The paper demonstrates the application of adaptive vector quantization techniques to the coding of monochromatic and color pictures. Codebooks of representative vectors are generated for different portions of the image. By means of extensive simulations, the performance of the coder for monochrome images is estimated as a function of the various coding parameters-vector dimensionality, number of representative vectors, and degree of adaptivity. It is shown that for a vector size of dimension 4, there is a zone of operation, ranging from 1.0 to 1.5 bits/pixel, where adaptive vector quantization is advantageous. Despite the simplicity of the decoder, the performance of the adaptive vector quantizer is found to be comparable with block transform coding schemes for both monochrome and color pictures.

Vector quantization techniques are currently favored in speech compression. Recently a nonadaptive form of vector quantization was proposed for monochrome image compression. In this paper, color picture compression by adaptive vector quantization is presented. Both spatial and spectral redundancy is exploited. Adaptive vector quantization of color pictures is shown to produce better mean-square-error result than block cosine transform coding, for the same data rate. No statistical image model is assumed. Decoding is a simple process of table look-up, permitting video-rate decoding, thus only a small refresh memory containing the codewords is necessary.