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Distributed Video Coding for Wireless Visual Sensor Networks

Distributed Video Coding for Wireless Visual Sensor Networks. Outline. Introduction Distributed Source Coding (DSC) Distributed Video Coding (DVC) DVC for Wireless Visual Sensor Networks (WVSN) Concluding Remarks References. Conventional video coding

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Distributed Video Coding for Wireless Visual Sensor Networks

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  1. Distributed Video Codingfor Wireless Visual Sensor Networks

  2. Outline • Introduction • Distributed Source Coding (DSC) • Distributed Video Coding (DVC) • DVCforWireless Visual Sensor Networks (WVSN) • Concluding Remarks • References

  3. Conventional video coding MPEG-1/2/4, H.261, H.263, H.26L, H.264/AVC Interframe predictive coding Encoder is 5-10 times more complex than decoder Suitable for video down-link Interframe Encoder Interframe Decoder Xi Xi’ Introduction X’i-1 [Girod, 2002]

  4. ConventionalVideo Coding [Aramvith]

  5. ConventionalVideo Coding [Lin, NTHU, 2007]

  6. Transformation and Quantization [Lin, NTHU, 2007]

  7. InterframePredictive Video Coding [Lin, NTHU, 2007]

  8. Motion Estimation [Lin, NTHU, 2007]

  9. Motion Estimation [Lin, NTHU, 2007]

  10. Motion Compensated Prediction [Lin, NTHU, 2007]

  11. Applications of ConventionalVideo Coding [Pereira, 2007]

  12. Xi-1’ Interframe Decoder Intraframe Encoder Xi Xi’ Introduction • Problem: low-complexity video encoding for resource-limited video devices • DSC approach: Wyner-Ziv video coding with low-complexityintraframe encoding and possibly high-complexity interframe decoding with side information only available at decoder Side Information [Girod, 2002]

  13. Applications of Low-ComplexityVideo Coding • Wireless video cameras • Wireless low-power surveillance • Mobile document scanner • Video conferencing with mobile devices • Mobile video mail • Disposable video cameras • Wireless Visual Sensor Networks • Networked camcorders • Distributed video streaming • Multiview video entertainment • Wireless capsule endoscopy [Pereira, 2007]

  14. Applications of Low-ComplexityVideo Coding [Pereira, 2007]

  15. Applications of Low-ComplexityVideo Coding [Pereira, 2007]

  16. Wireless Visual Sensor Networks [Akyildiz, 2007, and Pereira, 2007]

  17. Wireless Visual Sensor Networks [Akyildiz, 2002]

  18. Introduction • Requirements of wireless visual sensor networks • low-complexity video encoder • high compression efficiency • Current approaches • distributed video coding (DVC) based on distributed source coding (DSC) • collaborative image coding and transmission • hybrid approach (proposed approach)

  19. Distributed Source Coding (DSC) • Lossless DSC, Slepian and Wolf, 1973 • Lossy DSC, Wyner and Ziv, 1976 • Distributed video coding (DVC) based on DSC • Girod, Stanford University, 2002~ • B. Girod, A. M. Aaron, S. Rane, and D. Rebollo-Monedero, “Distributed video coding,” Proceedings of the IEEE, vol. 93, no. 1, pp. 71-83, Jan. 2005. • Special session on Distributed video coding, 2005 IEEE International Conference on Image Processing (ICIP2005), Italy, Sept. 2005 • Ramchandran, Berkeley, 2002~ • R. Puri, A. Majumdar, and K. Ramchandran, “PRISM: a video coding paradigm with motion estimation at the decoder,” IEEE Trans. on Image Processing, vol. 16, no. 10, pp. 2436-2448, Oct. 2007. • R. Puri, A. Majumdar, P. Ishwar, and K. Ramchandran, “Distributed video coding in wireless sensor networks,” IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 94-106, July 2006.

  20. Distributed Source Coding • DISCOVER (Distributed Coding for Video Services) • 2005~ • F. Pereira, L. Torres, C. Guillemot, T. Ebrahimi, R. Leonardi, and S. Klomp, “Distributed video coding selecting the most promising application scenarios,” to appear in Signal Processing: Image Communication. • C. Guillemot, F. Pereira, L. Torres, T. Ebrahimi. R. Leonardi, J. Ostermann, “Distributed monoview and multiview video coding: basics, problems and recent advances,” IEEE Signal Processing Magazine, special issue on signal processing for multiterminal communication systems, vol. 24, no. 5, pp. 67-76, Sept. 2007. • M. Maitre, C. Guillemot, and L. Morin, “3-D model-based frame interpolation for distributed video coding of static scenes,” IEEE Trans. on Image Processing, vol. 16, no. 5, pp. 1246-1257, May 2007. • Six European major universities: UPC, IST, EPFL, UH, INRIA, UNIBS • Special session on Distributed source coding, 2007 IEEE International Conference on Image Processing (ICIP2007), USA, Sept. 2007 • DISCOVER Workshop on Recent Advances in Distributed Video Coding, Lisbon, Portugal, Nov. 2007 • http://www.discoverdvc.org/

  21. Distributed Source Coding • X、Y in S = {000, 001, 010, 011, 100, 101, 110, 111} • H(X) = H(Y) = 3 • If d(X, Y) ≤ 1, H(X) may be reduced to H(X|Y) = 2 • For example, if Y = 000 and d(X, Y) ≤ 1, the possible X => X in {000, 001, 010, 100} => H(X|Y) = 2 • A possible solution: S can be divided into the four disjoint sets based on d(X, Y) ≤ 1 {000, 111}, {100, 011}, {010, 101}, {001, 110} At the encoder, if X = 100， H(X|Y) = 2 denotes X in {100, 011} At the decoder, X = 100 can be correctly decoded based on Y = 000 and the correlation between X and Y, d(X, Y) ≤ 1 • X: source data to be encoded, Y: the side information of X

  22. Statistically dependent Distributed Source Coding Slepian-Wolf Theorem, 1973 Encoder Statistically dependent Decoder Encoder Wyner-Ziv Theorem, 1976 Encoder Decoder [Girod, 2002]

  23. Separate encodingand decoding of X and Y Separate encodingand joint decoding of X and Y Distributed Source Coding Slepian-Wolf Theorem, 1973 [Girod, 2002]

  24. ConventionalVideo Coding PredictiveInterframe Encoder PredictiveInterframe Decoder X X’ Side Information [Girod, 2006]

  25. “Motion JPEG” Encoder “Motion JPEG” Decoder Side Information Distributed Video Coding based on Wyner-Ziv Theorem Wyner-ZivIntraframe Encoder Wyner-ZivInterframe Decoder X X’ [Girod, 2006]

  26. Wyner-Ziv Video Coding • K: key frame, conventional intraframe encoding • X: Wyner-Ziv frame, Wyner-Ziv video encoding • The corresponding side information Y of X is generated at decoder based on interpolation of the previous decoded frames [Girod, 2003]

  27. Side Information Generation [Ebrahimi, 2006] [Guo, 2006]

  28. Wyner-Ziv Video Coding (a) (b) (a) The original frame (X); (b) the corresponding side information (Y) generated at the decoder. [Girod, 2003]

  29. Slepian-Wolf Codec Turbo Encoder Turbo Decoder Wyner-Ziv Decoder Wyner-Ziv Encoder Reconstruction X Scalar Quantizer X’ Y Wyner-Ziv Video Coding Wyner-Ziv Encoder Wyner-Ziv Decoder Minimum distortion Reconstruction Channel Encoder Channel Decoder Quantizer “Correlation channel” [Girod, 2002]

  30. Pixel-domain Wyner-Ziv Video Coding [Girod, 2003]

  31. Scalar Quantization • Scalar quantization in pixel domain (a) (b) (a) The original frame; (b) the corresponding 16 gray level quantized frame. [Girod, 2003]

  32. L bits in L bits Discarded Systematic Convolutional Encoder Rate Systematic Convolutional Encoder Rate bits bits 2L - n 1 Discarded L bits Interleaverlength L Turbo Encoder • For each input block of n – 1 bits, the turbo encoder produces codewords of length n composed of the actual input bits and one parity bit bits output [Girod, 2002]

  33. Pchannel SISO Decoder Pa posteriori Pa priori Pextrinsic Channel probabilities calculations Channel probabilities calculations bits in bits in L bits out Interleaverlength L Interleaverlength L Decision Pextrinsic Pa priori Deinterleaverlength L Deinterleaverlength L SISO Decoder Pchannel Pa posteriori Turbo Decoder [Girod, 2002]

  34. Simulation Results After Wyner-Ziv decoding Side information 16-level quantization [Girod, 2003]

  35. Simulation Results [Girod, 2003]

  36. DCT DCT bit-plane 2 bit-plane 1 Turbo Encoder Turbo Encoder level Quantizer level Quantizer Extract bit-planes Extract bit-planes Turbo Decoder Turbo Decoder Buffer Buffer Transform-domain Wyner-Ziv Video Coding WZ frames Decoded WZ frames W’ W Intraframe Encoder Interframe Decoder IDCT Xk Xk’ qk qk’ Reconstruction … Request bits bit-plane Mk Side information Yk For each transform band k DCT Y Interpolation/ Extrapolation Interpolation/ Extrapolation Key frames Conventional Intraframe decoding Conventional Intraframe coding K K’ [Girod, 2004]

  37. Transform-domain Wyner-Ziv Video Coding • Each coefficient band is quantized using a scalar quantizer with 2M levels. WZ frame W Mk = number of bit planes for kth coefficient band 4x4 DCT Xk For each transform band k • Combination of quantizers determines the bit allocation across bands. level Quantizer qk Sample quantizers: Values represent number quantization levels for coefficient band [Girod, 2004]

  38. bit-plane 1 bit-plane 2 Transform-domain Wyner-Ziv Video Coding Turbo Encoder Extract bit-planes Turbo Decoder qk qk’ Buffer … Request bits bit-plane Mk Yk • Bit planes of coefficients are encoded independently but decoded successively • Rate-compatible punctured turbo code (RCPT) • Flexibility for varying statistics • Bit rate controlled by decoder through feedback channel • Turbo decoder can perform joint source channel decoding [Girod, 2004]

  39. Simulation Results Side information Wyner-Ziv Coding370 kbps [Girod, 2004]

  40. Simulation Results H263 Intraframe Coding 330 kbps, 32.9 dB Wyner-Ziv Coding274 kbps, 39.0 dB [Girod, 2004]

  41. Simulation Results H263 interframe coding145 kbps, 40.4 dB Wyner-Ziv Coding 156 kbps, 37.5 dB [Girod, 2004]

  42. 3 dB 8 dB Simulation Results [Girod, 2004]

  43. DISCOVER DVC Codec • Based on the feedback channel solution from Stanford Univ. • Based on a split between Wyner-Ziv (WZ) and key frames • Key frames used with a regular (GOP size) or dynamic periodicity • Key frames coded with H.264/AVC Intraframe encoding [Pereira, 2007]

  44. Simulation Results [Pereira, 2007]

  45. Internet or satellite Remote control unit (RCU) Aggregation and forwarding node (AFN) Visual sensor node (VSN) Sensor field Wireless link DVC for Wireless Visual Sensor Networks (WVSN)

  46. ConventionalMultiview Video Coding Multiview video coding structure combining inter-view and temporal prediction [Kubota, 2007]

  47. Global Motion Estimation [Ebrahimi, 2007] [Lin, NTHU, 2007]

  48. Multiview Distributed Video Coding [Ebrahimi, 2006]

  49. Multiview Distributed Video Coding Temporal side information Inter-view side information [Ebrahimi, 2007]

  50. Simulation Results [Ebrahimi, 2007]

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