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Appl. Sci. 2014, 4(2), 128-147; doi:10.3390/app4020128

Compressed Sensing-Based Distributed Image Compression

School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand
School of Engineering and Advanced Technology, Massey University, Albany, Auckland, New Zealand
Asia-Pacific Broadcasting Union, Angkasapuri, Kuala Lumpur 50614, Malaysia
Author to whom correspondence should be addressed.
Received: 7 October 2013 / Revised: 21 January 2014 / Accepted: 28 February 2014 / Published: 31 March 2014
(This article belongs to the Special Issue Digital Signal Processing and Engineering Applications)
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In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods. View Full-Text
Keywords: distributed image coding; compressed sensing distributed image coding; compressed sensing

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Baig, M.Y.; Lai, E.M.-K.; Punchihewa, A. Compressed Sensing-Based Distributed Image Compression. Appl. Sci. 2014, 4, 128-147.

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