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 C₀ 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 C₀ 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