Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Detecting and restoring the tampered images based on iteration-free fractal compression
Journal of Systems and Software
Technique for fractal image compression using genetic algorithm
IEEE Transactions on Image Processing
Image coding based on a fractal theory of iterated contractive image transformations
IEEE Transactions on Image Processing
Fractal image compression using visual-based particle swarm optimization
Image and Vision Computing
Study on huber fractal image compression
IEEE Transactions on Image Processing
Genetic algorithm with a hybrid select mechanism for fractal image compression
Digital Signal Processing
Real time fractal image coder based on characteristic vector matching
Image and Vision Computing
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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.