Distributed lossy compression for hyperspectral images based on multilevel coset codes

Author:

Xu Ke1,Liu Bin2,Nian Yongjian3,He Mi3,Wan Jianwei1

Affiliation:

1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, P. R. China

2. Department of Medical Information, General Hospital of Jinan Military Area Command, Jinan 250031, P. R. China

3. School of Biomedical Engineering, Third Military Medical University, Chongqing 400038, P. R. China

Abstract

This paper focuses on the problem of lossy compression for hyperspectral images and presents an efficient compression algorithm based on distributed source coding. The proposed algorithm employs a block-based quantizer followed by distributed lossless coding, which is implemented through the use of multilevel coset codes. First, a bitrate allocation algorithm is proposed to assign the rational bitrate for each block. Subsequently, the multilinear regression model is employed to construct the side information of each block, and the optimal quantization step size of each block is obtained under the assigned bitrate while minimizing the distortion. Finally, the quantized version of each block is encoded by distributed lossless compression. Experimental results show that the compression performance of the proposed algorithm is competitive with that of state-of-the-art transform-based compression algorithms. Moreover, the proposed algorithm provides both low encoder complexity and error resilience, making it suitable for onboard compression.

Funder

National Natural Science Foundation of China

Publisher

World Scientific Pub Co Pte Lt

Subject

Applied Mathematics,Information Systems,Signal Processing

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