An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning
Nguyễn Việt Hùng, Thu Ngan Dao, Ngoc Son Pham, Tran Long Dang, Trung Dung Nguyen, Trương Thu Hương
Abstract
Nowadays, Virtual Reality is becoming more and more popular, and 360 video is a very important part of the system. 360 video transmission over the Internet faces many difficulties due to its large size. Therefore, to reduce the network bandwidth requirement of 360-degree video, Viewport Adaptive Streaming (VAS) was proposed. An important issue in VAS is how to estimate future user viewing direction. In this paper, we propose an algorithm called GLVP (GRU-LSTM-based-Viewport-Prediction) to estimate the typical view for the VAS system. The results show that our method can improve viewport estimation from 9.5% to near 20%compared with other methods.