distributed video coding bernd girod, anne margot aagon and shantanu rane, proceedings of ieee, jan,...
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Distributed Video Coding
Bernd Girod, Anne Margot Aagon and Shantanu Rane, Proceedings of IEEE, Jan, 2005
Presented by Peter
Outline
Introduction Foundations of distributed Coding Low-complexity video encoding Robust video transmission Conclusion
Introduction
Standards like MPEG and H.26x, the encoder exploits the statistic of the source signal
Efficient compression can also be achiebed by exploiting sources statistic – partially or wholly, at the decoder ONLY
It is the consequence of information-theoretic bounds established in 1970s By Slepian and Wolf for distributed lossless coding By Wyner and Ziv for lossy coding with decoder side
information The traditional balance of complex encoder and simple
decoder is essentially reversed
Foundations of Distributed CodingSlepian-Wolf theorem for lossless distributed coding Distributed compression refers to the coding of 2(or more)
dependent random sequence Each encoder sends a sends a separate bit stream to a single
decoder Decoder operates jointly on all incoming bit streams and thus
exploit the statistical dependencies
Foundations of Distributed CodingSlepian-Wolf theorem for lossless distributed coding Consider 2 statistically dependent i.i.d. finite-alphabet random
sequences X and Y Can do better with joint decoding (but separate encoding) Slepian-Wolf theorem establishes the rate region RX + RY ≥ H(X,Y), RX ≥ H(X|Y), RY ≥ H(Y|X) Surprisingly, the sum of rates RX + RY can achieve the joint
entropy H(X,Y), despite separate encoders for X and Y
Compression with decoder side information A special case of the distributed coding problem Side information Y is available at the decoder but
not at the encoder RY = H(Y) is achievable for encoding Y RX ≥ H(X|Y) , regardless of the encoder’s access to
side information Y
Rate-Distortion Theory for Lossy Compression with Receiver Side Information In 1970s, Wyner and Ziv extended Slepian and Wolf’s work
for lossy compression They showed that in the case of Gaussian
memoryless sources and mean-squared error distortion In 2003, S. Pradhan et al. showed that source
sequences X that are the sum of arbitrarily distributed side information Y and independent Gaussian noise
In 1996, Zamir proved that the rate loss is less than 0.5b/sample for general statistics and a mean-squared error distortion measure
0|| DRDR YXWZ
YX
0|| DRDR YXWZ
YX
Low-complexity Video Encoding Current video compression standard require much more
computation for the encoder than for the decoder (5-10 times) Well suited for broadcasting or for streaming VOD systems
Some applications require low-complexity encoders, e.g. wireless video sensors for surveillance, wireless PC cameras, mobile camera phones… etc.
The Wyner-Ziv theory suggests that individual frames can be encoded independently but decoded conditionally
Key frames are intra coded using conventional methods Non-key frames are intra coded using Wyner-Ziv encoder and
decode using Wyner-Ziv decoded with key frames as “side information”
Low-complexity Video Encoding Even if the receiver is another complexity-
constrained device, Wyner-Ziv can be used in conjunction with a transcoding architecture
Pixel-Domain Encoding
The simplest system that the authors have investigated Combination of a pixel-domain intraframe encoder and interframe
decoder system The decoder assumes the difference between the side
information and the original pixel are Laplacian distributed “Request-and decode” process is repeated used until an
acceptable probability of symbol error is researched Neither motion estimation and prediction, nor DCT and IDCT are
required at the encoder Requires 2 feedback shift registers and an interleaver Experiments on PIII 1.2Ghz machine
Average encoding runtime about 2.1ms/frame for the Wyner-Ziv scheme
36/ms/frame for H.263+ I-frame coding 227.0ms/frame for H.263+ B-frame coding
Pixel-Domain Encoding
Pixel-Domain Encoding
Transform-Domain Encoding
The authors theoretically studied the transformation of both the source vector and the side information
Block-wise DCT (4x4) is used and DCT coefficients are grouped into subbands
Similar to pixel domain, Laplacian residual model is assumed
Laplacian parameters are trained from difference sequences
A gain of up to 2dB over pixel-based system is observed
Transform-Domain Encoding
Pixel-Domain and Transform-Domain Encoding
Joint Decoding and Motion Estimation Joint decoding and motion estimation at the decoder A robust hash code word is sent to aid the decoder
in estimating the motion When motion exists, the block’s hash code is sent
along with the Wyner-Ziv bits Decoder performs motion search to generate the
best side information block from the previous frame 5-20% of the hash codewords are sent Substantially outperform conventional intraframe
DCT coding, still a gap relative to H.263+ interframe coding
Joint Decoding and Motion Estimation
Robust Video Transmission
Wyner-Ziv coding can be thought of as a technique which generates parity information to correct the “errors’ of the correlation channel
A source signal is transmitted over an analog channel without channel
An encoded version is sent over a digital channel as enhancement information
Reed-Solomon codes are used, only the parity symbols are transmitted to the receiver when error occurs
The authors refer the system as systematic lossy error protection (SLEP)
Robust Video Transmission
Robust Video Transmission
Robust Video Transmission
Robust Video Transmission
Conclusions
Distributed coding is a fundamentally new paradigm for video compression
Slepian-Wolf encoding, is fundamentally harder for practical applications due to the general statistics of the correlation channel
The rate-distortion performance of Wyner-Ziv coding does not yet reach the performance of conventional interframe coder
Its inherent robustness is a further attractive property, graceful degradation with deteriorating channel conditions can be achieved without a layered signal representation
It is unlikely that distributed video coding algorithm will ever beat conventional video coding schemes in R-D performance]
The authors believe that distributed coding techniques will soon complement conventional video coding to provide the best overall system performance and enable novel applications
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