论文标题
BMBC：双边运动估算，视频插值的双边成本量
BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
论文作者
论文摘要
视频插值通过合成两个连续帧之间的中间帧来增加视频序列的时间分辨率。我们提出了一种基于双边运动估计的新型基于学习的视频插值算法。首先，我们开发了双边运动网络，具有双边成本量，以准确估计双侧运动。然后，我们近似双向运动以预测不同的双侧运动。然后，我们使用估计的双侧运动扭曲两个输入帧。接下来，我们开发动态过滤器生成网络以产生动态混合过滤器。最后，我们使用动态混合过滤器将扭曲的帧组合在一起，以生成中间帧。实验结果表明，所提出的算法优于几个基准数据集上的最新视频插值算法。
Video interpolation increases the temporal resolution of a video sequence by synthesizing intermediate frames between two consecutive frames. We propose a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation. First, we develop the bilateral motion network with the bilateral cost volume to estimate bilateral motions accurately. Then, we approximate bi-directional motions to predict a different kind of bilateral motions. We then warp the two input frames using the estimated bilateral motions. Next, we develop the dynamic filter generation network to yield dynamic blending filters. Finally, we combine the warped frames using the dynamic blending filters to generate intermediate frames. Experimental results show that the proposed algorithm outperforms the state-of-the-art video interpolation algorithms on several benchmark datasets.
