Adaptive vector quantization for image coding in an entropy-constrained framework

Abstract

An adaptive entropy-constrained codebook design algorithm for vector quantization (VQ) of image data is proposed. The algorithm iteratively updates the code vectors of an initial general-purpose codebook C/sub T/ in order to generate an improved operational codebook C/sub O/ that is well-adapted to the statistics of a particular image or sub-image. Unlike other approaches, the rate-distortion trade-offs associated with transmitting updated code vectors to the decoder are explicitly considered in the design. An optimal trade-off is made possible by the entropy-constrained framework. For any image, the algorithm guarantees that the operational codebook C/sub O/ will have rate-distortion performance (including all side-information) better than or equal to that of any initial codebook C/sub T/. When coding the Barbara image, improvement at all rates is demonstrated with gains of up to 3 dB in PSNR observed. >