Abstract
An improved method for the lossy compression of the AVIRIS hyperspectral images is proposed. It is automatic and presumes blind estimation of the noise standard deviation in component images, their scaling (normalization) and grouping. A 3D DCT based coder is then applied to each group to carry out both the spectral and the spatial decorrelation of the data. To minimize distortions and provide a sufficient compression ratio, the quantization step is to be set at about 4.5. This allows removing the noise present in the original images practically without deterioration of the useful information. It is shown that for real life images the attained compression ratios can be of the order 8 ...35. © 2010 Springer-Verlag.
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Ponomarenko, N., Lukin, V., Zriakhov, M., & Kaarna, A. (2010). Improved grouping and noise cancellation for automatic lossy compression of AVIRIS images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6475 LNCS, pp. 261–271). https://doi.org/10.1007/978-3-642-17691-3_24
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