Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding. (30th September 2013)

• Record Type: Journal Article
• Authors: Nian, Yongjian; He, Mi; Wan, Jianwei
• Other Names: Yang Gelan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-09-30
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/825673
• Languages: English
• ISSNs: 1024-123X

Abstract

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.