Gray Tree-structured vector quantization is an approach to image compression that applies ideas from statistical clustering algorithms and tree-structured classification and regression algorithms to produce compression codes that trade off bit rate and average distortion in a near optimal fashion. This research is examining the explicit combination of these two forms of signal processing, compression and classification, into single tree-structured algorithms that permit a trade off between traditional distortion measures, such as squared error, with measures of classification accuracy such as Bayes risk. The intent is to produce codes with implicit classification information, that is, for which the stored or communicated compressed image incorporates classification information without further signal processing. Such systems can provide direct low level classification or provide an efficient front end to more sophisticated full-frame recognition algorithms. Vector quanitization algorithms for relatively large block sizes are also being developed with an emphasis on multiresolution compression algorithms. In order to improve the promising performance found in preliminary studies or combined compression and classification, it will be necessary to use larger block sizes or, equivalently, more context. Multiresolution or hierarchial quantizers provide a simple and effective means of accomplishing this. Other related issues are being explored, including improved prediction methods for predictive vector quantization and image sequence coding.
