Li-Wei Kang ( 康立威 ) Institute of Information Science, Academia Sinica Taipei, Taiwan 中央研究院資訊科學研究所 博士後研究員 Feb. 22, 2008 Distributed Video Coding for Wireless Visual Sensor Networks

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 2 Outline Introduction Distributed Source Coding (DSC) Distributed Video Coding (DVC) DVC for Wireless Visual Sensor Networks (WVSN) Concluding Remarks References

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 3 Introduction 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 X’ i-1 Interframe Encoder Interframe Decoder XiXi Xi’Xi’ [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 4 Conventional Video Coding [Aramvith]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 5 Conventional Video Coding [Lin, NTHU, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 6 Transformation and Quantization [Lin, NTHU, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 7 Interframe Predictive Video Coding [Lin, NTHU, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 8 Motion Estimation [Lin, NTHU, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 9 Motion Estimation [Lin, NTHU, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 10 Motion Compensated Prediction [Lin, NTHU, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 11 Applications of Conventional Video Coding [Pereira, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 12 Introduction Interframe Decoder Intraframe Encoder XiXi X i-1 ’Xi’Xi’ Side Information Problem: low-complexity video encoding for resource-limited video devices DSC approach: Wyner-Ziv video coding with low-complexity intraframe encoding and possibly high-complexity interframe decoding with side information only available at decoder [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 13 Applications of Low-Complexity Video 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]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 14 Applications of Low-Complexity Video Coding [Pereira, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 15 Applications of Low-Complexity Video Coding [Pereira, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 16 Wireless Visual Sensor Networks [Akyildiz, 2007, and Pereira, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 17 Wireless Visual Sensor Networks [Akyildiz, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 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)

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 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 , Jan  Special session on Distributed video coding, 2005 IEEE International Conference on Image Processing (ICIP2005), Italy, Sept  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 , Oct  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 , July 2006.

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 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 , Sept  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 , May  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  DISCOVER Workshop on Recent Advances in Distributed Video Coding, Lisbon, Portugal, Nov 

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 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

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 22 Distributed Source Coding Encoder Decoder Statistically dependent Slepian-Wolf Theorem, 1973 Encoder Decoder Wyner-Ziv Theorem, 1976 Statistically dependent [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 23 Distributed Source Coding Separate encoding and decoding of X and Y Separate encoding and joint decoding of X and Y Slepian-Wolf Theorem, 1973 [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 24 Conventional Video Coding Predictive Interframe Decoder Predictive Interframe Encoder X’ Side Information X [Girod, 2006]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 25 Distributed Video Coding based on Wyner-Ziv Theorem “Motion JPEG” Decoder “Motion JPEG” Encoder X’ X Wyner-Ziv Interframe Decoder Wyner-Ziv Intraframe Encoder Side Information [Girod, 2006]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 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]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 27 Side Information Generation [Ebrahimi, 2006] [Guo, 2006]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 28 Wyner-Ziv Video Coding (a) The original frame (X); (b) the corresponding side information (Y) generated at the decoder. (a) (b) [Girod, 2003]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 29 Wyner-Ziv Video Coding Quantizer Channel Encoder Channel Decoder Minimum distortion Reconstruction Wyner-Ziv Decoder Wyner-Ziv Encoder “Correlation channel” Wyner-Ziv Decoder Scalar Quantizer X Wyner-Ziv Encoder Reconstruction X’ Y Turbo Encoder Turbo Decoder Slepian-Wolf Codec [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 30 Pixel-domain Wyner-Ziv Video Coding [Girod, 2003]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 31 Scalar Quantization Scalar quantization in pixel domain (a) The original frame; (b) the corresponding 16 gray level quantized frame. (a) (b) [Girod, 2003]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 32 Turbo Encoder bits output Interleaver length L L bits in L bits Systematic Convolutional Encoder Rate bits Discarded Systematic Convolutional Encoder Rate L bits Discarded bits 1  n 2L 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 [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 33 Turbo Decoder Interleaver length L L bits out Channel probabilities calculations bits in Channel probabilities calculations bits in SISO Decoder P channel P extrinsic P a priori Interleaver length L Deinterleaver length L SISO Decoder P channel P extrinsic P a priori Deinterleaver length L Decision P a posteriori [Girod, 2002]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 34 Simulation Results Side information After Wyner-Ziv decoding 16-level quantization [Girod, 2003]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 35 Simulation Results [Girod, 2003]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 36 Transform-domain Wyner-Ziv Video Coding WZ frames W Request bits Interpolation/ Extrapolation Reconstruction Key frames K Conventional Intraframe coding Conventional Intraframe decoding DCT For each transform band k K’ W’ Y YkYk XkXk Xk’Xk’ IDCT Decoded WZ frames level Quantizer DCT Turbo Encoder Buffer Turbo Decoder Extract bit- planes qkqk bit-plane 1 bit-plane 2 bit-plane M k … qk’qk’ Interframe Decoder Intraframe Encoder level Quantizer DCT Turbo Encoder Buffer Turbo Decoder Extract bit- planes Interpolation/ Extrapolation Side information [Girod, 2004]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 37 Each coefficient band is quantized using a scalar quantizer with 2 M levels. Transform-domain Wyner-Ziv Video Coding level Quantizer WZ frame W 4x4 DCT XkXk qkqk For each transform band k Combination of quantizers determines the bit allocation across bands. M k = number of bit planes for k th coefficient band Sample quantizers: Values represent number quantization levels for coefficient band [Girod, 2004]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 38 Transform-domain Wyner-Ziv Video Coding Turbo Encoder Buffer Turbo Decoder Request bits Extract bit- planes bit-plane 1 bit-plane 2 bit-plane M k … qk’qk’ qkqk YkYk 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]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 39 Simulation Results Side informationWyner-Ziv Coding 370 kbps [Girod, 2004]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 40 Simulation Results H263 Intraframe Coding 330 kbps, 32.9 dB Wyner-Ziv Coding 274 kbps, 39.0 dB [Girod, 2004]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 41 Simulation Results H263 interframe coding 145 kbps, 40.4 dB Wyner-Ziv Coding 156 kbps, 37.5 dB [Girod, 2004]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 42 Simulation Results [Girod, 2004] 3 dB 8 dB

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 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]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 44 Simulation Results [Pereira, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 45 DVC for Wireless Visual Sensor Networks (WVSN) Internet or satellite Remote control unit (RCU) Visual sensor node (VSN) Aggregation and forwarding node (AFN) Sensor field Wireless link

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 46 Conventional Multiview Video Coding [Kubota, 2007] Multiview video coding structure combining inter-view and temporal prediction

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 47 Global Motion Estimation [ Lin, NTHU, 2007 ] [Ebrahimi, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 48 Multiview Distributed Video Coding [Ebrahimi, 2006]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 49 Multiview Distributed Video Coding Temporal side information Inter-view side information [Ebrahimi, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 50 Simulation Results [Ebrahimi, 2007]

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 51 Collaborative Image Coding and Transmission [1] M. Wu and C. W. Chen, “Collaborative image coding and transmission over Wireless Sensor Networks,” EURASIP Journal on Advances in Signal Processing, special issue on Visual Sensor Networks, [2] K. Y. Chow, K. S. Lui, and E. Y. Lam, “Efficient on-demand image transmission in visual sensor networks,” EURASIP Journal on Advances in Signal Processing, special issue on Visual Sensor Networks, 2007.

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 52 Proposed Multiview DVC The proposed low-complexity video codec is based on  the motion estimation is shifted to the decoder  the low-complexity image matching is performed at the encoder based on image warping and robust media hashing L. W. Kang and C. S. Lu, “Low-complexity power-scalable multi-view distributed video encoder,” in Proc. of 2007 Picture Coding Symposium, Lisbon, Portugal, Nov L. W. Kang and C. S. Lu, “Multi-view distributed video coding with low-complexity inter-sensor communication over wireless video sensor networks,” in Proc. of 2007 IEEE Int. Conf. on Image Processing, special session on Distributed source coding II: Distributed video and image coding and their applications, San Antonio, TX, USA, Sept. 2007, vol. 3, pp (invited paper). L. W. Kang and C. S. Lu, “Low-complexity Wyner-Ziv video coding based on robust media hashing,” in Proc. of IEEE Int. Workshop on Multimedia Signal Processing, Victoria, BC, Canada, Oct. 2006, pp P.S. Co-author: Prof. Chun-Shien Lu ( 呂俊賢 教授, 中研院資訊所副研究員 )

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 53 Robust Media Hashing A compact representation for a frame

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 54 Robust Media Hashing A parent and its four child nodes. Only the parent-child pair with the maximum magnitude difference (Diff) among those of the four pairs in a “parent-four children” pair will be selected p C4C4 C3C3 C2C2 C1C1 The wavelet decomposition for a frame. c1 c2c1 c2 c 3 c 4 c1 c2c1 c2 p Structural digital signature (SDS) C. S. Lu and H. Y. M. Liao, “Structural digital signature for image authentication: an incidental distortion resistant scheme,” IEEE Trans. on Multimedia, vol. 5, no. 2, pp , June 2003.

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 55 Robust Media Hashing Labeling an SDS  the signature symbol sym(p,c) of a parent-child pair (p, c) can be defined as follows  each parent-four children pair will be represented by a symbol sym(p,c), where the pair (p, c) is with maximum magnitude difference

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 56 An illustrated example for encoding with GOP = 4 Proposed Single-view DVC L. W. Kang and C. S. Lu, “Low-complexity Wyner-Ziv video coding based on robust media hashing,” in Proc. of 2006 IEEE Int. Workshop on Multimedia Signal Processing, Victoria, BC, Canada, Oct. 2006, pp (MMSP2006).

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 57 Consider several adjacent VSNs observing the same target scene in a WVSN For each VSN, V s, an input video sequence is divided into several GOPs, in which a GOP consists of a key frame, K s,t, followed by several non-key frames, W s,t Proposed Multiview DVC

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 58 Key Frame Encoding Key frames  each key frame is encoded using the H.264/AVC intra- frame encoder first  The global motion estimation between the key frames from adjacent VSNs will be performed at the decoder (RCU)  The estimated motion parameters between each pair of the key frames from adjacent VSNs will be sent back to the corresponding VSNs via feedback channel

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 59 Global Motion Estimation between the Key Frames from Adjacent VSNs

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 60 Key Frame Encoding Target scene V0V0 V1V1 K’ 0,48 K’ 1,48 VkVk Warping (a) Co-located block MSE calculation and comparison (b) Block-based SDS extraction and comparison (c) Significant wavelet coefficients extraction Ќ 0,48 Quantization and entropy encoding Compressed bitstream for K 1,48 Significant wavelet coefficients for K 1,48

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 61 Non-key Frame Encoding Based on hash comparisons Block coding mode selection (Intra, Inter, or Skip)  for each frame, all the blocks are sorted in an increasing order based on their PSNR values (calculated with their co- located blocks in the reference frame from the same VSN) B (1) B (2) B (i) B (i+1) B (i+2) B (j) B (j+1) B (k) PSNR (1) ≤ PSNR (2) ≤ ≤ PSNR (i+1) ≤ ≤ PSNR (k) T1T1 T2T2 Blocks with Intra mode (H.264/AVC intra-frame encoding) Blocks with Inter mode (SDS extraction and comparison) Blocks with Skip mode

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 62 Non-key Frame Encoding for Blocks with Inter Mode

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 63 Simulation Results

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 64 Concluding Remarks Low-complexity video coding becomes a very hot research topic Distributed video coding (DVC) based on distributed source coding (DSC) becomes a new paradigm of low-complexity video coding Further researches  side information generation  transformation and quantization  channel coding  rate control  Other DSC-related applications  multimedia authentication  biometrics security  layered video coding  Error resilience for standard video coding  other low-complexity video coding architectures

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 65 References [1] 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. [2] 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, vol. 24, no. 5, pp , Sept [3] 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 , May [4] 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 , Oct [5] 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 , July [6] B. Girod, A. M. Aaron, S. Rane, and D. Rebollo-Monedero, “Distributed video coding,” Proceedings of the IEEE, vol. 93, no. 1, pp , Jan [7] X. Artigas, J. Ascenso, M. Dalai, S. Klomp, D. Kubasov, and M. Ouaret, “The DISCOVER codec: architecture, techniques and evaluation,” in Proc. of 2007 Picture Coding Symposium, Lisbon, Portugal, Nov

Distributed Video Coding for Wireless Visual Sensor Networks Feb. 22, 2008 at CSIE/NDHU 66 Our Preliminary Publications [1] L. W. Kang and C. S. Lu, “Low-complexity power-scalable multi-view distributed video encoder,” in Proc. of Picture Coding Symposium, Lisbon, Portugal, Nov (PCS2007). [2] L. W. Kang and C. S. Lu, “Multi-view distributed video coding with low-complexity inter-sensor communication over wireless video sensor networks,” in Proc. of IEEE Int. Conf. on Image Processing, special session on Distributed Source Coding II: Distributed Image and Video Coding and Their Applications, San Antonio, TX, USA, Sept. 2007, vol. 3, pp (ICIP2007, invited paper). [3] L. W. Kang and C. S. Lu, “Low-complexity Wyner-Ziv video coding based on robust media hashing,” in Proc. of IEEE Int. Workshop on Multimedia Signal Processing, Victoria, BC, Canada, Oct. 2006, pp (MMSP2006). [4] L. W. Kang and C. S. Lu, “Wyner-Ziv video coding with coding mode-aided motion compensation,” in Proc. of IEEE Int. Conf. on Image Processing, Atlanta, GA, USA, Oct. 2006, pp (ICIP2006).