Data compression aims at producing an efficient representation for storage or transmission in order to communicate or store the smallest possible number of bits while retaining high quality in the reconstructed image. Statistical classification and segmentation are both concerned with labeling parts of an image as being of a certain type or class, such as tumor or normal tissue in a medical image or text, graphics, or photographs in a document page. Classification and segmentation can be used to assist human users of images, as in highlighting suspicious tissue in a medical image or defects in an imaged circuit, or they can be used for the automatic extraction of features in intelligent browsing of large databases. All three operations have in common the goal of efficiently representing an image as a whole, by optimally trading off quality and cost or distortion, an din decomposing the image into useful components. Most of the systems involving these operations, however, focus on each separate operation rather than on combining them into common algorithms with multiple goals. This project is devoted to the development of theory and algorithms for jointly performing these operations and related signal processing such as the estimation of probability distributions or models from observed data. The approach involves optimizing single combined systems subject to possibly conflicting combined goals, such as maximizing signal-to-noise ratios and minimizing bit rates and Bayes risk. Theory will be reinforced by simulations for both artificial sources, where theoretical performance bounds can be used for comparison, and real-world examples drawn from medical and document applications.
