General Framework of Compressive Sampling and its Applications for Signal and Image Compression A Random Approach

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Authors

  • Prabhat ThakurJaypee University of Information Technology, Waknaghat, Solan (H.P.)
DOI Number
https://doi.org/https://doi.org/10.15415/jtmge.2015.61001
Keywords
Basis Function, Compressive Sampling, Incoherent Signal, l1-norm, Sparse Signal

Abstract

Compressive sampling emerged as a very useful random protocol and has become an active research area for almost a decade. Compressive sampling allows us to sample a signal below Shannon Nyquist rate and assures its successful reconstruction if the signal is sparse. In this paper we used compressive sampling for arbitrary signal and image compression and successfully reconstructed them by solving l1 norm optimization problem. We also showed that compressive sampling can be implemented if signal is sparse and incoherent through simulations.

References

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  • J.Candes (2006) “Compressive sampling,” in Proceedings of the International Congress of Mathematicians, Vol. 3, pp. 1433– 1452.
  • Tropp and A.C. Gilbert (2007) “Signal Recovery from Partial Information Via Orthogonal Matching Pursuit,” IEEE Trans. Inform. Theory, Vol. 53:12, pp. 4655- 4666.
  • Emmanuel Cand`es and Terence Tao (2005) “Decoding by Linear Programming. IEEE Trans. Inform. Theory, Vol 51:12, pp.4203–4215. http://dx.doi.org/10.1109/TIT.2005.858979.
  • Scott Shaobing Chen, David L. Donoho, and Michael A. Saunders (1999) “Atomic Decomposition By Basis Pursuit. SIAM J. Sci Comp., 20:1, pp. 33–61.
  • Candès and J. Romberg (2007) “Sparsity and Incoherence in Compressive Sampling,” Inverse Prob., Vol. 23:3, pp. 969–985.

How to Cite

Prabhat Thakur. General Framework of Compressive Sampling and its Applications for Signal and Image Compression A Random Approach. J.Technol. Manag. Grow. Econ.. 2015, 06, 7-14
General Framework of Compressive Sampling and its Applications for Signal and Image Compression A Random Approach

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