A Survey on Distributed Compressed Video Sensing
Abstract: This electronic distributed video coding is a new paradigm for video compression. Compared to conventional video coding standards in which the video sequence is coded jointly and decoded jointly, distributed video coding sys-tem codes the video sequence separately for two or more sources that are independent identically distributed and de-codes jointly with the statistical correlation between different sources, then the coder becomes as simple as possible, so as to solve the problems of the limited video terminal. On the other hand, an emerging signal acquisition technology (Compressive Sensing, CS) provides a new way for the signal sampling, signal compression reconstruction based on the sparstiy of signal, random measurement matrix and nonlinear optimization algorithm. It broke through the limitations of traditional Nyquist sampling theorem, which has been applicable to directly capture compressed image data efficiently. Combination of distributed video coding and CS (Distributed Compressed Video Sensing, DCVS) results in more low-complexity and low-cost for video coding. This paper reviews the theory of distributed video coding, classic schemes involved, as well as theoretical knowledge of compressed sensing, and development status of distributed com-pressed video sensing at this stage. Finally we present some problems and the probably corresponding solutions, then discuss its possible applications in the future prospects.
文章引用: 解晨 , 龚声蓉 (2013) 分布式压缩视频感知综述。 图像与信号处理， 2， 8-18. doi: 10.12677/JISP.2013.21002
 D. L. Donoho. Compressed sensing. IEEE Transactions on Infor- mation Theory, 2006, 52(4): 1289-1306.
 E. Candès, J. Romberg and T. Tao. Robust uncertainty princi- ples: Exact signal reconstruction from highly incomplete frequ- ency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
 E. Candès. Compressive sampling. Proceedings of International Congress of Mathematicians, Madrid: European Mathematical Society Publishing House, 2006: 1433-1452.
 J. D. Slepian, J. K. Wolf. Noiseless coding of correlated infor- mation sources. IEEE Transactions on Information Theory, 1973, 219(4): 471-480.
 A. Wyner, J. Ziv. The rate distortion function for source coding with side information at the decoder. IEEE Transactions on In- formation Theory, 1976, 22(1): 1-10.
 A. Aaron, R. Zhang and B. Girod. Wyner-Ziv coding of motion video. Conference Record of the Asilomar Conference on Sig- nals, Systems and Computers, Pacific Grove, 2007: 240-244.
 A. Aaron, E. Setton and B. Girod. Toward practical Wyner-Ziv coding of video. International Conference on Image Processing, Barcelona, 2003: 869-872.
 A. Aaron, S. Rane and R. Zhang. Wyner-Ziv coding for video: Applications to compression and error resilience. Proceedings of the IEEE Data Compression Conference, Snowbird, 2003: 93- 102.
 A. Aaron, S. Rane and B. Girod. Wyner-Ziv video coding with hash based motion compensation at the receiver. The Interna- tional Conference on Image Processing, Singapore, 2004, 2: 3097- 3100.
 T. T. Do, Y. Chen, D. T. Nguyen and N. Nguyen. Distributed compressed video sensing. Proceedings of the IEEE Interna- tional Conference on Image, Baltimore, 2009: 1393-1396.
 J. Prades-Nebot, Y. Ma and T. Huang. Distributed video coding using compressive sampling. Proceedings of the Picture Coding Symposium, Chicago, 2009: 1-4.
 L. Kang, C. Lu. Distributed compressive video sensing. IEEE International Conference on Acoustics, Speech, and Signal Proc- essing, Taipei, 2009: 1169-1172.
 H. W. Chen, L. W. Kang and C. S. Lu. Dictionary learning-based distributed compressive video sensing. Proceedings of the Pic- ture Coding Symposium, Nagoya, 2010: 210-213.
 14M. Aharon, M. Elad and A. M. Bruckstein. The K-SVD: An algo- rithm for designing of overcomplete dictionaries for sparse rep- resentations. IEEE Transactions on Image Processing, 2006, 54 (11): 4311-4322.
 15S. J. Wright, R. D. Nowak and M. A. T. Figueiredo. Sparse re- construction by separable approximation. IEEE Transactions on Signal Processing, 2009, 57(7): 2479-2493.
 H.-W. Chen, L.-W. Kang and C.-S. Lu. Dynamic measurement rate allocation for distributed compressive video sensing. Pro- ceedings of the SPIE—The International Society for Optical En- gineering, Bellingham, 2010.
 X. Wang, H. Fang, X. Zhu, B. Li and Y. Liu. Sparse filter corre- lation model based joint reconstruction in distributed compres- sive video sensing. IEEE International Conference on Network Infrastructure and Digital Content, Beijing, 2010: 483-487.
 C. Ma, Y. Liu, L. Zhang and X. Q. Zhu. Distributed compressive video sensing based on smoothed .0 norm with partially known support. IEEE International Conference on Multimedia and Expo, 2011: 11-15.
 H.-Y. Tseng, Y.-C. Shen. Distributed video coding with com- pressive measurements. MM’11 Proceedings of the 19th ACM International Conference on Multimedia. New York, 2011: 1273- 1276.
 D. Baron, M. B. Wakin and M. Duarte. Distributed compressed sensing. http://www.dsp.rice.edu/~drorb/pdf/DCS112005.pdf
 B. A. Olshausen, D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Na- ture, 1996, 381(6583): 607-609.
 S. Mallat. A wavelet tour of signal processing. San Diego: Aca- demic Press, 1996.
 E. Candès, D. Donoho. Curvelets: A surprisingly effective non- adaptive representation for objects with edges. Technical Report 1999-28, Department of Statistics, Stanford: Stanford University, 1999.
 M. Wakin, J. Laska, M. Duarte and D. Baron. Compressive ima- ging for video representation and coding. Proceedings of Picture Coding Symposium, Beijing, 2006.
 K. Skretting, K. Engan. Recursive least squares dictionary learn- ing algorithm. IEEE Transactions on Signal Processing, 2010, 58(4): 2121-2130.
 H. Zayyani, M. Babaie-Zadeh. Thresholded smoothed-L0 (SL0) dictionary learning for sparse representations. IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing, Taipei, 2009: 1825-1828.
 M. Elad, M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745.
 J. Mairal, F. Bach and J. Ponce. Online dictionary learning for sparse coding. ICML '09 Proceedings of the 26th Annual Inter- national Conference on Machine Learning, New York, 2009: 689- 696.
 E. Candes, J. Romberg. Robust signal recovery from incomplete observation. IEEE International Conference on Image Procession, Atlanta, 2006: 1281-1284.
 Y. F. Zhang, S. L. Mei. A novel image/video coding method based on compressed sensing theory. IEEE International Confer- ence on Acoustics, Speech and Signal Processing, Las Vegas, 2008: 1361-1364.
 E. Candès, T. Tao. Decoding by linear programming. IEEE Trans- actions on Information Theory, 2005, 51(12): 4203-4215.
 E. Candès, T. Tao. Near optimal signal recovery from random- projections: Universal encoding strategies? IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425.
 T. T. Do, T. D. Trany and L. Gan. Fast compressive sampling with structurally random matrices. Proceedings of the IEEE In- ternational Conference on Acoustics, Speech and Signal Proc- essing, Washington DC, 2008: 3369-3372.
 L. Gan, T. T. Do and T. D. Trany. Fast compressive imaging us- ing scrambled block hadamard ensemble. European Signal Pro- cessing Conference, 2008.
 G. Lu. Block compressed sensing of natural images. Interna- tional Conference on Digital Signal Processing, Cardiff, 2007: 403-406.
 H. Lee, H. Oh and S. Lee. A new block compreesive sensing to control the number of measurements. IEEE International Confe- rence on Image Processing, Brussels, 2011: 2713-2716.
 J. Zheng, E. L. Jacobs. Video compressive sensing using spatial domain sparstiy. Optical Engineering, the International Society for Optical Engineering, 2009, 48(8): 087006.
 Z. L. Wang, I. Lee. A study of video coding by reusing compres- sive sensing measurements. Proceedings of the 7th International Conference on Ubiquitous Intelligence & Computing and Auto- nomic & Trusted Computing, Xi’an, 2010: 64-69.
 J. Xu, J. W. Ma. Compressive video sensing based on user atten- tion model. The 28th Picture Coding Symposium, Nagoya, 2010: 90-93.
 J. E. Fowler, S. Mun. Multiscale block compresed sensing with smoothed projected landweber reconstruction. Proceedings of the 19th European Signal Processing Conference. Barcelona, 2011: 564-568.
 Z. R. Liu, V. Zhao. Block-based adaptive compressed sensing for video. IEEE of the 17th International Conference on Image Processing, Hong Kong, 2010: 1649-1652.
 A. Masomeh, A. Ali. Compressed video sensing using adaptive sampling rate. The 5th International Symposium on Telecommu- nication, Tehran, 2010: 710-714.
 M. A. T. Fiqueiredo, R. D. Nowak and S. J. Wright. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
 S. Dekel. Adaptive compressed image sensing based on wave- let-trees. http://dsp.rice.edu/files/cs/adaptiveCSimag.pdf
 C. La, M. N. Do. Signal reconstruction using sparse tree repre- sentation. Proceedings of the International Society for Optical Engineering, San Diego, 2005, 5914: 273-283.
 S. S. Chen, D. L. Donoho and M. A. Saunder. Atomic decompo- sition by basis pursuit. SIAM Review, 2001, 43(1): 129-159.
 R. Neff, A. Zakhor. Very low rate video coding based on match- ing pursuits. IEEE Transactions on Circuits and Systems for Vi- deo Technology, 1997, 7(1): 158-171.
 J. A. Tropp, A. C. Gilber. Signal recovery from patial Informa- tion by orthogonal matching pursuit. 2005. www-personal.umich.edu/_Jtropp/papers/TG05-Signal-recovery.pdf
 Y. F. Zhang, Sh. L. Mei. A multiple description image/video co- ding method by compressed sensing theory. IEEE International Symposium on Circuits and Systems, Seattle, 2008: 1830-1833.
 S. Y. Xiang, L. Cai. Scalable video coding with compressive sensing for wireless videocast. IEEE International Conference on Communications, Kyoto, 2011: 1-5.
 H. Jiang, C. B. Li. Scalable video coding using compressive sensing. Bell Labs Technical Journal, 2012, 16(4): 149-169.
 M. Mashud, K. Mahata. A scalable distributed video coder using compressed sensing. Annual IEEE on India Conference, Gujarat, 2009: 1-4.
 N. Imran, B.-C. Seet and A. C. M. Fong. A comprarative analy- sis of video codecs for multihop wireless video sensor networks. MultiMedia Systems, 2012, 18(5): 373-389.