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Proceedings Paper

Lossless compression of hyperspectral images based on the prediction error block
Author(s): Yongjun Li; Yunsong Li; Juan Song; Weijia Liu; Jiaojiao Li
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Paper Abstract

A lossless compression algorithm of hyperspectral image based on distributed source coding is proposed, which is used to compress the spaceborne hyperspectral data effectively. In order to make full use of the intra-frame correlation and inter-frame correlation, the prediction error block scheme are introduced. Compared with the scalar coset based distributed compression method (s-DSC) proposed by E.Magli et al., that is , the bitrate of the whole block is determined by its maximum prediction error, and the s-DSC-classify scheme proposed by Song Juan that is based on classification and coset coding, the prediction error block scheme could reduce the bitrate efficiently. Experimental results on hyperspectral images show that the proposed scheme can offer both high compression performance and low encoder complexity and decoder complexity, which is available for on-board compression of hyperspectral images.

Paper Details

Date Published: 17 May 2016
PDF: 9 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401W (17 May 2016); doi: 10.1117/12.2228252
Show Author Affiliations
Yongjun Li, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)
Juan Song, Xidian Univ. (China)
Weijia Liu, Xidian Univ. (China)
Jiaojiao Li, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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