Edge-Oriented Compressed Video Super-Resolution

Abstract

Due to the proliferation of video data in Internet of Things (IoT) systems, in order to reduce the data burden, most social media platforms typically employ downsampling to reduce the resolution of high-resolution (HR) videos before video coding. Consequently, the loss of detail and the introduction of additional artifacts seriously compromise the quality of experience (QoE). Recently, the task of compressive video super-resolution (CVSR) has garnered significant attention, aiming to simultaneously eliminate compression artifacts and enhance the resolution of compressed videos. In this paper, we propose an edge-oriented compressed video super-resolution network (EOCVSR), which focuses on reconstructing higher-quality details, to effectively address the CVSR task. Firstly, we devised a motion-guided alignment module (MGAM) to achieve precise bi-direction motion compensation in a multi-scale manner. Secondly, we introduced an edge-oriented recurrent block (EORB) to reconstruct edge information by combining the merits of explicit and implicit edge extraction. In addition, benefiting from the recurrent structure, the receptive field of EOCVSR can be enhanced and the features can be effectively refined without introducing additional parameters. Extensive experiments conducted on benchmark datasets demonstrate that our method surpasses the performance of state-of-the-art (SOTA) approaches in both quantitative and qualitative evaluations. Our approach can provide users with high-quality and cost-effective HR videos by integrating with sensors and codecs.