Adaptive approximation image coding models In this work we present some image coding models based on adaptive approximation techniques. The image coding models presented are based on Matching Pursuit and High Resolution Pursuit, which are the most popular adaptiveapproximation techniques. These models have a similar computational complexity and structure. The models expands an image along an overcomplete dictionary. The dictionary was selected according to a best basis metric or a training strategy. From such expansion, the model selects the coefficients that correspond to the most important image structures. Selected coefficients are quantized just when they are chosen, in order to minimize error propagation along the process. These coefficients represent an optimal image decomposition, or a reduced image representation. This representation, in some way, corresponds to a coded image with a high compression rate. A simple reconstruction algorithm recovers the original image with a high visual quality.

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.