由于传统的K-均值算法在用于矢量量化时强烈依赖初始码书的选取,如果初始码书选取不好,则很容易陷入局部最小点;而Bezdek的模糊K-均值算法由于计算量很大,也很少用于矢量量化的设计码书,因此,人们一直在寻找收敛速度和收敛效果两者性能都较好的算法.在研究Nicolaos等人提出的模糊矢量量化(FVQ)算法基础上,针对FVQ算法收敛过程存在的问题,并从收敛结构和收敛策略出发,提出了一种动态的模糊矢量量化算法(DFVQ);同时给出了两种具体实现形式以及算法步骤.实验表明,该动态模糊矢量量化算法在收敛速

K-means algorithm applied in vector quantization strongly depends on the selection of the initial codebook, and if not given a good initial codebook it can easily be trapped in local minima. Furthermore, Bezdek's fuzzy K-means algorithms are computationally expensive so that they are impractical in codebook design. So, people have been researching those algorithms which can achieve good performance in the convergent speed of algorithms and the quality of the reconstructed image. Analyzing the fuzzy vector quantization algorithm (FVQ) presented by Nicolaos.B.K. and aiming at the irrational convergent procedure of the algorithm, from the aspect of convergent structure and strategy the paper presents a dynamic fuzzy vector quantization algorithm (DFVQ) and gives two concrete methods based on the idea of the presented algorithm. Experiments show the presented methods markedly accelerate the convergent procedure and improve the quality of convergence.