Measurement of the Efficiency of Parallel Genetic Algorithm for Compress and Decompression of Fractal Imaging Using Multiple Computers - Shahla Abdul Qadir (Al-Rafidain Journal of Computer Sciences and Mathematics)
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.