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

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_T in order to generate an improved operational codebook C_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 _O will have rate-distortion performance (including all side-information) better than or equal to that of any initial codebook C _T. When coding the “Barbara” image, improvement at all rates is demonstrated with gains of up to 3 dB in PSNR observed