Measurement of the Efficiency of Parallel Genetic Algorithm for Compress and Decompression of Fractal Imaging Using Multiple Computers

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 best and quickest compress for images 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 searching domain block in order to choose a protection strategy and compress of high accuracy of images . Results showed that PGA is quicker than standard algorithm in FIC and is more 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, (selection, crossover and mutation) were of a significant role in improving the image compression ratio and quality for images in high speed that has reached 15s , compression ratio has reached 91.68% , while the image quality was improved after decompression and has reached roughly 34.81 compared to traditional method of fractal image coding (FIC) where the compression ratio has reached 83.87% and image quality 31.79 with algorithm implementation speed reached 28s.