编码时间过长是目前分形图像压缩存在的主要问题,尽管对图像块进行分类是解决这一问题的一类重要方法,然而诸多分类方法中仍普遍存在着编码速度与解码质量之间的矛盾.针对这一问题,在给出衡量分类方法性能指标体系的基础上,首先提出了一种自适应分类方法,从而较好地解决了这一矛盾,然后将该方法运用于质心分类上,并结合满意匹配得到了一种快速的分形编码方法.实验表明,与原来的均匀分类方法相比,在取得相同压缩比的前提下,该方法可进一步提高分形编码的速度和改善解码图像质量.

Long coding time is the main problem in fractal image compression at present,to which classification of image blocks is an important and efficient solution.However,there is an contradiction between coding speed and image quality in most classification methods.Aiming at the contradiction we first give out the guide lines of the performance for classification methods,hence propose a technique called "Adaptive Classification",which is then used in the classification by mass center combined with satisfied match,thus obtain a fast method for fractal coding.Experimental results indicate that at the same compression ratio,the proposed method improves the speed of fractal coding and the quality of decoded image,in contrast with the original uniform classification.