论文标题
恢复在社交媒体上共享的用户视频
Restoration of User Videos Shared on Social Media
论文作者
论文摘要
在社交媒体平台上共享的用户视频通常遭受由未知专有处理程序引起的降解，这意味着它们的视觉质量比原件差。本文提出了一个新的一般视频修复框架，用于恢复社交媒体平台上共享的用户视频。与执行端到端映射的大多数基于深度学习的视频恢复方法相反，在该方法中，特征提取大部分被视为黑匣子，这是某种意义上的意义，即功能通常未知的角色，我们的新方法通过自适应退化感应（投票）称为视频恢复（投票），介绍了降级图（DFM）的概念，以实现视频恢复程序的概念。具体而言，对于每个视频框架，我们首先会自适应地估算其DFM以提取代表难以恢复其不同区域的功能。然后，我们将DFM馈送到卷积神经网络（CNN）中，以计算层次降解功能，以调节端到端视频恢复骨干网络，从而明确地将更多的关注引起到潜在的难以恢复领域的可能性，从而导致增强的恢复性能。我们将解释投票框架的设计基本原理，并提出广泛的实验结果，以表明新的投票方法在定量和定性上都优于各种最新技术。此外，我们还为在不同社交媒体平台上共享的用户视频的大规模现实世界数据库提供了贡献。代码和数据集可从https://github.com/luohongming/votes.git获得
User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms. In contrast to most deep learning-based video restoration methods that perform end-to-end mapping, where feature extraction is mostly treated as a black box, in the sense that what role a feature plays is often unknown, our new method, termed Video restOration through adapTive dEgradation Sensing (VOTES), introduces the concept of a degradation feature map (DFM) to explicitly guide the video restoration process. Specifically, for each video frame, we first adaptively estimate its DFM to extract features representing the difficulty of restoring its different regions. We then feed the DFM to a convolutional neural network (CNN) to compute hierarchical degradation features to modulate an end-to-end video restoration backbone network, such that more attention is paid explicitly to potentially more difficult to restore areas, which in turn leads to enhanced restoration performance. We will explain the design rationale of the VOTES framework and present extensive experimental results to show that the new VOTES method outperforms various state-of-the-art techniques both quantitatively and qualitatively. In addition, we contribute a large scale real-world database of user videos shared on different social media platforms. Codes and datasets are available at https://github.com/luohongming/VOTES.git
