Transcript Slide 1
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 Coding Slepian-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 Coding Slepian-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 RWZ X |Y D RX |Y D 0 in the case of Gaussian memoryless sources and mean-squared error distortion In 2003, S. Pradhan et al. showed that RWZ X |Y D RX |Y D 0 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 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 complexityconstrained 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 Comments?