Grey video compression methods using fractals

Image compression methods using fractals has been widely because of its advantages of high compression ratio and the simplicity of decompression, which is particularly suited to the situation of one encoding and many decodings. Fractal theory studies self-similarity in objects. At the beginning of the 1980s, it was used to simulate natural scenes in computer graphics. The self-similarity used in fractal theory is known as an iterated functional system (IFS), and was first proposed by Hutchinson in 1981 [1]. Later, Barnsley and colleagues [2, 3] used IFS to simulate natural scenes, such as clouds, trees, and leaves, and implemented the concept using computer programs. Subsequently, fractal image compression, in which a complete image is compressed using IFS, was developed. The first fractal image compression system was described by Jacquin [4] in 1989. From the early 1990s to the mid-1990s, studies of fractal image compression focused on different partition methods, including square, triangular, and rectangular partition, to improve the qualities of decompressed images, and on classifications of range blocks and domain blocks to reduce the run times of compression algorithms [5]. Recently, researchers have focused on two aspects of fractal image compression: fractal image compression for still images, concentrating on the hybrid combination of fractal compression methods and other compression methods, and parallel or distributed algorithms for