Fast motion estimation algorithm combining search point sampling technique with adaptive search range algorithm

Y. Ko, Hyun-Soo Kang, Jae-Won Suh
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引用次数: 4

Abstract

This paper presents an enhanced fast motion estimation method where a search point sampling technique is combined with the adaptive search range algorithm (ASRA) based on the distribution of motion vector differences, which is our previous work. Since the ASRA is based on downsizing of search ranges for less computational complexity rather than sub-sampling of search points that is adopted by most of the fast algorithms, it results in smaller search areas where all points are considered as search points. Therefore, the conventional fast algorithms based on search point sampling techniques such as three-step search algorithm can be easily employed to the ASRA. As a result, we propose an algorithm where a part of the points within the search areas determined by the ASRA are sampled as the search points. Experimental results show that the proposed method reduces complexity of our ASRA by about 60% without quality degradation.
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结合搜索点采样技术和自适应搜索范围算法的快速运动估计算法
本文在前人工作的基础上,提出了一种基于运动矢量差分布的搜索点采样和自适应搜索范围算法相结合的增强快速运动估计方法。由于ASRA是基于缩小搜索范围以减少计算复杂度,而不是像大多数快速算法那样对搜索点进行子采样,因此它的搜索区域更小,所有的点都被认为是搜索点。因此,基于搜索点采样技术的传统快速算法,如三步搜索算法,可以很容易地应用于ASRA。因此,我们提出了一种算法,该算法将ASRA确定的搜索区域内的部分点作为搜索点进行采样。实验结果表明,该方法在不降低质量的前提下,将ASRA的复杂度降低了约60%。
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