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Novel True-Motion Estimation Algorithm and Its Application to Motion-Compensated Temporal Frame Interpolation

机译:新颖的真运动估计算法及其在运动补偿时间帧插值中的应用

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In this paper, a new low-complexity true-motion estimation (TME) algorithm is proposed for video processing applications, such as motion-compensated temporal frame interpolation (MCTFI) or motion-compensated frame rate up-conversion (MCFRUC). Regular motion estimation, which is often used in video coding, aims to find the motion vectors (MVs) to reduce the temporal redundancy, whereas TME aims to track the projected object motion as closely as possible. TME is obtained by imposing implicit and/or explicit smoothness constraints on the block-matching algorithm. To produce better quality-interpolated frames, the dense motion field at interpolation time is obtained for both forward and backward MVs; then, bidirectional motion compensation using forward and backward MVs is applied by mixing both elegantly. Finally, the performance of the proposed algorithm for MCTFI is demonstrated against recently proposed methods and smoothness constraint optical flow employed by a professional video production suite. Experimental results show that the quality of the interpolated frames using the proposed method is better when compared with the MCFRUC techniques.
机译:本文针对视频处理应用提出了一种新的低复杂度真实运动估计(TME)算法,例如运动补偿时间帧插值(MCTFI)或运动补偿帧速率上转换(MCFRUC)。经常在视频编码中使用的常规运动估计旨在找到运动矢量(MV)以减少时间冗余,而TME则旨在尽可能紧密地跟踪投影的对象运动。通过在块匹配算法上施加隐式和/或显式平滑约束来获得TME。为了产生更好质量的内插帧,对于前向和后向MV都获得了内插时的密集运动场。然后,通过巧妙地混合使用前向和后向MV进行双向运动补偿。最后,针对最近提出的方法和专业视频制作套件采用的平滑度约束光流,证明了所提出的MCTFI算法的性能。实验结果表明,与MCFRUC技术相比,该文提出的插值帧质量更好。

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