Schema genetic algorithm for fractal image compression
In this paper, fractal image compression using schema genetic algorithm (SGA) is proposed. Utilizing the self-similarity property of a natural image, the partitioned iterated function system (PIFS) will be found to encode an image through genetic algorithm (GA) method. In SGA, the genetic operators are adapted according to the schema theorem in the evolutionary process performed on the range blocks. Such a method can speed up the encoder and also preserve the image quality. Simulations show that the encoding time of our method is over 100 times faster than that of the full search method, while the retrieved image quality is still acceptable. The proposed method is also compared to another GA method proposed by Vences and Rudomin. Simulations also show that our method is superior to their method in both the speedup ratio and retrieved quality. Finally, a comparison of the proposed SGA to the traditional GA is presented to demonstrate that when the schema theorem is embedded, the performance of GA has significant improvement.
genetic algorithm fractal image compression partitioned iterated function system schema theorem