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Compression video overview. 演講者:林崇元. Outline. Introduction Fundamentals of video compression Picture type Signal quality measure Video encoder and decoder Standard ’ s. Introduction. Why we need to compression Picture - PowerPoint PPT Presentation
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Compression video overview
演講者:林崇元
Outline Introduction Fundamentals of video
compression Picture type Signal quality measure Video encoder and decoder Standard’s
Introduction Why we need to compression
Picture A picture consists of three rectangular matrices
representing luminance (Y) and two chrominance (Cb and Cr) values
The Y matrix has an even number of rows and columns
The Cb and Cr matrices are one-half the size of the Y matrix in each direction (horizontal and vertical).
Introduction Applications for image, video, and audio compression
Introduction Achieve high compression performance
while keep good picture quality Theorem
Spatial redundancy – DCT,DFT,subband,wavelet
Temporal redundancy – MC/ME Statistical redundancy – VLC, Entropy
coding Perceptual redundancy – VQ
Introduction Tradeoffs in lossy compression
Fundamentals of video compression Use the technique of the JPEG
DCT based coding scheme DCT transform (2D)
Fundamentals of video compression Use the technique of the JPEG
Discrete cosine transform
Fundamentals of video compression Use the technique of the JPEG
DCT based coding system
Image
Spatial-to-DCT domain transformation
8 x 8 DCT
Lossless coding of DCT domain samples
Entropy Coding
Discard unimportant DCT domain samples
Quantization
Fundamentals of video compression Quantization
Eyes are insensible to high-frequency components
The greater quantizer means greater loss
Lower frequency component has smaller quantizer, high frequency component has greater quantizer
The quantiation tables in the encoder and decoder are the same
Fundamentals of video compression Use the technique of the JPEG
The spatial domain is redundancy For the DCT-based coding system on an
image-by-image, one can achieve close to 14Mbits per second, which is too high for practical uses
For lower bit rate, we must introduce temporal redundancy
Fundamentals of video compression Temporal redundancy
The temporal correlation in an image sequence
Fundamentals of video compression Temporal redundancy
Instead of 3-D DCT, most video coders use a two-stage process to achieve good compression
Two-stage video coding process
Fundamentals of video compression Temporal redundancy
Motion estimation
Fundamentals of video compression Temporal redundancy
Full search algorithm
Picture type Video bit stream
Picture type Slice
One or more "contiguous'' macroblocks. The order of the macroblocks within a slice is from left-to-right and top-to-bottom.
Macroblock A 16-pixel by 16-line section of luminance components
and the corresponding 8-pixel by 8-line section of the two chrominance components.
Block A block is an 8-pixel by 8-line set of values of a
luminance or a chrominance component.
Picture type Intra picture
Coded using only information present in the picture itself
I-pictures provide potential random access points into the compressed video data.
I-pictures use only transform coding
Picture type Predicted picture
coded with respect to the nearest previous I- or P-picture.
P-pictures use motion compensation Unlike I-pictures, P-pictures can propagate
coding errors
Picture type Bidirectional picture
Coded use both a past and future picture as a reference
B-pictures provide the most compression and do not propagate errors
Picture type The choice of picture type
The MPEG algorithm allows the encoder to choose the frequency and location of I-pictures is based on the application's need for random accessibility and the location of scene cuts in the video sequence
The encoder also chooses the number of B-pictures between any pair of reference (I- or P-) pictures. This choice is based on factors such as the amount of memory in the encoder and the characteristics of the material being coded
Picture type Typical display order of picture types
Video stream composition The MPEG encoder reorders pictures in the video
stream to present the pictures to the decoder in the most efficient sequence
Signal quality measure SNR (signal-to-noise ratio)
encoder input signal energy
SNR = 10log10
noise signal energy
PSNR (peak signal-to-noise ratio) Instead of using the encoder input signal,
one uses a hypothetical signal with a signal strength of 255
Video encoder
Video decoder
MPEG-1 Media storage Optimal for frame size 352x240x30 Bitrate : up to 1.5 Mbit/s International standard in 1992
MPEG-2 Applications from storage to HDTV Bitrate : standard definition TV:4-9 Mbit/s HDTV:15-25 Mbit/s Interlaced/non-interlaced Scalability Capable of decoding MPEG-1 bitstream International standard in 1994 Single chip for video and audio
MPEG-4 Applications for multimedia communication Bitrate : 10K-25 Mbit/s Object – based coding Natural and synthetic video Scalability Robust and error resilience International standard in 1998 Single chip for video and audio