|
[1]
|
D. L. Donoho. Compressed sensing. IEEE Transactions on Infor- mation Theory, 2006, 52(4): 1289-1306.
|
|
[2]
|
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.
|
|
[3]
|
E. Candès. Compressive sampling. Proceedings of International Congress of Mathematicians, Madrid: European Mathematical Society Publishing House, 2006: 1433-1452.
|
|
[4]
|
J. D. Slepian, J. K. Wolf. Noiseless coding of correlated infor- mation sources. IEEE Transactions on Information Theory, 1973, 219(4): 471-480.
|
|
[5]
|
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.
|
|
[6]
|
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.
|
|
[7]
|
A. Aaron, E. Setton and B. Girod. Toward practical Wyner-Ziv coding of video. International Conference on Image Processing, Barcelona, 2003: 869-872.
|
|
[8]
|
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.
|
|
[9]
|
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.
|
|
[10]
|
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.
|
|
[11]
|
J. Prades-Nebot, Y. Ma and T. Huang. Distributed video coding using compressive sampling. Proceedings of the Picture Coding Symposium, Chicago, 2009: 1-4.
|
|
[12]
|
L. Kang, C. Lu. Distributed compressive video sensing. IEEE International Conference on Acoustics, Speech, and Signal Proc- essing, Taipei, 2009: 1169-1172.
|
|
[13]
|
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.
|
|
[14]
|
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.
|
|
[15]
|
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.
|
|
[16]
|
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.
|
|
[17]
|
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.
|
|
[18]
|
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.
|
|
[19]
|
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.
|
|
[20]
|
D. Baron, M. B. Wakin and M. Duarte. Distributed compressed sensing. http://www.dsp.rice.edu/~drorb/pdf/DCS112005.pdf
|
|
[21]
|
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.
|
|
[22]
|
S. Mallat. A wavelet tour of signal processing. San Diego: Aca- demic Press, 1996.
|
|
[23]
|
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.
|
|
[24]
|
M. Wakin, J. Laska, M. Duarte and D. Baron. Compressive ima- ging for video representation and coding. Proceedings of Picture Coding Symposium, Beijing, 2006.
|
|
[25]
|
K. Skretting, K. Engan. Recursive least squares dictionary learn- ing algorithm. IEEE Transactions on Signal Processing, 2010, 58(4): 2121-2130.
|
|
[26]
|
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.
|
|
[27]
|
M. Elad, M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745.
|
|
[28]
|
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.
|
|
[29]
|
E. Candes, J. Romberg. Robust signal recovery from incomplete observation. IEEE International Conference on Image Procession, Atlanta, 2006: 1281-1284.
|
|
[30]
|
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.
|
|
[31]
|
E. Candès, T. Tao. Decoding by linear programming. IEEE Trans- actions on Information Theory, 2005, 51(12): 4203-4215.
|
|
[32]
|
E. Candès, T. Tao. Near optimal signal recovery from random- projections: Universal encoding strategies? IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425.
|
|
[33]
|
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.
|
|
[34]
|
L. Gan, T. T. Do and T. D. Trany. Fast compressive imaging us- ing scrambled block hadamard ensemble. European Signal Pro- cessing Conference, 2008.
|
|
[35]
|
G. Lu. Block compressed sensing of natural images. Interna- tional Conference on Digital Signal Processing, Cardiff, 2007: 403-406.
|
|
[36]
|
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.
|
|
[37]
|
J. Zheng, E. L. Jacobs. Video compressive sensing using spatial domain sparstiy. Optical Engineering, the International Society for Optical Engineering, 2009, 48(8): 087006.
|
|
[38]
|
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.
|
|
[39]
|
J. Xu, J. W. Ma. Compressive video sensing based on user atten- tion model. The 28th Picture Coding Symposium, Nagoya, 2010: 90-93.
|
|
[40]
|
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.
|
|
[41]
|
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.
|
|
[42]
|
A. Masomeh, A. Ali. Compressed video sensing using adaptive sampling rate. The 5th International Symposium on Telecommu- nication, Tehran, 2010: 710-714.
|
|
[43]
|
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.
|
|
[44]
|
S. Dekel. Adaptive compressed image sensing based on wave- let-trees. http://dsp.rice.edu/files/cs/adaptiveCSimag.pdf
|
|
[45]
|
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.
|
|
[46]
|
S. S. Chen, D. L. Donoho and M. A. Saunder. Atomic decompo- sition by basis pursuit. SIAM Review, 2001, 43(1): 129-159.
|
|
[47]
|
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.
|
|
[48]
|
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
|
|
[49]
|
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.
|
|
[50]
|
S. Y. Xiang, L. Cai. Scalable video coding with compressive sensing for wireless videocast. IEEE International Conference on Communications, Kyoto, 2011: 1-5.
|
|
[51]
|
H. Jiang, C. B. Li. Scalable video coding using compressive sensing. Bell Labs Technical Journal, 2012, 16(4): 149-169.
|
|
[52]
|
M. Mashud, K. Mahata. A scalable distributed video coder using compressed sensing. Annual IEEE on India Conference, Gujarat, 2009: 1-4.
|
|
[53]
|
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.
|