EVASR: Edge-Based Salience-Aware Super-Resolution for Enhanced Video Quality and Power Efficiency

With the rapid growth of video content consumption, it is important to deliver high-quality streaming videos to users even under limited available network bandwidth. In this article, we propose EVASR, a system that performs edge-based video delivery to clients with salience-aware super-resolution. We select patches with higher saliency score to perform super-resolution while applying the simple yet efficient bicubic interpolation for the remaining patches in the same video frame. To efficiently use the computation resources available at the edge server, we introduce a new metric called “saliency visual quality” (SVQ) and formulate patch selection as an optimization problem to achieve the best performance when an edge server is serving multiple users. We implement EVASR based on the FFmpeg framework and deploy it on three different platforms including desktop/laptop computers, mobile phones, and single board computers (SBCs). We conduct extensive experiments for evaluating the visual quality, super-resolution speed, and power savings that can be achieved by EVASR. Results show that EVASR outperforms baseline approaches in both resource efficiency and visual quality metrics including PSNR, SVQ, and VMAF. EVASR can also achieve substantial energy savings compared to baseline approaches MobileSR and JetsonSR on mobile devices.