Conference Paper

Space-color quantization of multispectral images in hierarchy of scales

Univ. of Montenegro, Podgorica
DOI: 10.1109/ICIP.2001.959195 In proceeding of: Image Processing, 2001. Proceedings. 2001 International Conference on, Volume: 1
Source: IEEE Xplore

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

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