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28 January 2002 Near-lossless compression of multi/hyperspectral image data through a fuzzy-matching-pursuit interband prediction
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Abstract
In this work, near-lossless compression yielding strictly bounded reconstruction error, is proposed for high-quality compression of remote sensing images. A space-varying linear-regression prediction is obtained through fuzzy-logic techniques as a problem of matching pursuit, in which a predictor different for every pixel is obtained as an expansion in series of a finite number of prototype nonorthogonal predictors, that are calculated in a fuzzy fashion as well. To enhance entropy coding, the spatial prediction is followed by context-based statistical modeling of prediction errors. Performance comparisons with JPEG 2000 and previous works by the authors, highlight the advantages of the proposed fuzzy approach to data compression.
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Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Leonardo Santurri "Near-lossless compression of multi/hyperspectral image data through a fuzzy-matching-pursuit interband prediction", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454159
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