论文标题
EVRNET：边缘设备上有效的视频修复
EVRNet: Efficient Video Restoration on Edge Devices
论文作者
论文摘要
视频传播应用程序（例如，会议）正在获得动力，尤其是在全球健康大流行时期。视频信号是通过有损耗的渠道传输的，导致接收到低质量的信号。为了实时恢复接收者边缘设备上的视频，我们引入了一个高效的视频修复网络EVRNET。 EVRNET使用对齐，微分和融合模块有效地分配了网络中的参数。通过有关视频恢复任务（DEBLOCKING，DENOSISION和SUPER-LONESLOUSE）的广泛实验，我们证明EVRNET为具有较少参数和MAC的现有方法提供了竞争性能。例如，EVRNET的参数少260倍，而MAC的MAC比增强的基于可变形的视频恢复网络（EDVR）少4倍，其SSIM得分比EDVR少0.018。我们还评估了在看不见的数据集对多个变形下的EVRNET的性能，以证明其在相机和对象运动下都在建模可变长度序列中的能力。
Video transmission applications (e.g., conferencing) are gaining momentum, especially in times of global health pandemic. Video signals are transmitted over lossy channels, resulting in low-quality received signals. To restore videos on recipient edge devices in real-time, we introduce an efficient video restoration network, EVRNet. EVRNet efficiently allocates parameters inside the network using alignment, differential, and fusion modules. With extensive experiments on video restoration tasks (deblocking, denoising, and super-resolution), we demonstrate that EVRNet delivers competitive performance to existing methods with significantly fewer parameters and MACs. For example, EVRNet has 260 times fewer parameters and 958 times fewer MACs than enhanced deformable convolution-based video restoration network (EDVR) for 4 times video super-resolution while its SSIM score is 0.018 less than EDVR. We also evaluated the performance of EVRNet under multiple distortions on unseen dataset to demonstrate its ability in modeling variable-length sequences under both camera and object motion.
