Motion estimation using long-term motion vector prediction
Motion estimation using long-term motion vector prediction
复制标题
使用长期运动矢量预测的运动估计
DOI：
10.1109/dcc.1999.785688
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
R. Ward
中科院分区：
文献类型：
--
作者：
I. Ismaeil;A. Docef;F. Kossentini;R. Ward
仅给出摘要表格。本文提出了一种基于长期时间预测的视频序列编码运动估计技术。使用投影方法从一帧到另一帧跟踪移动对象的运动矢量。追踪的运动矢量用作运动估计搜索算法的起点。所使用的运动估计算法基于最佳快速块匹配算法。空间和时间预测的组合也被用来获得当前宏块的运动矢量的更准确的估计。不准确预测的运动矢量会对运动估计算法产生显着的负面影响。它可以迫使搜索算法陷入局部最小值，或者花费不必要的计算来找到最佳运动矢量。使用可靠性测量来估计预测运动矢量的精度，该可靠性测量允许运动搜索算法决定是否使用时间运动矢量预测器、空间运动矢量预测器或两者。作为可靠性测量，我们使用当前帧和前一帧中跟踪的运动矢量之间的均方误差。如果运动矢量属于恒速运动物体，则可靠性度量会增加。如果可靠性度量小于某个阈值，则我们放弃时间预测并使用空间预测。如果两种预测方法都失败，我们放弃快速运动搜索并在低分辨率图像中执行全搜索运动估计。实验结果表明，长期预测可将运动搜索算法执行的计算量减少多达 20%，同时获得基本相同的质量。
Summary form only given. This paper presents a motion estimation technique for the coding of video sequences that is based on long-term temporal prediction. The motion vector of a moving object is tracked from one frame to another using a projection method. The traced motion vector is used as a starting point for the motion estimation search algorithm. The motion estimation algorithm used is based on an optimum fast block matching algorithm. Combinations of both spatial and temporal prediction are also used to obtain a more accurate estimate of the motion vector of the current macroblock. An inaccurately predicted motion vector can have a significant negative impact on the motion estimation algorithm. It can force the search algorithm to be trapped in a local minimum, or to spend unnecessary computations to find the optimum motion vector. The accuracy of the predicted motion vector is estimated using a reliability measure that allows the motion search algorithm to decide whether to use temporal motion vector predictor, spatial motion vector predictor, or both. As a reliability measure we used the mean squared error between the traced motion vectors in the current frame and in the previous frame. The reliability measure increases if the motion vector belongs to a moving object with constant speed. If the reliability measure is smaller than a certain threshold, then we abandon the temporal prediction and use spatial prediction. If both prediction methods fail, we abandon the fast motion search and perform full-search motion estimation in the low-resolution images. The experimental results show that long-term prediction reduces the number of computations performed by the motion search algorithm by up to 20%, while obtaining essentially the same quality.