Tree-structured vector quantization with region-based classification

Unbalanced or pruned tree-structured vector quantization (PTSVQ), a variable-rate coding technique that tends to use more bits to code active regions of the image and fewer to code homogeneous ones, is developed based on a training sequence of typical images. A regression tree algorithm is used to segment the images of the training sequence using the x, y pixel location as a predictor for the intensity. This segmentation is used to partition the training data by region and generate separate codebooks for each region, and to allocate differing numbers of bits to the regions. Region-based classification requires no side information, as the decoder knows where in the image the current encoded block originated. These methods can enhance the perceptual quality of compressed images when compared with ordinary PTSVQ. Results for magnetic resonance data are shown.<
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