An improved fast fractal image compression using spatial texture correlation

This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.