Fractal image compression using visual-based particle swarm optimization
Fractal image compression is promising both theoretically and practically. The encoding speed of the traditional full search method is a key factor rendering the fractal image compression unsuitable for real-time applications. In this paper, particle swarm optimization (PSO) method by utilizing the visual information of the edge property is proposed, which can speedup the encoder and preserve the image quality. Instead of the full search, a direction map is built according to the edge-type of image blocks, which directs the particles in the swarm to regions consisting of candidates of higher similarity. Therefore, the searching space is reduced and the speedup can be achieved. Also, since the strategy is performed according to the edge property, better visual effect can be preserved. Experimental results show that the visual-based particle swarm optimization speeds up the encoder 125 times faster with only 0.89dB decay of image quality in comparison to the full search method.
fractal image compression particle swarm optimization edge-type classification