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1 September 2006 Classification and prediction of wavelet coefficients for lossless compression of Landsat images
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Abstract
Inspired by previous work on the modelling of wavelet coefficients, and on the observed differences between distributions of wavelet coefficients belonging to different landscapes, we present a lossless compressor of multi-spectral images based on the prediction of wavelet coefficients, conditioned to the landscape. This compressor operates blockwise. The wavelet transform is applied to each block, and detail coefficients from the two finest scales are predicted by means of a linear combination of other coefficients, which may belong to the same band as the predicted coefficient, or to a previously coded band. The weights for the lineal combination are estimated on-line: for each detail subband, the compressor is trained on all the detail coefficients belonging to the same class. In addition, a different band ordering is considered for each block. Differences in prediction are coded with a conditional entropy coder. Preliminary results reveal that we obtain more accurate predictions.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Acevedo and Ana Ruedin "Classification and prediction of wavelet coefficients for lossless compression of Landsat images", Proc. SPIE 6300, Satellite Data Compression, Communications, and Archiving II, 63000O (1 September 2006); https://doi.org/10.1117/12.677384
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