Image-adaptive vector quantization in an entropy-constrained framework.

An adaptive vector quantization (VQ) scheme with codebook transmission is derived for the variable-rate source coding of image data using an entropy-constrained Lagrangian framework. Starting from an arbitrary initial codebook C(I) available to both the encoder and decoder, the proposed algorithm iteratively generates an improved operational codebook C(0) that is well adapted to the statistics of a particular image or subimage. Unlike other approaches, the rate-distortion trade-offs associated with the transmission of updated code vectors to the decoder are explicitly considered in the design. In all cases, the algorithm guarantees that the operational codebook C(0) will have rate-distortion performance (including all side-information) better than or equal to that of any initial codebook C(I). When coding the Barbara image, improvement at all rates is demonstrated with observed gains of up to 3 dB in peak signal-to-noise ratio (PSNR). Whereas in general the algorithm is multipass in nature, encoding complexity can be mitigated without an exorbitant rate-distortion penalty by restricting the total number of iterations. Experiments are provided that demonstrate substantial rate-distortion improvement can be achieved with just a single pass of the algorithm.