1 / 14

Distributed Video Coding

Distributed Video Coding. Bernd Girod, Anne Margot Aaron, Shantanu Rane, and David Rebollo-Monedero. IEEE Proceedings 2005. Outline. Foundations of Distributed Coding Low-Complexity Video Encoding. Foundations of Distributed Coding (1). What is distributed coding?

rangle
Télécharger la présentation

Distributed Video Coding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 1 3 5 Encoder Decoder Encoder 0 1 2 3 4 0 2 4

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

  5. Foundations of Distributed Coding (3) • Given two dependent i.i.d. random sequences X and Y. • RX≥ H(X), RY ≥ H(Y) • Slepian-Wolf theorem • RX +RY ≥ H(X, Y) • RX≥ H(X|Y), RY ≥ H(Y|X) entropy Joint entropy X X Y Y encoding decoding

  6. Foundations of Distributed Coding (4) Slepian-Wolf encoder Slepian-Wolf decoder • 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 encoder decoder A X Y B C B C B C X A A A Y Alternative 2: encoder decoder A B C B C B C A A P Alternative 1: Channel coding Channel decoding X XP YP X Parity bits Y

  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. Yisn’tknown at the encoder Y is 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 ★log23 bits Encoder Decoder X Y A  (3/2)δ (3/2)δ Y B C B C B C B C A A A A

  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 Key Frame W-Z Frame W-Z Frame Key Frame … …

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

  12. 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) 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 previous current CRC (1,1) encoder decoder

  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 • …

More Related