论文标题
基于差异的自适应极端视频完成
Divergence-Based Adaptive Extreme Video Completion
论文作者
论文摘要
极端的图像或视频完成，例如，我们仅保留1％的像素在随机位置，可以根据所需的预处理进行非常便宜的采样。然而，结果是对人类和介入算法都充满挑战的重建。我们建议将最先进的极端图像完成算法扩展到极端视频完成。我们根据颜色KL差异分析了一种适合极稀疏场景的颜色运动估计方法。我们的算法利用估计值在重建稀疏随机采样视频时适应其空间和时间过滤。我们使用重建PSNR和平均意见分数验证了50个公共可用视频的结果。
Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike. We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We analyze a color-motion estimation approach based on color KL-divergence that is suitable for extremely sparse scenarios. Our algorithm leverages the estimate to adapt between its spatial and temporal filtering when reconstructing the sparse randomly-sampled video. We validate our results on 50 publicly-available videos using reconstruction PSNR and mean opinion scores.
