Abstract :
Summary form only given. Given a training set TS of n k-dimensional input vectors TS=(x1,x2,...,xn ,) considered representative of the signal statistics, the task of designing the optimal vector quantizer consists of finding a set of clusters P={p1,p2,...,ph} containing np1, np2,..., nph vectors each in a way to minimize a distortion functional. In the article the mean square error with respect to the training set TS is considered. The experimental results presented have been obtained on three 512×512 pixels monochromatic (8 bit/pel) images. The proposed method and the well known generalized Lloyd algorithm are iterated until a stable quantizer is obtained. The computational complexity of the two methods is comparable
Keywords :
computational complexity; error analysis; functional equations; image coding; vector quantisation; 262144 pixel; 512 pixel; clusters; computational complexity; distortion functional; experimental results; generalized Lloyd algorithm; input vectors; mean square error; monochromatic images; optimal vector quantizer; signal statistics; stable quantizer; training set; vector quantization; Clustering algorithms; Computational complexity; Partitioning algorithms; Pixel; Signal design; Statistics; Testing; Vector quantization;
