Research On Several Fractal Image Compression Methods

Fractal image coding method is a new promising technology on data compression. Based on higher compression ratio, it also can provide better quality of the reconstructed image. A series of research and improvement of fractal image coding algorithm have been carried out in this paper. We also expand its application to 24-bits true color images. The concrete research methods and results are as follows:(1) A no search fractal image coding method is proposed in this paper. We applies it to 24-bits true color images. We first dispose a RGB image with gray-scale processing method, then compress the processed image with no search fractal image coding algorithm, and then treat the decoded image with pseudo-color processing. A set of experiments and simulations show that our method can significantly reduce coding time while improving image quality. Possibilities for further improvement are discussed.(2) An efficient lossless image compression scheme for still images based on adaptive arithmetic coding compression algorithm is proposed. The algorithm increases the image coding compression rate, and ensures the quality of the decoded image combined the adaptive probability model and predictive coding. The use of adaptive models for each encoded image block dynamically estimates the probability of the relevant image block. The decoded image block can accurately recover the encoded image according to the information of code book. We adopt adaptive arithmetic coding algorithm for image compression, the algorithm greatly improves the image compression rate.(3) An efficient fractal color image compression method based on cluster search according to fractal encode theory is proposed in this paper. We compress 3d color information into Id with color space conversion to gain the less encode time and classify the image blocks segmented by cluster algorithm to enlarge the probability of successful match. This is done in order to guarantee that a desired quality (fixed in advance using the well known PSNR metric) is checked. The efficiency of our scheme is demonstrated by results, especially, when faced to the method presented in the recently published paper on image compression code. Contrast experimental results show that our compression method is highly efficient.