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Fast optimal motion estimation based on gradient-based adaptive multilevel successive elimination

机译:基于梯度自适应多级连续消除的快速最优运动估计

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摘要

[[abstract]]In this paper, we propose a fast and optimal solution for block motion estimation based on an adaptive multilevel successive elimination algorithm. This algorithm is accomplished by applying a modified multilevel successive elimination algorithm (SEA) with the elimination order determined by the sum of the gradient magnitudes of each subblock and the elimination process terminated by comparing the above sum with a threshold. In addition a fast approximate motion estimation method and the accumulated distortion scheme are employed to make the proposed algorithm even more efficiently. Experimental results show that the proposed adaptive multilevel successive elimination strategy (AdaMSEA) algorithm significantly outperforms other previous optimal motion estimation algorithms, including SEA, MSEA, and FGSE on a wide variety of video sequences. Finally, we modify the proposed AdaMSEA to an approximate motion estimation algorithm to achieve very fast computational speed, and the experimental results show superior performance of this approximate algorithm over some fast motion estimation algorithms.
机译:[[摘要]]在本文中,我们提出了一种基于自适应多级连续消除算法的快速,最优的块运动估计解决方案。该算法是通过应用改进的多级连续消除算法(SEA)来实现的,消除顺序由每个子块的梯度大小之和确定,并且消除过程通过将上述总和与阈值进行比较而终止。另外,采用快速近似运动估计方法和累积失真方案来使所提出的算法更加有效。实验结果表明,所提出的自适应多级连续消除策略(AdaMSEA)算法在各种视频序列上均明显优于其他先前的最佳运动估计算法,包括SEA,MSEA和FGSE。最后,我们将提出的AdaMSEA修改为近似运动估计算法,以实现非常快的计算速度,并且实验结果表明,该近似算法优于某些快速运动估计算法。

著录项

  • 作者

    Shao-Wei Liu;

  • 作者单位
  • 年度 2012
  • 总页数
  • 原文格式 PDF
  • 正文语种 [[iso]]en
  • 中图分类

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