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Fractal image compression based on visual perception

Self-similarities, commonly explored in fractal image compression, are usually translated into matches between two pools, the range and the domain blocks, which are different partitions of the same image to be encoded. Simple transformations on the domain blocks are used in order to obtain a better match. A root-mean-square error measure between a range block and a transformed domain block is used in the encoding process to quantify the performance of the matching process and subsequently the quality of the encoded imaged. Alternative measures can be used in fractal image compression to account for human visual perception. Simple strategies, such as block intensity weighting and block texture weighting, reduce perceptual degradation with only very little added computational cost. Weighted error measures in frequency domain, though computationally much more expensive, can provide a more natural model of visual perception such as the direct account for the contrast sensitivity function. A multiscale approach to encode the image details at different resolution is proposed to not only speed up the matching process between the range and the domain blocks but also provide a mechanism for multiscale representation.