LAUSR

Recently, video processing tasks, such as video enhancement and analysis, have received increasing attention from both academics and industries. However, the current video processing procedure on edge decouples the decoding phase and the subsequent video processing tasks, missing the opportunity to accelerate the procedure by orchestrating video decoding and enhancement stages. Thus, we propose an intelligent video processing workflow and architecture(IVP) for cloud-edge video streaming. For edge devices that receive compressed videos, IVP can perform direct DNN-based video enhancement, e.g., super-resolution and frame-interpolation. By leveraging the metadata motion vectors and residuals extracted from the encoded video, our architecture will significantly eliminate unnecessary frame pixels being processed by the DNNs and improve execution efficiency. The proposed IVP and workflow are proved to reduce up to 90% of the processing latency while producing accurate and high-quality videos. Furthermore, we observe a significant portion of similar optical flow in time domain of continuous videos, which can be used to reduce the computation overhead. Thus, to utilize such temporal similarity of optical flow, the proposed IVP is upgraded to be capable of reusing previous computation results, which further improves energy efficiency of the whole system.