A Parallel Network for Compressed Video Enhancement

Abstract

Recent years, we have witnessed significant progress in the quality enhancement of compressed video by deep learning methods. In this paper, we propose an effective method for Video Quality Enhancement(VQE) task. Our method is realized via \textbf{A Parallel Network for Compressed Video Enhancement(PEN)}. To tackle optical flow estimates and complicated motion, PEN has two branches which are \textbf{Offset Deformable Fusion Network(ODFN)} and \textbf{Complex Motion Solution Network(CMSN)}. During the alignment stage, existing methods typically estimate optical flow for temporal motion compensation. However, because the compressed video may be severely distorted as a result of various compression artifacts, the estimated optical flow is typically inaccurate and unreliable. Therefore in ODFN we use deformable convolution to align frames in a fast and efficient way. At the same time, we adopt pyramidal processing and cascading refinement in CMSN which can address complex motions and large parallax problems in alignment. Furthermore, we use the target frame's neighbor Peak Quality frames(PQFs) as reference frames, which adjusts for video quality variations. Extensive experiments show that our method has improved the average video quality by 0.7 decibel.