基于时空域相关预测的步长自适应运动估计算法

Adaptive Estimation Algorithm Based on Temporal-spatial Correlation Prediction

为了更好地消除视频中空间和时间冗余,快速并有效地获得足够精度的运动矢量,本文提出一种改进的自适应十字搜索算法。本文算法利用时间空间域相关来预测当前块的运动矢量,对于视频的边缘图像采取固定小步长来进行十字搜索,对于图像的非边缘部分则采取由粗到精的方式进行搜索,搜索模板的自适应臂长为预测得到的目标运动矢量的横纵坐标的最大值。通过实验仿真比较传统的自适应十字搜索算法及其他几种经典的运动估计算法,结果表明本文算法增强了搜索预测的准确性,减少了平均每块搜索的次数,提高了搜索速率。

In order to eliminate spatial and temporal redundancy in video better and obtain motion vectors with sufficient accuracy quickly and efficiently, this paper proposes an improved adaptive rood pattern search algorithm. The algorithm uses temporal spatial domain correlation to predict the motion vector of the current block. For the edge image of the video, a fixed small step is used for cross search. For the non-edge part of the image, the search is performed from coarse to fine. The adaptive arm length is the maximum value of the horizontal and vertical coordinates of the predicted target motion vector. Compared with the traditional adaptive rood pattern search algorithm and several other classical motion estimation algorithms, the proposed algorithm enhances the accuracy of search prediction, reduces the average number of searches per block, and improves the search rate.