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Distance scaling of higher order motion parameters in an extension of HEVC

机译:HEVC扩展中高阶运动参数的距离缩放

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Complex motion in video sequences, such as rotation and scaling, conventionally approximated translationally, can more efficiently be represented by higher order motion models. An important contribution to the efficiency of the higher order motion compensation approach is the prediction and encoding of the additional motion parameters. The additional data rate caused by an increased amount of motion parameters has to be kept small for the higher order motion model to outperform the translational one within blocks of complex motion. Addressing an aspects of motion prediction yet to be adjusted to higher order motion, this paper proposes an advanced method of distance scaling of higher order motion parameters. Just as translational motion vector predictors require scaling whenever the block inheriting them has a different distance to its reference picture than the block predicted from, higher order motion parameter prediction can profit from such scaling as well. However, the distance scaling of higher order motion parameters cannot be applied to the elements of the motion model transformation matrix directly, but is performed on separate higher order motion components provided by a preceding transformation matrix decomposition. Accordingly, the objective of this work is to introduce and evaluate a procedure for higher order motion distance scaling. The resulting rate reduction of about 2% on average and up to over 6% attains a further improvement on the higher order motion compensation in an extension of HEVC.
机译:视频序列中的复杂运动(例如旋转和平移和缩放)通常可以平移地近似地由高阶运动模型来更有效地表示。对高阶运动补偿方法的效率的重要贡献是附加运动参数的预测和编码。对于较高阶的运动模型,在复杂运动的块内,平移运动的性能要优于平移运动,因此,必须将由运动参数增加引起的附加数据速率保持较小。针对尚未调整为高阶运动的运动预测的一个方面,本文提出了一种高阶运动参数的距离缩放的高级方法。正如平移运动矢量预测变量每当继承它们的块到其参考图片的距离与从其预测的块到参考图像的距离不同时就需要缩放一样,更高阶的运动参数预测也可以从这种缩放中受益。但是,高阶运动参数的距离缩放不能直接应用于运动模型变换矩阵的元素,而是在由先前的变换矩阵分解提供的单独的高阶运动分量上执行。因此,这项工作的目的是介绍和评估用于高阶运动距离缩放的过程。由此产生的速率平均降低约2%,最高降低超过6%,这是HEVC扩展中的高阶运动补偿的进一步改进。

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