Research On Fractal Image Compression Literature&Improvement Measures

In the 21st century, the technology of network quickly develops, the multimedia technology based on image enriches our lives. However if there is no highly effective data compression approach, image communication can not be achieved. The purpose of image compression coding is to represent images with few bits, maintain the quality of recovering images according to the requirements of certain application situations. Image compression is the key and bottleneck of multimedia technology.Fractal image processing technique is widely applied in compression coding, region increasing, pattern recognition and so on. The method of fractal image compression transfers a digital image into a group of contract iterated function system (IFS) model. Encoding IFS's parameters achieves image compression. This method may gain higher compression ratio, as well as rapid decoding. To many image which is not strictiy self-similar, the usual fractal image compression method based on block partition divides the image into non-overlap regular shape block collection. Every block's iterate function system is found out by local self-similarity. The parameters of all iterated function system form fractal image compression code.Traditional fractal image coding first divides the image into ranges and domains at different size, and then searches the best matching domain of a range in the whole image after contracting and affine transforming of the domains. Since each domain commonly corresponds to eight affine transforms, the process of searching contracting mapping block cost vast time. The advantages of traditional fractal coding are counteracted by the low speed of compression. Therefore, after researching on basic theories of fractal compression, we improved algorithm based on nearest neighbor search. The new method search the domain block in the near neighbor. This procedure not only reduces the compression time, but also achieves the purpose of compression. In addition, we also introduce ideas based on human visual system about analyzing the similarity between blocks to improve the fractal image compression.