the first to give us information on the quality of the food, on their edibility, but also the first of the five senses that shows us the beauty of the plate.Often Marchesi proudly quotes his daughter’s phrase: “the pure beauty is the real good” and this prize is a homage to the knowledge made up of imagination, culture and ability, to the great artistic sensibility that Marchesi has been able to transmit to his children, grandchildren and to his numerous pupils, some of whom have become great chefs themselves.


Nella sua storia il colore è sempre presente così come la cultura, la curiosità, l’amore per l’arte, la musica. Così racconta nella sua autobiografia il suo concetto di vivanda:

«è un racconto gastronomico, un intreccio una recitazione dove gli interpreti sono gli ingredienti che si vivificano, si organizzano, interagiscono in un equilibrio di sapori, di colori, di gusto. ….»

Con la sua opera ha svolto un fondamentale ruolo di catalizzatore e stimolo nell’innovazione e modernizzazione della ristorazione italiana; ancora dalla sua autobiografia:

«la cucina contribuisce, e non secondariamente, a fare da ambasciatrice d’italianità e creatrice d’immagine. Fa parte a buon diritto dello stile italiano al pari di altre forme riconosciute come il design industriale, la moda, la musica e l’arte in generale.»

Uomo cordiale, raffinato e colto, gentile e spiritoso, ha saputo raccontare i suoi piatti con semplicità e ironia. Ha parlato della sua vita di ricerca (“la cucina mi ha insegnato l’umiltà e la curiosità”, “la cucina è scienza, sta al cuoco farla diventare arte”) ha condiviso ricordi che intrecciano persone, passioni ed emozioni.

“Un cuoco senza pregiudizi, un anarchico che nel piatto riconosce solo la legge dell’equilibrio, imposta dalla natura. Un pensiero condiviso con Toulouse-Lautrec”

così gli piacerebbe essere definito.

Figura 1 - Gualtiero Marchesi: riso, oro e zafferano Figure 1 - Gualtiero Marchesi: rice, gold and saffron

RUBRICA | COLUMN

In his history colours have always been present just like culture, curiosity, love for art and music have. He tells us in his autobiography his concepts:

«it is a gastronomic story, a play where the actors are the main ingredients that vivify, organise, interact in an equilibrium of flavours, colours, and taste…»

With his work he has taken part in a role as catalyst and stimulus of innovation and modernisation of Italian restoration:

«cuisine is, and not on a secondary level, an ambassador of Italy and creates a renowned image of Italianness. It is a key part of the Italian style together with other like industrial design, fashion, music and art»

A cordial, elegant, cultured, humble and humorous man who was able to recount his dishes with simplicity and irony. He has talked about his life and has shared memories that intertwine people, passions and emotions.

“A chef without prejudices, an anarchic who recognises the natural law of equilibrium in his dishes. A thought shared with Toulouse-Lautrec”:

this is how he would like to be defined.


Figura 2- Gualtiero Marchesi: il rosso e il nero Figura 3 - Lucio Fontana: il rosso e il nero

RUBRICA | COLUMN


RUBRICA | COLUMN

La cerimonia si è conclusa con un’ovazione generale, tutti i partecipanti alla conferenza si sono stretti sul palco intorno a Marchesi per salutarlo, per una foto con lui, per stringergli la mano: il Maestro calorosamente ha salutato tutti senza trascurare baci e abbracci per tutte le signore.

The ceremony closed with a standing ovation, when all the participants reunited on stage around Marchesi to greet him, take a photo, or simply to shake his hand: the Master warmly saluted everyone without overlooking kisses and hugs to all the ladies in the room.


Nel 1° capitolo Causse, cercando di semplificare al massimo la comprensione degli aspetti più ostici, affronta un ventaglio ricco e complesso di questioni che riguardano il coloreL’incipit recita con tono confidenziale:

“A costo di darvi una delusione, vi informo che il colore non esiste!”

e prosegue sostenendo che se le teorie contemporanee sulla luce intesa come trasferimento di energia (fascio di fotoni) si sono affermate da poco è perché

“c’è chi non ha avuto abbastanza coraggio si mandare in pensione l’amico Goethe”.

Con esempi accattivanti (perché le nuvole appaiono bianche, il cielo azzurro e il sole rosso all’orizzonte) conduce il lettore a comprendere alcuni aspetti della fisica, della percezione, della Temperatura di colore, della sinestesia e della riproduzione, ma anche della percezione dei colori nella fauna e nella flora.

Nel 2° capitolo l’autore dimostra come i colori influenzino le facoltà e i comportamenti umani, assorbito dalla retina ma anche dalla pelle, incidendo sull’umore, il bioritmo, l’udito, la

RECENSIONI

Jean-Gabriel Causse, Lo stupefacente potere dei colori Ponte alle Grazie, Milano, 2015

L’editore Ponte alle Grazie (marchio di Adriano Salani Editore) dopo il successo ottenuto dalla pubblicazione dei libri sul colore di Pastoureau promuove un altro libro sul colore e scritto dal francese Jean-Gabriel Causse, color designer membro del Comité Français de la Couleur di Parigi, che è già stato tradotto in 19 lingue.Un libro agile, senza illustrazioni, scritto in tono colloquiale rivolgendosi direttamente al lettore, ricco di aneddoti e di ‘consigli’.Il titolo stesso indica il focus dello scritto: stupire il lettore avvicinandolo alle numerose proprietà dei colori di influenzare la nostra vita personale e collettiva, fatto che nonostante sia ormai scientificamente provato rimane molto sottovalutato.

RECENSIONI

A cura diRenata Pompas


A cura diRenata Pompas

RECENSIONI

memoria, l’emotività e altro. Gli esempi variano dalla neurofisiologia all’etologia, dal suo uso nella pubblicità, ai suoi effetti sul gusto, l’olfatto, il packaging, la vendita, sulle prestazioni degli sportivi, il rendimento degli scolari, la condotta dei carcerati: sarebbe stato infatti dimostrato che le celle dipinte di rosa nel Centro correzionale della Marina americana di Seattle sono state in grado di ridurre l’aggressività dei detenuti dopo soli quindici minuti di esposizione. Inoltre, un paragrafo è dedicato ai “Colori sessuali” perché, come recita l’aletta della copertina,

“Sapevate che la frequenza dei rapporti sessuali è maggiore all’interno di una stanza a dominante rossa invece che grigia?”.

E sebbene il colore preferito in tutto il mondo sia il Blu e quello che genera unanime ostilità sia il giallo, Casse suggerisce di ascoltare istintivamente qual è il nostro colore preferito per trarne benessere: raccomandazione consigliata da un color designer che progetta cartelle tendenze colore e che ne mostra un’apertura notevole. Un altro paragrafo è dedicato all’importanza della luce e al suo rapporto con la melatonina, infine il capitolo si conclude con un accorato richiamo alle proprietà curative della Cromoterapia.

Nel 3° capitolo si trovano i consigli, suffragati di esempi e riferimenti, sulla scelta dei colori nella moda e nell’arredamento in relazione alla loro simbologia e all’apporto del Feng Shui, suddivisi colore per colore: blu, rosso, rosa, verde, nero, grigio, bianco, viola, giallo, arancione e bruno/marrone/beige e turchese che, come scrive l’autore,

“già cinquemilacinquecento anni prima di Cristo era un colore chic”.

Nelle Conclusioni Causse scrive che

“Il colore è indispensabile alla nostra vita, eppure non è mai stato così poco presente accanto a noi, sia sui nostri abiti che nelle nostre case, come lo è oggi. In quale misura un simile grigiore si può ritenere responsabile del basso morale degli occidentali? È difficile da stabilire. Io tuttavia sono persuaso che i graffiti colorati nelle periferie grigie siano opera di ragazzi che inconsapevolmente provano il bisogno viscerale di uscire da questa cupezza (…) come ha detto l’umorista Pierre Dac: se la materia grigia fosse più rosa, il mondo avrebbe meno idee nere”.

Nella Appendice Causse confronta le simbologie dei colori nel mondo, colore per colore, tratte da: Goethe, Kandinsky, Pastoureau, Grossman e Wisenblit, dizionario teologico di Elwell, École Polytechnique di Montréal, trattati di Feng Shui e di medicina ayurvedica.

Da ultimo le ventidue pagine dedicate alle Fonti legittimano il tono leggero e divertente del libro.



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