نظام هجين : توازي خوارزمية جينية- عصبية في كبس الصور الكسوري باستخدام حاسبات متعددة

Abstract

Recently, effective technologies in Fractal Image Coding (FIC) were used to reduce the complexity of search for the matching between the Range blocks and the Domain blocks which reduces the time needed for calculation. The aim of this research is to propose a Hybird Parallel Neural -Genetic Algorithm (HPNGA) using the technique of (Manager/Worker) in multiple computers in order to obtain the fastest and best compression through extracting the features of the gray and colored images to attenuate the problem of dimensions in them .The NN enabled to train separate images from the test images to reduce the calculation time. The NN able to adapt itself with the training data to reduce the complexity and having more data and is merged with the parallel GA to reach optimum values of weights with their biases. The optimum weights obtained will classify the correct search domains with the least deviation ,which, in turn ,helps decompress the images using the fractal method with the minimum time and with high resolution through multiple computers. The results showed that the proposed hybrid system is faster than the standard algorithm ,the NN and GA in decompressing the FIC and they are flexible and effective to reach the optimum solution with high speed and resolution .The search method used for compression and de-compression has a vital role in improving the ratio and the quality of image compression which reached 15s .The ratio of compression reached to 90.68% and the image improvement after decompression reached to 34.71db when compared to other methods of (FIC), which didn't exceed 90.41% and image quality of 32.41db and the execution speed was only 21s.