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Low complexity motion estimation for long term memory motion compensation

机译:低复杂度运动估计,可进行长期记忆运动补偿

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Long term memory motion compensation (LTMC) is an approach to extend the temporal motion search range by using multiple decoded frames as reference frames. By employing multiple reference frames, LTMC reduces the residual frame energy significantly. However the computational complexity of motion estimation for LTMC increases significantly as well. Therefore reduction of the required computational complexity is one of the most challenging issues for LTMC. Also, if we locate motion search windows at fixed locations in a frame buffer for a given macro-block, it is highly possible that the oldest frames in a frame buffer do not contain matching blocks due to the reduced correlation between the current frame and the reference frames located further in the frame buffer. Therefore, if we can locate the motion search window at a good position adaptively in a frame buffer, we can enhance the gain performance of LTMS. In this paper, we propose a novel motion estimation algorithm for LTMC to reduce significantly the required computation complexity, and to enhance the performance of LTMC. For the proposed motion estimation algorithm, we introduce a directed search strategy. Also, we propose to employ hypothesis testing fast matching (HTFM) as a fast matching criterion. The goal of a directed search strategy is to let the location of the motion search windows change adaptively as the search proceeds to older frames in the frame buffer. The main benefit over standard, fixed window, approaches is that the algorithm can track larger motion and therefore, we can reduce the residual frame energy. In addition, because the directed search strategy keeps track of best matched blocks, we can reduce the computational complexity significantly by reducing the motion search window area in a frame buffer. Simulation results show that by employing the directed search with reduced motion search window, we can reduce the computational complexity approximately 30%-40%, and that by employing HTFM, we can reduce the computational complexity as additional 40%-50% as compared to the result of a full search algorithm which employs a non-directed search strategy for LTMC. The proposed algorithm provides particularly significant gains for video sequences which contain large motions. In the case of high motion sequences directed search also results in slight gains in PSNR over non-directed search.
机译:长期记忆运动补偿(LTMC)是一种通过使用多个解码帧作为参考帧来扩展时间运动搜索范围的方法。通过采用多个参考帧,LTMC大大降低了残留帧能量。但是,用于LTMC的运动估计的计算复杂性也显着增加。因此,降低所需的计算复杂度是LTMC最具挑战性的问题之一。另外,如果我们将运动搜索窗口定位在给定宏块的帧缓冲区中的固定位置,则由于当前帧与帧之间的相关性降低,因此很有可能帧缓冲区中最旧的帧不包含匹配块。参考帧位于帧缓冲区的更远处。因此,如果我们可以将运动搜索窗口自适应地定位在帧缓冲区中的合适位置,则可以增强LTMS的增益性能。在本文中,我们提出了一种新的LTMC运动估计算法,以显着降低所需的计算复杂度,并提高LTMC的性能。对于提出的运动估计算法,我们引入了定向搜索策略。此外,我们建议采用假设检验快速匹配(HTFM)作为快速匹配标准。定向搜索策略的目标是让运动搜索窗口的位置在搜索到帧缓冲区中的较旧帧时自适应地更改。与标准的固定窗口方法相比,主要优点是该算法可以跟踪较大的运动,因此可以减少残留的帧能量。另外,由于定向搜索策略跟踪最佳匹配的块,因此我们可以通过减少帧缓冲区中的运动搜索窗口面积来显着降低计算复杂性。仿真结果表明,通过使用具有减小的运动搜索窗口的定向搜索,我们可以将计算复杂度降低大约30%-40%,而通过使用HTFM,我们可以将计算复杂度降低40%-50%,与完全搜索算法的结果,该算法对LTMC采用非定向搜索策略。所提出的算法为包含大运动的视频序列提供了特别显着的增益。在高运动序列的情况下,有向搜索比无向搜索的PSNR略有增加。

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