Progressive model-based VQ for image data archival and distribution

Abstract

Vector quantization (VQ) based lossy compression techniques are ideally suited for image archival and distribution applications due to their asymmetrical computational properties. The practical use of VQ is however limited due to complications arising from design and maintenance of codebooks. Model-based VQ (MVQ) is a variant of VQ with the computational properties of VQ but without complications from explicit handling of codebooks. In MVQ, error models combined with a human visual system (HVS) model are used to generate an implicit model codebook. The decoder generates same codebook using the model parameters thus eliminating the need for training algorithm, transmitting and maintaining the codebooks. This algorithm was tested on several NASA images and results were published, M. Manchar et al. (1995). The present authors describe progressive MVQ along the lines of progressive VQ and evaluate its performance on NASA images.