目的 基本分形图像编码非常耗时,基于子块特征的方法在加快编码速度方面非常有效,提出了一个新的子块特征函数,加快编码速度,改善编码性能.方法 首先证明了一个定理,它统一了多个文献提出的理论方法,可以对子块任意定义特征.定理阐述了特征轨迹的长度、分布与编码性能的关系,比较了多个文献算法的优劣,并在定理的基础上定义了一个新的特征.结果 实验结果表明,提出的算法在编码性能上较主对角和算法和叉迹算法有一定程度的提高,在PSNR相同情况下该算法具有较短的编码时间,在编码时间相同情况下该算法具有较高的PSNR;在相同的搜索半径内,该算法可找到更多的最佳匹配块;基于子块特征的近邻只是匹配误差极小的必要而非充分条件,当搜索10%的码本容量时,该算法只有约25%的R块找到了其最佳匹配块,当搜索到一半码本容量时,这样的R块数量达到了约80%.结论 提出的新子块特征算法较主对角和算法和叉迹算法改善了编码性能,提高了图像质量.

Objective Traditional fractal image coding is highly time consuming. However, according to the sub-block feature, the approach is very effective in accelerating encoding speed. This study proposes a new sub-block feature function to accelerate coding speed and improve coding performance.Method This study proves a theorem that unifies theoretical approaches from other documents. This theorem describes the relationship among trace length, trace distribution, and encoding performance. The advantages of multiple documents are compared, and a new block feature is defined on the basis of the theorem. Result Experimental results show that the proposed algorithm has improved the main diagonal sum feature. Moreover, the local cross trace feature in coding performance has a short encoding time under the same PSNR and has high PSNR under the same encoding time. In the same search radius, the algorithm could determine a large number of optimal matching blocks. That the R-block is a neighbor of the D-block according to block feature is a necessary but insufficient condition to obtain the best matching error. When 10% of the codebook capacity is searched, only approximately 25% of all R-blocks can find their best matching blocks. When half of the codebook capacity is searched, the number of such R-blocks can reach approximately 80%. Conclusion The proposed algorithm has better coding performance and decoding image quality than the main diagonal sum and local cross trace features.