1. Distributed Video Coding Bernd Girod, Anne Margot Aaron, Shantanu Rane, and David Rebollo-Monedero IEEE Proceedings 2005

2. Outline Foundations of Distributed Coding Low-Complexity Video Encoding

3. Foundations of Distributed Coding (1) What is distributed coding?  Coding of multiple dependent random sequences with separate encoders sending separate bit-streams to a single decoder.  Based Slepian-Wolf and Wyner-Ziv information-theoretic results 024 135 01234 Encoder Decoder

4. Foundations of Distributed Coding (2) Slepian-Wolf Coder and Wyner-Ziv Coder  X and Y are very similar Lossless coder Side information

5. Foundations of Distributed Coding (3) Given two dependent i.i.d. random sequences X and Y.  R X ≥ H(X), R Y ≥ H(Y) Slepian-Wolf theorem  R X + R Y ≥ H(X, Y)  R X ≥ H(X|Y), R Y ≥ H(Y|X) entropy Joint entropy encodingdecoding X Y X Y

6. Foundations of Distributed Coding (4) Slepian-Wolf coding  Slepian-Wolf coder Encoder: Encoding X without Y Decoder: Reconstructing X with Y  Assumptions X and Y are very similar Y is known at the decoder X Channel coding XP Y P YP Channel decoding X Parity bits Alternative 2: encoderdecoder Slepian-Wolf encoder Slepian-Wolf decoder A BC A BC A BC X A ABC A BC A BC Y Y X encoder decoder Alternative 1:

7. Foundations of Distributed Coding (5) RD Theory for Lossy Compression with Receiver Side Information  Distortion  Wyner-Ziv RD function  in the case of  Gaussian memoryless sources and mean-squared error distortion, or  X is the sum of arbitrarily distributed Y and independent Gaussian noise. Y is known at the encoder Y isn’t known at the encoder

8. Foundations of Distributed Coding (6) Wyner-Ziv Coding  Reconstruct with side information Y.  Assumptions Quantization step size   Three interleaved quantizers: A, B, and C   AA BBCC Y ★ log 2 3 bits AA BBCC X A Y EncoderDecoder (3/2)δ

9. Low-Complexity Video Encoding (1) Conventional video encoder  5-10 times more complex than the decoder  Suitable for the case that video is compressed once and decoded many times Broadcasting or VOD systems Distributed video encoder  Low-complexity encoder, but high-complexity decoder  Suitable for Wireless video sensors for surveillance Wireless PC cameras Mobile camera phones Disposable video cameras

10. Low-Complexity Video Encoding (2) Pixel-Domain and Transform-Domain Encoding   A Laplacian distribution between S and is assumed The Laplacian parameter is estimated from previous decoded frames  Encoding time (Pentimu III 1.2GHz) – pixel-domain encoding Wyner-Ziv: 2.1 ms/frame H.263 I-frame: 36 ms/frame H.263 B-frame: 227 ms/frame Key Frame W-Z Frame W-Z Frame W-Z Frame Key Frame W-Z Frame Key Frame ……

11. Low-Complexity Video Encoding (3) Pixel-Domain and Transform-Domain Encoding

12. Low-Complexity Video Encoding (4) Side information in decoder side  Copying from previous frames, motion-compensated interpolation, multiple frame predictors, …  e.g. Motions estimation at the decoder Additional information is helpful  CRC or some coefficients of the quantized symbol CRC (0,0) CRC (0,1) CRC (0,2) CRC (1,0) CRC (1,1) CRC (1,2) CRC (2,0) CRC (2,1) CRC (2,2) CRC (1,1) previous current encoderdecoder

13. Low-Complexity Video Encoding (4) Rate control  Controlled by the decoder Using a feedback channel Must be performed online useful information can help flexible generation of side information through the feedback channel  Controlled by the encoder Classifying blocks into several modes with different rates  Using the frame difference or block behavior Better side information cannot lower the bit-rate Can be performed offline

14. Low-Complexity Video Encoding (5) Some topics about DVC  How to generate side information?  Spatial domain or frequency domain?  What is the optimal quantizer for DVC?  Rate control in DVC  Robust transmission  …