Image Coding by Matching Pursuit and Perceptual Pruning

We introduce an image coding algorithm based on the cortex transform, matching pursuit and perceptual pruning that selects and codes only the most perceptually relevant image components, as determined by a new image indistinguishability criterion that derives from a maximum likelihood paradigm. We demonstrate that this method selects image components that produce images with higher subjective quality than matching pursuit with the same number of components. We also show that our perceptual quantization rule makes a substantial gain over uniform scalar quantization. Finally, we present image coding results of high quality at low encoding rates.