A Spatio-Temporal Decomposition Network for Compressed Video Quality Enhancement

Compressed video quality enhancement has always been a widely concerned research. However, existing methods rarely build models from the consideration of object motion diversity and feature frequency distribution. In this paper, we propose a Spatio-Temporal Decomposition Network (STDN) to reduce the compressed distortion with motion classification and frequency separation. In the temporal domain, a novel deformable convolution is designed to estimate the various motion offsets of different categories objects, and then the adjacent frame features would be accurately fused with them. In the spatial domain, a frequency decomposition module is first proposed to decompose the features of different frequencies and process them with appropriate precision. Experiments show that our method can surpass all the existing methods in both of subjective and objective aspects, and achieve the performance of state-of-the-art.