Towards Omniscient Feature Alignment for Video Rescaling

Video super-resolution often reconstructs high-resolution (HR) video from low-resolution (LR) video that has been downsampled using predefined methods, which is an ill-posedness problem. Recent video rescaling algorithms alleviate this problem by jointly training the downsampling and upsampling processes. However, they primarily exploit the shallow temporal correlations among video frames, overlooking the intricate, long-term sequential depth dependencies within the video. In this paper, we propose an omniscient feature alignment to leverage the bidirectional deep temporal information for video rescaling, namely OFA-VRN. In the downsampling phase, the proposed method separates the input HR video into LR frames and high-frequency components using haar wavelet transform and explicitly embeds the high-frequency components into the LR frames. In this way, detailed information is stored in the frame and maintains visual perception quality in downsampled videos. During the upsampling phase, we use an advanced bidirectional propagation paradigm to enhance temporal information aggregation capabilities. By incorporating the proposed omniscient feature alignment, the network is capable of leveraging multi-frame feature information from the triplet dimension to further alleviate misalignment issues, thereby enhancing its capacity for deep temporal information utilization. The experiments on Vid4 and Vimeo90K-T demonstrate that our model achieves competitive performance compared to the state-of-the-art methods.