Fractal image compression based on spatial correlation and chaotic particle swarm optimization

Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In order to solve the high complexity of the conventional encoding scheme for fractal image compression, a spatial correlation chaotic particle swarm optimization (SC-CPSO), based on the characteristics of fractal and partitioned iterated function system (PIFS) is proposed in this paper. There are two stages for the algorithm: (1) Make use of spatial correlation in images for both range and domain pool to exploit local optima. (2) Adopt chaotic PSO (CPSO) to explore the global optima if the local optima are not satisfied. Experiment results show that the algorithm convergent rapidly. At the premise of good quality of the reconstructed image, the algorithm saved the encoding time and obtained high compression ratio.