三均值特征的快速分形图像编码算法

分形图像编码因在高压缩比时具有好的重建图像质量而备受图像压缩领域的极大关注，但它在编码过程特别耗时的致命缺点限制了其诸多应用。针对这个问题，新定义了图像块的规范块三均值特征，并根据匹配均方根误差与规范块三均值特征间的关系，提出了一个限制搜索空间的快速分形编码算法：一个待编码range块的最佳匹配块搜索范围仅在初始匹配块（与range块的三均值特征值相近的domain块）的邻域内，搜索邻域的大小由预先设置的剔除条件来自动控制。3幅图像的仿真结果表明，它确实能够在重建图像质量更好的情况下，平均加快了全搜索分形编码算法的编码速度29倍左右，且也优于新叉迹特征算法。

Fast fractal image encoding algorithm based on three mean feature

Fractal image coding has been attracting extensive attention in image compression field on account that it can provide a highly reconstructed image with a high compression ratio. Whereas it is hardly useful in many applications due to the fatal drawback of being quite time consuming during its encoding process. In response to this problem, the fast scheme is proposed to limit search space on the basis of an inequality linking the root-mean-square and newly-defined three-mean features of normalized block. It can effectively confine the searching scope of best-matched block for an input range block to the neighbourhood of the initial-matched block(i.e., the domain block having the closest three-mean features to the input range block being encoded), and the proposed algorithm sets up a predetermined kick-out condition, which is also used to control the size of search neighbourhood automatically. Simulation test results demonstrate that, for three standard test images, the proposed scheme averagely obtain the speedup of 29 times or so, while can achieve good quality for the reconstructed images against the full search method. Besides, its performance is better than newly cross trace feature of normalised block algorithm.