Fast Fractal Image Coding And High Compression Than The Texture Compression Method

Fractal image compression, as a new scheme of image compression, has received a great deal of attention and study from researchers all over the world in the field of image compression because of its desirable properties such as fast decoding, resolution independence of decoded image and high compression ratio. However, there is a especially unsatisfying problem in this method: too long encoding time, mainly because of the considerable number of domain blocks to compare with for each range block in encoding phase, which, in fact, prevent fractal image compression from becoming a practical method for image compression, hence fast encoding has become a hot issue in fractal image compression. Existed fast encoding methods are often at the cost of image quality, or can only obtain poor speed-up ratio. In this paper, we give a hybrid fractal image encoding technique, which takes on the domain blocks of fractal coding is a vector space, transforms the match of the range block and the domain block into the minimized distance problem of the Euclid space, establishes kd-tree for each sub-space at basis of classifying, further translates the match problem into the nearest neighbor search on tree structure and introduces 1+ε nearest neighbor search to more speed up. It can reach run-time coding. The experiments indicate that compared to the old method, our method can speed up the fractal encoding perfectly. Compared with no speeding-up coding, when ε=14, the hybrid fractal image coding can improve 66 times with rotation and reflection transformations, and can improve 653 times without rotation and reflection transformations.Texture is an important concept of the image processing and image third dimension sculpting whose many special properties make it obtain high compression ratio. Taking texture as the local property of images we can combine the texture compression and traditional compression methods to offset the disadvantage of the latter. The advanced texture compression methods are all based on hardware, so the potential of high compress ratio couldn't showed completely. The theory of training codebook of vector quantization accords with the repetition property of the texton, and the development of the synthesis methods give the texture compression the newway. In this paper, we give a new texture compression method, which samples the texture, then trains the codebook by vector quantization and takes the codebook as a result of coding, as is different form conventional vector quantization. The codebook is taken on as the "sample texture" of texture synthesis, and the decoding just is texture synthesis. If using blocks sampling, the decoding process is texture synthesis based on blocks. And instead of pixels sampling, the one is based on pixels. The experiments indicate that when insuring the quality we can attain high compression ratio, control it, and get decoding image of random size. Guaranteeing the fidelity, we can get a ratio of 32:1 or more.