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Fast motion vector estimation by using spatiotemporal correlation of motion field

机译:利用运动场的时空相关性快速估计运动矢量

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Abstract: Motion vector (MV) estimation plays an important role in motion compensated video coding. In this research, we first examine a stochastic MV model which enables us to exploit the strong correlation of MVs in both spatial and temporal domains in a given image sequence. Then, a new fast stochastic block matching algorithm (SBMA) is proposed. The basic idea is to select a set of good MV candidates and choose from them the one which satisfies a certain spatio-temporal correlation rule. The proposed algorithm reduces matching operations to about 2% of that of the full block matching algorithm (FBMA) with only 2% increase of the sum of absolute difference (SAD) in motion compensated residuals. The excellent performance of the new algorithm is supported by extensive experimental results.!10
机译:摘要:运动矢量(MV)估计在运动补偿视频编码中起着重要作用。在这项研究中,我们首先研究了随机MV模型,该模型使我们能够利用给定图像序列中时空域中MV的强相关性。然后,提出了一种新的快速随机块匹配算法(SBMA)。基本思想是选择一组好的MV候选者,并从中选择满足一定时空相关性规则的候选者。所提出的算法将运动匹配残差的绝对差之和(SAD)仅增加2%,从而将匹配操作减少到全块匹配算法(FBMA)的2%左右。大量的实验结果证明了该新算法的出色性能。!10

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