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A Triangulation-Based Backward Adaptive Motion Field Subsampling Scheme

机译:基于三角剖分的向后自适应运动场回忆方案

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Optical flow procedures are used to generate dense motion fields which approximate true motion. Such fields contain a large amount of data and if we need to transmit such a field, the raw data usually exceeds the raw data of the two images it was computed from. In many scenarios, however, it is of interest to transmit a dense motion field efficiently. Most prominently this is the case in inter prediction for video coding. In this paper we propose a transmission scheme based on subsampling the motion field. Since a field which was subsampled with a regularly spaced pattern usually yields suboptimal results, we propose an adaptive subsampling algorithm that preferably samples vectors at positions where changes in motion occur. The subsampling pattern is fully reconstructable without the need for signaling of position information. We show an average gain of 2.95 dB in average end point error compared to regular subsampling. Furthermore we show that an additional prediction stage can improve the results by an additional 0.43 dB, gaining 3.38 dB in total.
机译:光学流程用于生成近似真实运动的密集运动场。这些字段包含大量数据,如果需要传输这样的字段,则原始数据通常超过从其计算的两个图像的原始数据。然而,在许多情况下,有效地传输密集的运动场是有意思的。最显着的是视频编码帧间预测的情况。在本文中,我们提出了一种基于对运动场的传递方案的传输方案。由于具有规则间隔模式的限制的字段通常产生次优效果,因此我们提出了一种自适应数据采样算法,其优选地在运动发生变化的位置处的样品向量。如果需要对位置信息的信令完全重建,则限制图案完全重建。与常规限制相比,我们在平均终点错误中显示平均增益2.95 dB。此外,我们表明额外的预测阶段可以通过额外的0.43 dB改善结果,总共增加3.38 dB。

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