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Proceedings Paper

Lossless compression of hyperspectral images using adaptive edge-based prediction
Author(s): Keyan Wang; Liping Wang; Huilin Liao; Juan Song; Yunsong Li
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Paper Abstract

By fully exploiting the high correlation of the pixels along an edge, a new lossless compression algorithm for hyperspectral images using adaptive edge-based prediction is presented in order to improve compression performance. The proposed algorithm contains three modes in prediction: intraband prediction, interband prediction, and no prediction. An improved median predictor (IMP) with diagonal edge detection is adopted in the intraband mode. And in the interband mode, an adaptive edge-based predictor (AEP) is utilized to exploit the spectral redundancy. The AEP, which is driven by the strong interband structural similarity, applies an edge detection first to the reference band, and performs a local edge analysis to adaptively determine the optimal prediction context of the pixel to be predicted in the current band, and then calculates the prediction coefficients by least-squares optimization. After intra/inter prediction, all predicted residuals are finally entropy coded. For a band with no prediction mode, all the pixels are directly entropy coded. Experimental results show that the proposed algorithm improves the lossless compression ratio for both standard AVIRIS 1997 hyperspectral images and the newer CCSDS test images.

Paper Details

Date Published: 24 September 2013
PDF: 10 pages
Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 887105 (24 September 2013); doi: 10.1117/12.2022426
Show Author Affiliations
Keyan Wang, Xidian Univ. (China)
Liping Wang, Xidian Univ. (China)
Huilin Liao, Xidian Univ. (China)
Juan Song, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 8871:
Satellite Data Compression, Communications, and Processing IX
Bormin Huang; Antonio J. Plaza; Chein-I Chang, Editor(s)

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