Improvements to the JPEG-LS prediction scheme

Improvements to the JPEG-LS prediction scheme

Authors: S. Bedi, E. A. Edirisinghe, and G. Grecos

Source : Image and Vision Computing. Vol. 22, No. 1, 2004, pp. 9-14

Speaker: Chia-Chun Wu (吳佳駿 )

Date : 2004/09/15

2

Outline

• JPEG-LS prediction scheme

• Improvements JPEG-LS

• Our method

• Sample images

• Experimental results

• Comments

3

JPEG-LS prediction scheme

ˆIn smooth, , when a = b = c = dx a

c b d

a x

In nonsmoth,

min , , if max ,

ˆ max , , if min ,

, if min a,b max ,

a b c a b

x a b c a b

a b c c a b

ˆ: the predictive valuex50 50 50

50 50

31 20 40

30 20

JPEG-LS predictive template

4

Improvements JPEG-LS

ˆ3

a b dx

1 1

2

max , OR min a,b

AND

c a b T c T

abs a b T

1 2, Ideally T T

c b d

a x

200 50 5

50 30

200 50 8

50 36

ˆ (50+50+8)/3=108/3=36 x

Diagonal edge T1=20, T2=0

5

Our method

ˆ3

a b cx

c b d

a x

200 50 8

50 30

200 50 8

50 100

ˆ (50+50+200)/3=300/3=100 x

6

Sample images

Airplane Baboon

7

Sample images

Barb Boat

8

Sample images

Girl Gold

9

Sample images

Lena Lenna

10

Sample images

Pepper Sailboat

11

Experimental results

N

2

ii 1

ˆxPMSE

ix

N

78 90 74

64 145 142

100 63 97

70 87 76

60 140 135

100 59 99

2 2 2 2 2 2 2 2 2PMSE=(8 3 2 4 5 7 0 4 2 ) / 9

(64 9 4 16 25 49 0 16 4) /

9

187 0 / 9 2 .7

Predictive mean squared error

N = 9

12

Experimental resultsTable 1 Lossless compression ratios of all test images (Unit: Bytes)

Image 傳統JPEG-LS

作者提出的方法

我們的方法

Airplane 118204 118210 121769

Baboon 184298 184467 186268

Barb 156728 156830 159434

Boat 134376 134432 137859

Girl 123198 123199 125974

13

Experimental resultsTable 2 Lossless compression ratios of all test images (Unit: Bytes)

Image 傳統JPEG-LS

作者提出的方法

我們的方法

Gold 138765 138778 141910

Lena 121322 121316 125340

Lenna 123200 123202 125604

Pepper 126636 126630 128687

Sailboat 156263 156286 157521

14

Experimental resultsTable 3 PMSE values of all test images (T1=20, T2=0)

Image 傳統JPEG-LS

作者提出的方法

我們的方法

Airplane 33.135755 32.958340 113.812148

Baboon 249.629536 251.804470 393.657663

Barb 186.421616 185.490680 247.140415

Boat 49.250247 49.718750 148.811983

Girl 20.418006 20.301835 69.531317

15

Experimental resultsTable 4PMSE values of all test images (T1=20, T2=0)

Image 傳統JPEG-LS

作者提出的方法

我們的方法

Gold 33.715391 33.942265 88.004221

Lena 31.184482 30.837186 90.242236

Lenna 32.613686 31.928698 86.730731

Pepper 29.134177 28.974733 85.146601

Sailboat 89.210825 89.294461 135.007134

16

Comments

• 本篇論文的預測方法，獲得的 PMSE整較傳統的 JPEG-LS好，但是影像整體的壓縮率卻沒有明顯的提升。

• 跟傳統的 JPEG-LS及本篇論文的方法比較，目前我們提的預測方法，並沒有提升預測的準確度及影像的壓縮率。

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Source Code

if (Rc >= MAX(Ra,Rb))

Px = MIN(Ra,Rb);

else if (Rc <= MIN(Ra,Rb))

Px = MAX(Ra,Rb);

else Px = (Ra + Rb - Rc);

Errval = (Ix - Px);

PMSE = PMSE+ (Errval*Errval);

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Source Code if ( (((Rc -MAX(Ra,Rb)) > Threshold1) || ((MIN(Ra,Rb)-Rc) > Threshold1)) && (ABS(Ra-Rb) <= Threshold2) ) { Px=(Ra + Rb + Rd)/3; } else if (Rc >= MAX(Ra,Rb)) Px = MIN(Ra,Rb); else if (Rc <= MIN(Ra,Rb)) Px = MAX(Ra,Rb); else Px = (Ra + Rb - Rc); Errval = (Ix - Px); PMSE = PMSE+ (Errval*Errval);

19

Source Code

Px = (Ra + Rb + Rc) /3;

Errval = (Ix - Px);

PMSE = PMSE+ (Errval*Errval);