一种结合空间预测和CDS的快速块匹配算法

A Hybrid Method for Combining Spatial Prediction with the CDS Algorithm

运动估计是根据视频序列中时间上相关的信息估计场景或目标的二维运动向量场的过程．因为块运动估计的简单性和有效性,它已经成为目前使用最广泛的运动估计方法．本文设计了一种结合空间预测和CDS的快速块匹配算法．若当前块和相邻块的运动相似,则选择相邻块的运动向量中使当前块的匹配误差最小的一个作为当前块运动向量的预测估计,再以该预测值为中心,比较SDSP上搜索点的块匹配误差．若当前块和相邻块的运动不相关,则采用CDS算法从原点开始搜索运动向量．实验结果表明,本文设计的算法兼顾了搜索速率和精度,相比N3SS、DS、HEXBS、CDS、CDHS算法,更好地适用于超分辨率图像复原．

Motion estimation refers to estimating 2-D motion vector field of the scene or object according to temporal information redundancy in a clipped video. Because of its simplicity and efficiency, block-based motion estimation has recently been widely used. This paper proposes a hybrid method for combining spatial prediction with the CDS algorithm. If the motion of the current block is similar to that of its neighbor blocks, we choose the best candidate block from the neighbor blocks and use its motion vector to form an initial estimate for the current block. The neighbor block whose motion vector yields the minimum block distortion is called the best candidate block. The true motion vector is then obtained by comparing the search points of SDSP centered at the initial estimate. If the current block is not correlated with its spatial neighbors, we search for the motion vector from the origin of the search window using the CDS algorithm. Experimental results show that the proposed algorithm achieves a better tradeoff between search speed and accuracy for super resolution restoration than N3SS, DS, HEXBS, CDS, and CDHS.