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Reinforcement Learning Driven Adaptive VR Streaming with Optical Flow Based QoE by Quan, Wei; Pan, Yuxuan; Xiang, Bin; Zhang, Lin is a scholarly article available to read on EtoBox.

What is Reinforcement Learning Driven Adaptive VR Streaming with Optical Flow Based QoE about?

With the merit of containing full panoramic content in one camera, Virtual Reality (VR) and 360-degree videos have attracted more and more attention in the field of industrial cloud manufacturing and training. Industrial Internet of Things (IoT), where many VR terminals needed to be online at the same time, can hardly guarantee VR's bandwidth requirement. However, by making use of users' quality of experience (QoE) awareness factors, including the relative moving speed and depth difference between the viewpoint and other content, bandwidth consumption can be reduced. In this paper, we propose OFB-VR (Optical Flow Based VR), an interactive method of VR streaming that can make use of VR users' QoE awareness to ease the bandwidth pressure. The Just-Noticeable Difference through Optical Flow Estimation (JND-OFE) is explored to quantify users' awareness of quality distortion in 360-degree videos. Accordingly, a novel 360-degree videos QoE metric based on PSNR and JND-OFE (PSNR-OF) is proposed. With the help of PSNR-OF, OFB-VR proposes a versatile-size tiling scheme to lessen the tiling overhead. A Reinforcement Learning(RL) method is implemented to make use of historical data to perform

Author

Quan, Wei; Pan, Yuxuan; Xiang, Bin; Zhang, Lin

Published

2020

Language

EN