Paper Info
Title
Fixation Prediction for 360° Video Streaming in Head-Mounted Virtual Reality
Abstract
We study the problem of predicting the Field-of-Views (FoVs) of viewers watching 360° videos using commodity Head-Mounted Displays (HMDs). Existing solutions either use the viewer's current orientation to approximate the FoVs in the future, or extrapolate future FoVs using the historical orientations and dead-reckoning algorithms. In this paper, we develop fixation prediction networks that concurrently leverage sensor- and content-related features to predict the viewer fixation in the future, which is quite different from the solutions in the literature. The sensor-related features include HMD orientations, while the content-related features include image saliency maps and motion maps. We build a 360° video streaming testbed to HMDs, and recruit twenty-five viewers to watch ten 360° videos. We then train and validate two design alternatives of our proposed networks, which allows us to identify the better-performing design with the optimal parameter settings. Trace-driven simulation results show the merits of our proposed fixation prediction networks compared to the existing solutions, including: (i) lower consumed bandwidth, (ii) shorter initial buffering time, and (iii) short running time.
Year
DOI
Venue
2017
10.1145/3083165.3083180
NOSSDAV
Field
DocType
ISBN
Virtual reality,Computer science,Salience (neuroscience),Video streaming,Testbed,Real-time computing,Bandwidth (signal processing)
Conference
978-1-4503-5003-7
Citations 
PageRank 
References 
31
1.03
21
Authors
6
Name
Order
Citations
PageRank
Ching-Ling Fan1738.04
Jean Lee2582.10
Wen-Chih Lo3623.20
Chun-Ying Huang420718.22
Kuan-Ta Chen51896136.86
Cheng-Hsin Hsu699181.56