Adaptive Approximation Image Coding Models

摘要

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 adaptive approximation 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.