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Reinventing Compression: The New Paradigm of Distributed Video Coding

Reinventing Compression: The New Paradigm of Distributed Video Coding. Bernd Girod Information Systems Laboratory Stanford University. Outline. Lossless and lossy compression with receiver side information Shifting the complexity of video encoding to the decoder

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Reinventing Compression: The New Paradigm of Distributed Video Coding

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  1. Reinventing Compression:The New Paradigm ofDistributed Video Coding Bernd Girod Information Systems Laboratory Stanford University

  2. Outline • Lossless and lossy compression with receiver side information • Shifting the complexity of video encoding to the decoder • Error-resilient video transmission • Image authentication

  3. Outline • Lossless and lossy compression with receiver side information • Shifting the complexity of video encoding to the decoder • Error-resilient video transmission • Image authentication

  4. Lossless Compression with Side Information R≥ H(X|Y) Encoder Decoder Statistically dependent Side Information R≥ ? Encoder Decoder Statistically dependent Side Information

  5. Lossless Compression with Side Information R≥ H(X|Y) Encoder Decoder Statistically dependent R≥ H(X|Y) Encoder Decoder Statistically dependent [Slepian, Wolf, 1973]

  6. Towards Practical Slepian-Wolf Coding • Convolution coding for data compression [Blizard, 1969] • Convolutional source coding [Hellman, 1975] • Syndrome source coding [Ancheta, 1976] • Coset codes [Pradhan and Ramchandran, 1999] • Trellis codes [Wang and Orchard, 2001] • Turbo codes [García-Frías and Zhao, 2001] [Bajcsy and Mitran, 2001] [Aaron and Girod, 2002] • LDPC codes [Liveris, Xiong, and Georghiades, 2002] • . . . • . . .

  7. Encoder Buffer 0.7 Turbo Encoder Turbo Decoder 0.6 Rate-adaptive turbo codes 0.5 0.4 Rate Slepian-Wolf bound 0.3 Rate = H(X|Y) 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 H(X|Y) Rate-Adaptive Slepian-Wolf Coding Parity bits Request bits L = 8192 bits Total simulated bits = 226

  8. Lossy Compression with Side Information RX|Y (d) Encoder Decoder [Wyner, Ziv, 1976] For MSE distortion and Gaussian statistics, rate-distortion functions of the two systems are the same. [Zamir, 1996]The rate loss R*(d) – RX|Y (d) is bounded. R*(d) Encoder Decoder

  9. Practical Wyner-Ziv Coding Wyner-Ziv Decoder Wyner-Ziv Encoder Slepian- Wolf Decoder Minimum Distortion Reconstruction Slepian-Wolf Encoder Quantizer

  10. Non-Connected Quantization Regions • Example: Non-connected intervals for scalar quantization • Decoder: Minimum mean-squared error reconstruction with side information x x

  11. Outline • Lossless and lossy compression with receiver side information • Shifting the complexity of video encoding to the decoder • Error-resilient video transmission • Image authentication

  12. Interframe Video Coding PredictiveInterframe Encoder PredictiveInterframe Decoder X’ Side Information

  13. Low Complexity Encoder Wyner-ZivIntraframe Encoder Wyner-ZivInterframe Decoder X’ Side Information [Witsenhausen, Wyner, 1980] [Puri, Ramchandran, Allerton 2002] [Aaron, Zhang, Girod, Asilomar 2002]

  14. Pixel-Domain Wyner-Ziv Video Codec Interframe Decoder Intraframe Encoder WZ frames Slepian-Wolf Codec Reconstruction Turbo Decoder Turbo Encoder Scalar Quantizer X X’ Buffer Request bits Side information Y Interpolation/ Extrapolation Key frames Conventional Intraframe decoding Conventional Intraframe coding I I’ [Aaron, Zhang, Girod, Asilomar 2002]

  15. Pixel-Domain Wyner-Ziv Video Codec After Wyner-Ziv Decoding Decoder side informationgenerated by motion-compensated interpolationPSNR 30.3 dB 16-level quantization – 1.375 bpp11 pixels in errorPSNR 36.7 dB

  16. Pixel-Domain Wyner-Ziv Video Codec After Wyner-Ziv Decoding Decoder side informationgenerated by motion-compensated interpolationPSNR 24.8 dB 16-level quantization – 2.0 bpp0 pixels in errorPSNR 36.5 dB

  17. DCT-Domain Wyner-Ziv Video Codec Intraframe Encoder Interframe Decoder WZ frames Xk Xk’ Turbo Encoder Recon Scalar Quantizer Turbo Decoder W W’ DCT IDCT Buffer Request bits Side information Yk For each transform band k DCT Y Interpolation/ Extrapolation Key frames Conventional Intraframe decoding Conventional Intraframe coding I I’

  18. Interframe 100% 3 dB 6 dB Rate-Distortion Performance - Salesman Encoder Runtime Pentium 1.73 GHz machine • Every 8th frame is a key frame • Salesman QCIF sequence at 10fps 100 frames

  19. 3 dB 8 dB Rate-Distortion Performance – Hall Monitor • Every 8th frame is a key frame • Hall Monitor QCIF sequence at 10fps 100 frames

  20. Salesman at 10 fps DCT-based Intracoding 149 kbps PSNRY=30.0 dB Wyner-Ziv DCT codec 152 kbps PSNRY=35.6 dB GOP=8

  21. Hall Monitor at 10 fps DCT-based Intracoding 156 kbps PSNRY=30.2 dB Wyner-Ziv DCT codec 155 kbps PSNRY=37.1 dB GOP=8

  22. Outline • Lossless and lossy compression with receiver side information • Shifting the complexity of video encoding to the decoder • Error-resilient video transmission • Image authentication

  23. Analog Channel Digital Channel Encoder Decoder Side info Side info Digital Channel Digital Channel Wyner-Ziv Decoder Wyner-Ziv Decoder Wyner-Ziv Encoder Wyner-Ziv Encoder Systematic Lossy Source/Channel Coding • Information theoretic optimality conditions [Shamai, Verdú, Zamir, 1998] • Enhancing analog image transmission using digital side information [Pradhan, Ramchandran, 2001] • Lossy source-channel coding of video waveforms [Rane, Aaron, Girod, 2004,’05,’06]

  24. Systematic Lossy Error Protection (SLEP) “Analog Channel” Video With Errors Video Encoder Video Decoder With Error Concealment Input Video Channel Side Information Wyner-Ziv Encoder Wyner-Ziv Decoder Output Video

  25. Q-1 Entropy Decoding Recovered motion vectors for erroneously received primary slices WYNER-ZIV ENCODER WYNER-ZIV DECODER SLEP using H.264/AVC Redundant Slices H.264/AVC ENCODER H.264/AVC DECODER Output Video Input Video Entropy Decoding Encode Primary Pic + T-1 MC Motion Vecs + Coding Modes Motion Vecs + Coding Modes Encode Redundant Pic (Requantize) Encode Redundant Pic (Requantize) Error-prone Channel Side info QP Decode Redundant Slice Erasure Decoding Parity Slices QP RS Encoder

  26. Foreman @ 408 kbps, error resilience bit rate = 40 kbps Symbol error probability = 5 x 10-4 Error-free After error propagation QP = 28 35.7 dB Error concealment only SLEP with redundant QP = 36 40 kbps FEC SLEP with redundant QP = 40 SLEP with redundant QP = 48 20.9 dB 30.9 dB 25.5 dB 34.2 dB 32.9 dB

  27. Foreman @ 1 Mbps Symbol error probability = 2 x 10-4 100 kbps FEC PSNR: 32.5 dB Recovered 53.7 % of lost macroblocks 100 kbps Wyner-Ziv bit stream PSNR: 38.0 dB Recovered 96.6 % of lost macroblocks

  28. Rally, 1 Mbps, 3% packet loss 80 kbps FEC 33.4 dB 80 kbps Wyner-Ziv bit stream 38.1 dB Recovered 67.5 % of lost macroblocks Recovered 97.1 % of lost macroblocks

  29. Outline • Lossless and lossy compression with receiver side information • Shifting the complexity of video encoding to the decoder • Error-resilient video transmission • Image authentication

  30. Media Authentication Problem Received Illegimate degradation (e.g., compression + tampering) Original Legimate degradation (e.g., compression) How to distinguish legimate and illegimate signal degradationswithout access to the original?

  31. Image Authentication by Distributed Coding Received Original ? Side information Slepian-Wolf coder Slepian-Wolf decoder Coarse approximation or (random) projection [Lin, Varodayan, Girod, ICIP 2007] [Lin, Varodayan, Girod, MMSP 2007]

  32. Image Authentication by Distributed Coding Received Original ? Side information Slepian-Wolf coder Slepian-Wolf decoder Coarse approximation or (random) projection [Lin, Varodayan, Girod, ICIP 2007] [Lin, Varodayan, Girod, MMSP 2007]

  33. Image Authentication by Distributed Coding [Lin, Varodayan, Girod, ICIP 2007]

  34. Minimum Rate for Successful Decoding Experiment: JPEG or JPEG2000 compression + illegimate text banner [Lin, Varodayan, Girod, ICIP 2007]

  35. Demo

  36. Distributed Image/Video Coding:Why Do We Care? • New Paradigm: Chance to Reinvent Compression from Scratch • Entropy coding • Quantization • Signal transforms • Adaptive coding • Rate control • . . . • Powerful New Tool in the Compression Tool-Box • Very low complexity encoders • Compression for networks of cameras • Error-resilient transmission of signal waveforms • Digitally enhanced analog transmission • Unequal error protection without layered coding • Image authentication • Random access • Compression of encrypted signals • . . .

  37. Further interest: B. Girod, A. Aaron, S. Rane, D. Rebollo-Monedero, "Distributed Video Coding," Proceedings of the IEEE, Special Issue on Video Coding and Delivery. January 2005. http://www.stanford.edu/~bgirod/pdfs/DistributedVideoCoding-IEEEProc.pdf

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