Online video super-resolution (online-VSR) highly relies on an effective alignment module to aggregate temporal information, while the strict latency requirement makes accurate and efficient alignment very challenging. Though much progress has been achieved, most of the existing online-VSR methods estimate the motion fields of each frame separately to perform alignment, which is computationally redundant and ignores the fact that the motion fields of adjacent frames are correlated. In this work, we propose an efficient Temporal Motion Propagation (TMP) method, which leverages the continuity of motion field to achieve fast pixel-level alignment among consecutive frames. Specifically, we first propagate the offsets from previous frames to the current frame, and then refine them in the neighborhood, which significantly reduces the matching space and speeds up the offset estimation process. Furthermore, to enhance the robustness of alignment, we perform spatial-wise weighting on the warped features, where the positions with more precise offsets are assigned higher importance. Experiments on benchmark datasets demonstrate that the proposed TMP method achieves leading online-VSR accuracy as well as inference speed. The source code of TMP can be found at https://github.com/xtudbxk/TMP.

翻译：在线视频超分辨率（online-VSR）高度依赖有效的对齐模块来聚合时序信息，而严格的延迟要求使得精确且高效的对齐极具挑战性。尽管已有许多进展，但现有的大多数在线VSR方法单独估计每一帧的运动场以进行对齐，这不仅在计算上存在冗余，还忽视了相邻帧运动场之间的关联性。本文提出了一种高效的时序运动传播（Temporal Motion Propagation, TMP）方法，通过利用运动场的连续性实现连续帧间的快速像素级对齐。具体而言，我们首先将前一帧的偏移传播到当前帧，随后在邻域内对其进行细化，从而显著缩小匹配空间并加速偏移估计过程。此外，为增强对齐的鲁棒性，我们对变形特征进行空间加权，将更高的重要性赋予偏移更精确的位置。在基准数据集上的实验表明，所提出的TMP方法在在线VSR精度和推理速度上均达到了领先水平。TMP的源代码可在 https://github.com/xtudbxk/TMP 获取。