Published September 29, 2021
| Version v1
Journal article
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Full UHD 360-Degree Video Dataset and Modeling ofRate-Distortion Characteristics and Head Movement Navigation
- 1. New Jersey Institute of Technology
- 2. University of Alabama
- 3. Adobe Research
- 4. University of Massachusetts Amherst
Description
We investigate the rate-distortion (R-D) characteristics of full ultrahigh definition (UHD) 360◦videos and capture corresponding head movement navigation data of virtual reality (VR) headsets. We use the navigation data to analyze how users explore the 360◦look-around panorama for such content and formulate related statistical models. The developed R-D characteristics and modeling capture the spatiotemporal encoding efficiency of the content at multiple scales and can be exploited to enable higher operational efficiency in key use cases. The high-quality expectations for next-generation immersive media necessitate the understanding of these intrinsic navigation and content characteristics of full UHD 360◦videos.
Notes
Additional funding source: NSF Award CNS-1901137
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Readme - Dataset info.txt
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Additional details
Funding
- CCSS: Collaborative Research: Ubiquitous Sensing for VR/AR Immersive Communication: A Machine Learning Perspective 1711592
- U.S. National Science Foundation
- The Future VR/AR Network -- Towards Virtual Human/Object Teleportation: NSF Workshop on Networked Virtual and Augmented Reality Communications 1821875
- U.S. National Science Foundation
- CIF: Small: Mobile Immersive Communication: View Sampling and Rate-Distortion Limits 1528030
- U.S. National Science Foundation
- ICE-T: RC: Millimeter Wave Communications and Edge Computing for Next Generation Tetherless Mobile Virtual Reality 1836909
- U.S. National Science Foundation