基于模糊聚类优化的序列图像快速分形压缩

A fast sequential image fractal coding approach based on optimal fuzzy clustering

针对传统序列图像分形压缩算法编码时间过长的问题,提出了一种基于模糊聚类优化(OFC)的快速算法,它是一种基于单帧的序列图像帧间分形压缩算法。首先使用LBG(Linde-Buzo-Gray)方法对序列图像组成的搜索空间样本集进行初始化,然后将OFC方法应用于对样本集的软分类,匹配时通过用类内搜索取代全局搜索,将分形编码过程聚焦在最有效的局部范围内,从而减少了匹配次数,降低编码时间。由于OFC算法是一种软分类方法,样本集类别数的确定即最终聚类方案是取样本集所有可能的分割中对应于目标函数最小者的分割,所以它不但是基于全局最优的聚类方法,避免了基于局部最优LBG算法中的某些误判,而且有效抑制了传统硬分类方法中类别数需预先指定的人为干扰因素,使恢复图像的质量能够得到更有效保证。相同运算环境下的仿真实验结果说明,在不影响信噪比和压缩比的前提下,与传统序列图像分形压缩算法相比,OFC算法编码速度可提高约5倍,证明了本算法的优越性。

To reduce the coding time of the conventional method, a fast sequential image fractal compression algorithm was proposed on the basis of the principle of optimal fuzzy clustering (OFC) for an unsupervised sample set with the category number settled by the algorithm itself. We utilized the cost function defined by the OFC algorithm to obtain the best category number corresponding to the minimum value of the function. Firstly the Linde-Buzo-Gray (LBG) algorithm was realized to acquire a rough cluster of the domain pool. Then the optimal category number was obtained by implementing our algorithm with small computational cost. Finally the more precise category was gained and the detail of the reconstructed image efficiently preserved. As a global optimal algorithm, OFC not only helps LBG eliminate the local minima, but also effectively compensates for the arbitrary interference in hard clustering problem. Soft clustering of the domain blocks allows classified searches instead of global ones and takes less coding time, and therefore clearly outperforms to the classic method relying on reduction of the size of the domain pool by classification. In computer simulation, OFC-based algorithm for the fractal coding scheme achieved excellent performance. For some standard and sequential medical images, the results denoted that the encoding speed was improved by about 5 folds without affecting the signal-to-noise ratio and compression ratio, and the quality of the reconstructed image could be better retained.