自适应搜索的快速分形图像编码算法

Quick Fractal Image Encoding Algorithm Based on Adaptive Searching Scope

为了改进全搜索分形编码过程匹配耗时长而导致难以实用的缺点,新定义了规范块灰度值力矩来反映图像块的特征,根据匹配均方根误差与规范块灰度值力矩间的关系,建立一个预先剔除条件来排除许多不大可能匹配range块的domain块.对一个待编码range块,仅在与该range块灰度值力矩数值最接近的domain块的自适应搜索邻域范围内找它的最佳匹配块.仿真结果表明,与全搜索分形图像编码算法相比,三幅测试图像在重建图像质量更好的情况下,能够平均加快它的编码速度58倍.

Fractal image encoding with full search is hardly useful in reality due to the fatal drawback of being quite time consuming during its encoding process.In response to this problem,a newly-defined gray value moment features of normalized image block is proposed,then introducing a predetermined kick-out condition based on an inequality linking the root-mean-square and gray value moment features.It can effectively confine the searching scope of best-matched block for an input range block to the adaptive neighborhood of the initial-matched block（i.e.,the domain block having the closest gray value moment to the input range block being encoded）,Obviously,which has excluded a large number of unqualified domain blocks so as to speed-up fractal image encoding process.Simulation results show that the proposed scheme not only reduce the searching scope of best-matched to averagely obtain the speedup of 58 times or so,but also can accomplish good quality of the reconstructed images against the full search method.