A new region-based algorithm is proposed for the compression of multispectral images. The image is segmented in homogeneous regions, each of which is subject to spectral KLT, spatial shape-adaptive DWT, and SPIHT encoding. We propose to use a dedicated KLT for each region or for each class rather than a single global KLT. Experiments show that the classified KLT guarantees a significant increase in energy compaction, and hence, despite the need to transmit more side information, it provides a valuable performance gain over reference techniques.
Region based compression of multispectral images by classified KLT
Cagnazzo M.;
2006
Abstract
A new region-based algorithm is proposed for the compression of multispectral images. The image is segmented in homogeneous regions, each of which is subject to spectral KLT, spatial shape-adaptive DWT, and SPIHT encoding. We propose to use a dedicated KLT for each region or for each class rather than a single global KLT. Experiments show that the classified KLT guarantees a significant increase in energy compaction, and hence, despite the need to transmit more side information, it provides a valuable performance gain over reference techniques.
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