在以往的运动图象分块运动匹配算法中，分块运动向量的起始搜索点是固定的。本文根据各分块运动向量的分布统计特性和相邻分块运动向量的相关特性，提出了一种基于预测起始搜索点的运动图象分块运动匹配新算法。通过用相邻分块运动向量的相互关系预测当前分块的起始搜索点，利用基于中心搜索模式的分块运动匹配算法寻找匹配的运动向量。实验表明，该算法预测步骤简单，令搜索更快接近全局极值，与其它快速分块运动匹配算法相比有效地降低了帧间运动补偿误差和所需搜索运算量，效果较好。

Most of the fast block matching algorithms(BMAs) use the origin as the initial center of the search. To improve the accuracy of the fast BMAs, a new predicted search algorithm is proposed in this paper. Based on the spatial correlation of motion and the fast center-based BMAs, the four causal neighbour blocks are chosen to predict the start searching point and then the center-based BMA is used to find the final motion vector as the global minimum is more close to the predicted center. Experimental results show that the proposed predicted search algorithm overdrives the performance very well in most fast BMAs such as the new three-step search, the four-step search in terms of motion compensation errors. In addition, the average computational requirement is also reduced.