Fractal image compression based on spatial correlation and hybrid particle swarm optimization with genetic algorithm

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 this paper, a fractal image compression algorithm based on spatial correlation and hybrid particle swarm optimization with genetic algorithm (SC-PSOGA), is proposed to reduce the searching space. 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 hybrid particle swarm optimization with genetic algorithm (PSO-GA), 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.