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A fast motion estimation algorithm based on adaptive pattern and search priority

机译:基于自适应模式和搜索优先级的快速运动估计算法

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摘要

Block matching algorithm (BMA) has been widely used in motion estimation for various video coding standards since it can remove temporal redundancy effectively. However, motion estimation is the key problem in realizing real-time video coding due to the high computation complexity of BMA. In this manuscript, we present a fast motion estimation algorithm according to the adaptive pattern and search priority (APSP). Based on the distribution characteristics of motion vector (MV) that achieved by a series of experiments, the improved algorithm defines different efficient patterns and adopts the appropriate pattern adaptively. Firstly, the search can be stopped after checking one point by the features of the current block. And then the starting pattern is determined based on the motion vectors from the neighboring blocks. The subsequent pattern can be further adjusted according to the current best matching point. Furthermore, the proposed method assigns search priority to each point of every pattern. Therefore, the search is performed under the guidance of the search priority, with the result that each pattern can be interrupted in any position by using priority and threshold. Compared to conventional fast algorithms, the experimental results demonstrate that the proposed algorithm improves the performance of the search algorithm with significant reduction in computational complexity on the premise of ensuring the image quality and searching precision.
机译:块匹配算法(BMA)已被广泛用于各种视频编码标准的运动估计中,因为它可以有效地消除时间冗余。然而,由于BMA的高计算复杂度,运动估计是实现实时视频编码的关键问题。在此手稿中,我们根据自适应模式和搜索优先级(APSP)提出了一种快速运动估计算法。基于一系列实验获得的运动矢量(MV)的分布特征,改进的算法定义了不同的有效模式并自适应地采用了适当的模式。首先,可以通过当前块的功能检查一个点后停止搜索。然后基于来自相邻块的运动矢量来确定起始模式。可以根据当前的最佳匹配点进一步调整后续图案。此外,所提出的方法将搜索优先级分配给每个模式的每个点。因此,在搜索优先级的指导下执行搜索,结果是可以通过使用优先级和阈值在每个位置中断每个模式。与传统的快速算法相比,实验结果表明,该算法在保证图像质量和搜索精度的前提下,提高了搜索算法的性能,并显着降低了计算复杂度。

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