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Mathematical Problems in Engineering
Volume 2013, Article ID 825673, 7 pages
http://dx.doi.org/10.1155/2013/825673
Research Article

Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China
2School of Biomedical Engineering, Third Military Medical University and Chongqing University, Chongqing 400038, China

Received 17 July 2013; Revised 20 August 2013; Accepted 20 August 2013

Academic Editor: Gelan Yang

Copyright © 2013 Yongjian Nian et al. 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

A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC) is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.