Compressed Sensing based adaptive video coding for resource constrained devices

Compressed Sensing(CS) based video coding techniques are low-complexity in general, but with marginal compression results. Also, wireless multimedia devices have serious resource constraints, with fluctuations in bandwidth(for up-link traffic) and available power. In this paper we propose a nested technique in which CS data is further compressed within the CS domain to give far better compression results. Our technique is block based and Sum of Absolute Difference(SAD) values are used to estimate the amount of motion in each Macro Block(MB). The MB's are suppressed in subsequent frames according to the estimated motion. Later in the paper, we propose an adaptive compression mechanism, which adjusts the amount of compression according to fluctuations in resources.

[1]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[2]  Justin K. Romberg,et al.  Low-complexity video compression and compressive sensing , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[3]  Joohee Kim,et al.  Quad-tree partitioned compressed sensing for depth map coding , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Mike E. Davies,et al.  Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.

[5]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[6]  Rui Sun,et al.  The application of improved Hadamard measurement matrix in compressed sensing , 2012, 2012 International Conference on Systems and Informatics (ICSAI2012).

[7]  Abdulhakem Y. Elezzabi,et al.  Maximum Frame Rate Video Acquisition Using Adaptive Compressed Sensing , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  James E. Fowler,et al.  Residual Reconstruction for Block-Based Compressed Sensing of Video , 2011, 2011 Data Compression Conference.

[9]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[10]  Trac D. Tran,et al.  Distributed Compressed Video Sensing , 2009, 2009 43rd Annual Conference on Information Sciences and Systems.

[11]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[12]  Lu Gan.  Block Compressed Sensing of Natural Images , 2007, 2007 15th International Conference on Digital Signal Processing.

[13]  E. Candès.  The restricted isometry property and its implications for compressed sensing , 2008 .

[14]  Trac D. Tran,et al.  Fast compressive imaging using scrambled block Hadamard ensemble , 2008, 2008 16th European Signal Processing Conference.

[15]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..

[16]  Emmanuel J. Candès,et al.  Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..

[17]  E.J. Candes.  Compressive Sampling , 2022 .

[18]  P. Indyk,et al.  Near-Optimal Sparse Recovery in the L1 Norm , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[19]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[20]  Ming Li,et al.  Motion-Aware Decoding of Compressed-Sensed Video , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Joel Solé,et al.  Compressive sensing with adaptive pixel domain reconstruction for block-based video coding , 2010, 2010 IEEE International Conference on Image Processing.

[22]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[23]  Lei Liu,et al.  Adaptive Distributed Compressed Video Sensing , 2014, J. Inf. Hiding Multim. Signal Process..

[24]  Mike E. Davies,et al.  Gradient Pursuits , 2008, IEEE Transactions on Signal Processing.

[25]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[26]  Abdulhakem Y. Elezzabi,et al.  Block-based adaptive compressed sensing for video , 2010, 2010 IEEE International Conference on Image Processing.

[27]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .