Image Compression Using Vector Quantization and Decision Trees

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Research will be conducted on variable rate tree-structured vector quantizers for image compression applications. The emphasis will be on tree-structured codes designed using extensions and variations of the tree design techniques of Breiman, Friedman, Olshen and Stone, but a variety of other vector quantization structures will be considered in combination with the tree- structured codes. In particular, both finite state and predictive vector quantizers will be considered for incorporating memory in the coding and two-dimensional subsampling techniques will be considered as a means of increasing effective vector size, improving prediction accuracy and providing a natural data structure. The dual use of trees for classification and compression in finite-state vector quantizers will be explored, as will be the combination of compression of image sequences such as video and multimodal images of a common object or view, as in multispectral and color imaging. Experiments will be conducted with medical images, computer image data consisting of mixed video, imagery, and graphics, and satellite imagery, especially multispectral.