Abstract :
In this paper a novel model for multiscale space-color quantization of multispectral images, is described. The approach is based on the hierarchical clustering technique, derived from the statistical physics model of free energy (Jovovic 1996, Jovovic et al. 1999). The group vectors for image color are computed on the adaptively selected windows of computation, as contrasted to the block-size windows, optimizing the accuracy of the computation of the group vectors with the density of sampling an image by the group windows. The algorithm is suitable for implementation in parallel computer architectures. The results of quantization of color images by our algorithm are compared with 3 image compression techniques: 1) wavelets, 2) discrete cosine transform (DCT), and, 3) quad tree (QT). Contextual information of spatial coherency of the data is used in the segmentation process, in our algorithm. As a result, much better spatial resolution and small size of compressed images are obtained by our algorithm, as compared to the other techniques, for any error level of compression selected. Major spatial features are optimally color-coded along the hierarchy of scales of computation. The images quantized with our algorithm are suitable for the run-length encoding scheme of the hierarchy of binary images
Keywords :
data compression; image coding; image colour analysis; image resolution; image sampling; image segmentation; pattern clustering; adaptively selected windows; compressed images; contextual information; error level; free energy; group vectors; group windows; hierarchical clustering technique; image color; image compression; multiscale space-color quantization; multispectral images; parallel architectures; run-length encoding; sampling; segmentation process; spatial coherency; spatial resolution; Color; Computer architecture; Discrete cosine transforms; Discrete wavelet transforms; Image coding; Image sampling; Multispectral imaging; Physics; Quantization; Spatial coherence;
