Lossless compression of hyperspectral images using adaptive edge-based prediction

Keyan Wang; Liping Wang; Huilin Liao; Juan Song; Yunsong Li

doi:10.1117/12.2022426

24 September 2013 Lossless compression of hyperspectral images using adaptive edge-based prediction

Keyan Wang, Liping Wang, Huilin Liao, Juan Song, Yunsong Li

Author Affiliations +

Proceedings Volume 8871, Satellite Data Compression, Communications, and Processing IX; 887105 (2013) https://doi.org/10.1117/12.2022426
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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.

Citation Download Citation

Keyan Wang, Liping Wang, Huilin Liao, Juan Song, and Yunsong Li "Lossless compression of hyperspectral images using adaptive edge-based prediction", Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 887105 (24 September 2013); https://doi.org/10.1117/12.2022426

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