Segmentation based coding of textures using stochastic vector quantization

Abstract

In second generation image compression techniques the image to be compressed is first segmented. The pixels are divided into mutually exclusive spatial regions based on some criteria. After segmentation, the image consists of regions separated by contours. Then, the information is coded describing the shapes and interiors of the regions. The interiors of the regions are usually encoded using polynomials. The objective of this paper is to encode the interior of the regions by stochastic vector quantization techniques. If the segmentation process has been well defined and the obtained regions are homogeneous, then it is possible to design a specific codebook suited to the statistics of each region. The approach is to design the codebook according to some previously defined model for the regions of the image found in the segmentation process. If the approach is combined with efficient contour coding techniques, good visual results for high compression rates are obtained.