Research Article
An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning
@ARTICLE{10.4108/eetinis.v9i4.2218, author={Hung Nguyen and Thu Ngan Dao and Ngoc Son Pham and Tran Long Dang and Trung Dung Nguyen and Thu Huong Truong}, title={An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={INIS}, year={2022}, month={9}, keywords={}, doi={10.4108/eetinis.v9i4.2218} }- Hung Nguyen
Thu Ngan Dao
Ngoc Son Pham
Tran Long Dang
Trung Dung Nguyen
Thu Huong Truong
Year: 2022
An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning
INIS
EAI
DOI: 10.4108/eetinis.v9i4.2218
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.
Copyright © 2022 Nguyen Viet Hung et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
