Using Parallel Genetic Algorithms to Compress Fractal Images

	Using Parallel Genetic Algorithms to Compress Fractal Images
Buhuth Mustaqbaliya Scientific Periodical Journal
Article 1, Volume 4, Issue 2, 2013, Pages 207-228
Abstract
Abstract Efficient technologies have been recently used in fractal image coding (FIC) to reduce the complexity of searching for matching between range block and domain block. The research aims at using the Parallel Genetic Algorithm (PGA) by the technology of the (Manager/Worker) in parallel computers to obtain matched domain blocks that prevent unsuitable convergence by coding the site of the searching domain block with a Gray code and a fitness function that minimizes the space between the matching of the current range block with the domain block under discussion in order to choose a protection strategy and coding of high accuracy for any image. Results showed that PGA is successful in fractal image coding and is flexible and efficient in reaching the optimum solution in higher speed and efficiency through using the Gray code. The searching method used for the parallel algorithm for compression and decompression, the method of choosing GA's coefficients, the selection, the crossover and mutation had a significant role in improving the image compression ratio and quality. Compression ratio has reached 87% while the image quality was improved after decompression that reached roughly 33% compared to traditional method in fractal image coding (FIC). Keywords-Parallel Genetic Algorithm (PGA), Fractal Image Coding (FIC), Local Iterated Function System (LIFS), Rang Block, Domain Block.

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