Hybrid System: Parallel Neural -Genetic Algorithm Algorithm for Compacting Fractal Images Using Multiple Computers

• Main Authors: Shahla Abdel-Qader, Suzan Ibrahim, Omia Abdel-Jabbar
• Format: Article
• Language: Arabic
• Published: Mosul University 2013-12-01
• Series: Al-Rafidain Journal of Computer Sciences and Mathematics
• Subjects: hybird system; artificial neural network(ann); parallel genetic algorithm (pga); fractal image coding(fic); rang block; domain block
• Online Access: https://csmj.mosuljournals.com/article_163558_6e7bb57cf9677689f9525aa172234012.pdf

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 15_s .The  ratio of compression reached to  90.68% and the image improvement after decompression   reached  to 34.71_db  when  compared to other  methods of  (FIC), which didn't exceed 90.41% and image quality of 32.41_db and the execution speed  was only  21_s.