Image compression using constrained relaxation In this work, we develop a new data representation framework, called constrained relaxation for image compression. Our basic observation is that an image is not a random 2D array of pixels. They have to satisfy a set of imaging constraints so as to form a natural image. Therefore, one of the major tasks in image representation and coding is to efficiently encode these imaging constraints. The proposed data representation and image compression method not only achieves more efficient data compression than the stateoftheart H.264 Intra frame coding, but also provides much more resilience to wireless transmission errors with an internal errorcorrection capability.

Lossless and nearlossless image compression with successive refinement We present a technique that provides progressive transmission and nearlossless compression in one single framework. The proposed technique produces a bitstream that results in progressive reconstruction of the image justlike what one can obtain with a reversible wavelet codec. In addition, the proposed scheme provides nearlossless reconstruction with respect to a given bound after each layer of the successively refinable bitstream is decoded. We formulate the image data compression problem as one of asking the optimal questions to determine, respectively, the value or the interval of the pixel, depending on whether one is interested in lossless or nearlossless compression. New prediction methods based on the nature of the data at a given pass are presented and links to the existing methods are explored. The tradeoff between noncausal prediction and data precision is discussed within the context of successive refinement. Context selection for prediction in different passes is addressed. Finally, experimental results for both lossless and nearlossless cases are presented, which are competitive with the stateoftheart compression schemes.