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Distributed lossy compression for hyperspectral images based on multilevel coset codes.

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Bibliographic Details
Title: Distributed lossy compression for hyperspectral images based on multilevel coset codes.
Authors: Xu, Ke1, Liu, Bin2, Nian, Yongjian3, He, Mi3, Wan, Jianwei1
Source: International Journal of Wavelets, Multiresolution & Information Processing. Mar2017, Vol. 15 Issue 2, p-1. 20p.
Subjects: Hyperspectral imaging systems, Multilevel codes, Regression analysis, Mathematical models, Multilinear algebra
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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
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. [ABSTRACT FROM AUTHOR]
ISSN:02196913
DOI:10.1142/S0219691317500126