Spatio-temporal feature learning for enhancing video quality based on screen content characteristics

With the rising demands for remote desktops and online meetings, screen content videos have drawn significant attention. Different from natural videos, screen content videos often exhibit scene switches where the content abruptly changes from one frame to the next. These scene switches result in obvious distortions in compressed videos. Besides, frame freezing, where the content remains unchanged for a certain duration, is also very common in screen content videos. Existing alignment-based models struggle to effectively enhance scene switch frames and lack efficiency when dealing with frame freezing situations. Therefore, we propose a novel alignment-free method that effectively handles both scene switches and frame freezing. In our approach, we develop a spatial and temporal feature extraction module that compresses and extracts spatio-temporal information from three groups of frame inputs. This enables efficient handling of scene switches. In addition, an edge aware block is proposed for extracting edge information, which guides the model to focus on restoring the high-frequency components in frame freezing situations. The fusion module is then designed to adaptively fuse the features from three groups, considering different positions of video frames, to enhance frames during scene switch and frame freezing scenarios. Experimental results demonstrate the significant advancements achieved by the proposed edge aware with spatio-temporal information fusion network (EAST) in enhancing the quality of compressed videos, surpassing the current state-of-the-art methods.