Spatio-Temporal Detail Information Retrieval for Compressed Video Quality Enhancement

The past few years have witnessed the great success of multi-frame quality enhancement for compressed video. Although the existing methods based on deformable alignment have achieved the state-of-the-art performance, they do not pay enough attention to the recovery of detail information. In this work, we propose a Spatio-Temporal Detail Retrieval (STDR) method to promote the recovery of detail information. To alleviate the problem of inaccurate deformable offsets caused by the fixed receptive field, motivated by multi-task learning, we design a plug-and-play Multi-path Deformable Alignment (MDA) module to generate more accurate offsets by integrating the alignment features of different receptive fields, so that the temporal detail information can be better recovered. For the spatial detail information restoration, several residual dense blocks with channel attention layer are utilized in the reconstruction module to explore valuable high-frequency spatial information from the fused multi-path alignment features. Meanwhile, a complementary loss function based on the Pearson correlation coefficient is developed to ameliorate the over-smoothing shortcoming caused by pixel-wise mean square or absolute value loss. Experimental results demonstrate that the proposed STDR network achieves superior performance compared with the state-of-the-art methods in both quantitative and qualitative evaluations.