FOVQA: Blind Foveated Video Quality Assessment
Yize Jin, Anjul Patney, Richard Webb, Alan Bovik

TL;DR
FOVQA is a novel no-reference foveated video quality assessment model that accounts for space-variant natural scene statistics, achieving state-of-the-art results in VR video quality evaluation.
Contribution
The paper introduces FOVQA, a new NR VQA model that incorporates space-variant NSS and NVS models for foveated video quality assessment in VR applications.
Findings
FOVQA outperforms existing models on the LIVE-FBT-FCVR database.
It effectively captures space-variant distortions in foveated videos.
The model is publicly available for research use.
Abstract
Previous blind or No Reference (NR) video quality assessment (VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Distortions from foveated video compression increase with increased eccentricity, implying that the natural scene statistics are space-variant. Towards advancing the development of foveated compression / streaming algorithms, we have devised a no-reference (NR) foveated video quality assessment model,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
