Details

Autor(en) / Beteiligte

• Yang, Qin
• Zou, Junni
• Tang, Kexin
• Li, Chenglin
• Xiong, Hongkai

Titel

Single and Sequential Viewports Prediction for 360-Degree Video Streaming

Ist Teil von

• 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019, p.1-5

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Sending only the viewport of interest provides a solution for 360-degree video streaming under the current bandwidth-constrained infrastructure. To this end, the user viewport requires to be prefetched in advance by conducting viewport prediction. To more accurately capture the nonlinear and long-term dependent relation between the future and past viewports, we develop a single viewport prediction model using convolutional neural network (CNN), in which the pooling layers are dropped and more convolutional layers are added for stronger nonlinear fitting ability. Further, we design a viewport trajectory prediction model based on recurrent neural network (RNN) which learns long-term dependency in sequential viewports. Specially, it is capable to estimate future viewport trajectory and support variable-size prediction window with low complexity. Finally, a correlation filter-based viewport tracker (CFVT) is proposed to perform content-aware viewport prediction. The combination of the RNN and the CFVT through a fusion model enables them to complement each other which is validated by significant improvement in prediction accuracy.