IMAGE COMPRESSION VIA VECTOR QUANTIZATION WITH VARIABLE DIMENSION.

Novel vector-quantization algorithms are given and applied to image compression. These algorithms extend the conventional algorithms which is a direct generalization of the classical Lloyd-Max type. The quantizer generates subregions of convex polygons with different sizes. Novel size of the convex polygon is chosen so that the total distortion of the training or source image to a given codebook is minimized. Experiments on 8-b/pixel images show that the optimization of the variable-region (or variable-dimension) vector quantization subdivides the original rectilinear pixel array into versatile convex polygons allowing slant edges.