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Robust global motion estimation using motion vectors of variable size blocks and automatic motion model selection

机译:使用可变大小块的运动矢量和自动运动模型选择进行可靠的全局运动估计

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A new approach for highly robust and precise global motion estimation (GME) using motion vectors (MVs) is presented. We show that this approach obtains precise higher-order short-term motion parameters for global motion using motion vectors solely. The approach is general and works for different mathematical methods including least-squares and Newton-Raphson method. We show that the approach is suitable for fixed block sizes from plain full-search block-matching as well as for arbitrary block sizes from video streams compressed with H.264/AVC reference encoder. The proposed approach is compared against four other known MV-based GME (MV-GME) methods. Our results show that the approach is significantly more robust and obtains higher precision for global motion parameters in terms of background motion compensation, especially if moving objects occur. In addition, the results are as good as results from precise pixel-based GME methods or even better while the presented MV-GME methods have very low computational costs.
机译:提出了一种使用运动矢量(MV)的高度健壮和精确的全局运动估计(GME)的新方法。我们表明,此方法仅使用运动矢量即可获得用于全局运动的精确的高阶短期运动参数。该方法是通用的,并且适用于不同的数学方法,包括最小二乘法和Newton-Raphson方法。我们展示了该方法适用于纯全搜索块匹配中的固定块大小,以及适用于使用H.264 / AVC参考编码器压缩的视频流中的任意块大小。将提出的方法与其他四种已知的基于MV的GME(MV-GME)方法进行了比较。我们的结果表明,该方法在背景运动补偿方面,特别是在发生移动物体的情况下,对于全局运动参数而言明显更鲁棒,并且可以获得更高的精度。此外,结果与基于精确像素的GME方法的结果一样好,甚至更好,而提出的MV-GME方法的计算成本却非常低。

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