Recurrent Deformable Fusion for Compressed Video Artifact Reduction.

The compressed video inevitably appears in compression artifacts, which seriously affect the Quality of Experience. The state-of-the-art methods employ deformable alignment to gather similar information from multiple neighborhood frames to enhance target frame quality. However, they always align multiple frames to the target frame simultaneously, which brings repetitive and useless information because of multiple and imperfect alignments. In this paper, we propose a recurrent deformable fusion method which considers the alignment quality distortion caused by time distance from the target frame. Specifically, a Deformable Alignment (DA) module aligns each pair of the target frame and an adjacent frame following the time line. At the same time, a Recurrent Fusion (RF) module integrates the current aligned feature with the previous fused feature. After that, the fused features are concatenated along the time line. Then, a Multi-Scale Attention Reconstruction (MSAR) module is proposed to gather useful information from the fused features. Compared with the previous multi-frame alignment approach, our method can avoid obtaining a lot of repetitive and useless information. Experiment results confirm that our method achieves state-of-the-art performance on the standard test sequences.