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

Compressed Sensing-Based Distributed Image Compression

Received: 7 October 2013; in revised form: 21 January 2014 / Accepted: 28 February 2014 / Published: 31 March 2014
(This article belongs to the Special Issue Digital Signal Processing and Engineering Applications)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: distributed image coding; compressed sensing
PDF Full-text Download PDF Full-Text [3448 KB, uploaded 31 March 2014 08:29 CEST]

Export to BibTeX |
EndNote


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.

AMA Style

Baig MY, Lai EM-K, Punchihewa A. Compressed Sensing-Based Distributed Image Compression. Applied Sciences. 2014; 4(2):128-147.

Chicago/Turabian Style

Baig, Muhammad Y.; Lai, Edmund M.-K.; Punchihewa, Amal. 2014. "Compressed Sensing-Based Distributed Image Compression." Appl. Sci. 4, no. 2: 128-147.

Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert