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The Scientific World Journal
Volume 2014 (2014), Article ID 738735, 8 pages
Research Article

Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

Jin Li,1,2 Fei Xing,1,2 Ting Sun,1,2 and Zheng You1,2

1Department of Precision Instrument, The State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing 100084, China
2Collaborative Innovation Center for Micro/Nano Fabrication, Device and System, China

Received 17 April 2014; Revised 11 June 2014; Accepted 19 June 2014; Published 7 July 2014

Academic Editor: Brajesh K. Kaushik

Copyright © 2014 Jin Li 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.


Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.