الفهرس 
Chapter 1IntroductionOnly 14 pages are availabe for public view
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Abstract
Image compression techniques aim at the reduction of both the memory space necessary for storage and the transmission time required for transferring images. Compression can be defined in a formal way as the process intended to yield a compact digital representation of a signal. In the literature, the terms source coding, data compression, bandwidth compression, and signal compression are all used to refer to the process of compression. In this thesis, we are concerned with fractal image compression. Although fractal image compression is an efficient technique from the Compression Ratio (CR) perspective, it has complexity disadvantages. The complexity comes from the Iterated Function System (IFS) and the searching process. We propose in this thesis a simplified fractal algorithm to reduce the complexity and the consumed time. It is based on a modification in the segmentation or decomposition of the image into domain blocks. A modification in the searching process is performed to reduce the time consumed in the searching process. The results of the proposed algorithm are good in quality; Peak Signal-to-Noise Ratio (PSNR), and CR with about half the time consumed in the conventional fractal image compression system. One of the new techniques in fractal decomposition and searching is the spiral architectural technique. We study the spiral decomposition and searching process in fractal image compression to decode some prototype images. We found that the spiral architectural technique gives decoded images with better quality than those obtained with the square architectural technique. We have also investigated fractal image compression in transform domains and achieved good results.