Back to Current Issues

An Image representation using Compressive Sensing and Arithmetic Coding

Dr. Renuka Devi S M , ,

ECE Dept, GNITS, Hyderbad-500008

The demand for graphics and multimedia communication over intenet is growing day by day. Generally the coding efficiency achieved by CS measurements is below the widely used wavelet coding schemes (e.g., JPEG 2000). In the existing wavelet-based CS schemes, DWT is mainly applied for sparse representation and the correlation of DWT coefficients has not been fully exploited yet. To improve the coding efficiency, the statistics of DWT coefficients has been investigated. A novel CS-based image representation scheme has been proposed by considering the intra- and inter-similarity among DWT coefficients. Multi-scale DWT is first applied. The low- and high-frequency subbands of Multi-scale DWT are coded separately due to the fact that scaling coefficients capture most of the image energy. At the decoder side, two different recovery algorithms have been presented to exploit the correlation of scaling and wavelet coefficients well. In essence, the proposed CS-based coding method can be viewed as a hybrid compressed sensing schemes which gives better coding efficiency compared to other CS based coding methods.

Dr. Renuka Devi S M ," An Image representation using Compressive Sensing and Arithmetic Coding”, International Journal of Computer Engineering In Research Trends, 3(11):573-579,November-2016.

Keywords : Compressive sensing, Discrete wavelet tansform, Tree Structured wavelet CS, Basis Pursuit

[1]	Donoho D.L., “Compressed sensing,” IEEE Transac-tions on Information Theory, vol. 52, pp. 1289–1306, 2006.
[2]	Cand`es E. J.  and Wakin M. B. , “An introduction to compressive sampling,” IEEE Signal Processing Magazine, vol. 25, pp. 21–30, 2008.
[3]	Tropp, Joel A., and Stephen J. Wright. "Computational methods for sparse solution of linear inverse problems." Proceedings of the IEEE 98.6 (2010): 948-958. 
[4]	Candès E., Romberg J. and Tao T., “Robust uncertainty principles: Exact signal   reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory, vol. 52,no. 2, pp. 489–509, Feb. 2006.
[5]	J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pur-suit,” IEEE Transactions on Information Theory, vol. 53, pp. 4655–4666, 2007.
[6]	D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, “Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit,” March 2006, preprint.
[7]	B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Annals of Statistics (with discussion), vol. 32,pp. 407–499, 2004.
[8]	[Online]. Available:
[9]	Deng C.W., Lin W. S., Lee B. S. and Lau C. T., “Robust image compression based upon compressive sensing,” in Proc. IEEE Int. Conf. Multimedia and Expo. (IC-ME’10), Jul. 2010, pp. 462–467.
[10]	Ji S. , Xue Y. , and  Carin L., “Bayesian compressive sensing,” IEEE Transactions on Signal Processing, vol. 56, 2008,  pp. 2346–2356.
[11]	Said A. and Pearlman W. A., “A new, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, 1996,  pp. 243–250.
[12]	Pavithra V, Renuka Devi SMand Ganapathy Reddy Ch , "A survey of robust image coding techniques", IJCA (0975-8887),Volume No 71, No 5,May 2013,pp. 41-51
[13]	Pavithra V, Renuka Devi SM ‘An image representa-tion scheme by hybrid compressive sensing’ IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia),   19-21 December 2013

DOI Link :

Download :

Refbacks : There are currently no refbacks

Quick Links


Science Central

Score: 13.30

Submit your paper to