| With the rapid development of the national economy, many science and engineering research departments and institutes wish to deal with ever larger problems in scientific computing. Parallel computing is a possible way to fulfill such desires. Many large-scale scientific computations require high-performance computers to implement the computing tasks. High-performance computer is a broad computer science subject dealing with advanced hardware, software, network, and algorithms. Progress in the standard of high-performance is along with the development of technology. The application and research level of high-performance computer indicates the general scientific advancement of a country. This thesis examines parallel algorithms for fractal image compression which is based on the self-similarity search between range and domain blocks, so it can achieve a high compression ratio. Fractal compression of video sequences is the extension of fractal still image compression which has a high computational complexity that restricts its commercial applications. The implementation of the compression algorithm depends on the high configuration of computer hardware. In the process of performing fractal video compression, searching and matching image blocks between each group of frames are independent from each other. As a result compression time can be greatly reduced by using parallel computing. Due to the independent feature of the process of searching cube range blocks in a fractal video compression system this thesis examines the design of a suitable parallel algorithm for fractal video compression and the performance of such algorithm. An overview of of several different systematic structures of scalable parallel computers and parallel programming model suggested that MPI seems to be a good candidate for implementing fractal video compression algorithms based on distributed-memory parallel computers. The searching and matching process of domain blocks and range blocks of the image is assigned to several processors to execute. MPI is commonly used message passing library for parallel environment. MPI provides a criterion which is independent of language and platform and can be widely used for making message passing programs. Experimental results show that the parallel algorithm is able to reduce the compression time and achieve a high speedup without changing the compression rate. A scalability test has also performed, and results show that this parallel algorithm is scalable. Parallel computing is particularly suitable in treating data parallel processing, has the potential of achieving real-time compression of images, and can be used as the server program for image processing. The parallel algorithms developed in this thesis is industrial practical and will certainly become a promising tool in image compression and processing. |
