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Due to the limitation of shooting conditions in the real world, there exist many insufficient-resolution videos. To be transmitted under limited bandwidth conditions, low-resolution videos often have to be compressed further, which introduces more compression artifacts and results in severer damage to the video quality. Accordingly, video super-resolution in practical applications must repair compressed damage and down-sampling damage simultaneously, which can be intuitively solved by two subtasks, including compressed video quality enhancement (VQE) and video super-resolution (VSR). Therefore, we proposed a novel model, the Feature Multiplexing Video Super-Resolution for Compressed Video (FM-VSR) to effectively handle such multi-tasks. Firstly, we use a complicated Deformable Regression Pyramid (DRP) module to align the reference frame and each supporting frame. Then feature multiplexing structure is utilized in VSR to make better use of the VQE information. Finally, a skip-attention structure is introduced in the reconstruction module to predict the high-quality video frame. Many experiments on benchmark datasets show that our method can achieve better performance both in quantitative and qualitative than the state-of-the-art (SOTA) two-stage approaches.

FM-VSR: Feature Multiplexing Video Super-Resolution for Compressed Video