  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, called FOVQA, which
is based on new models of space-variant natural scene statistics (NSS) and
natural video statistics (NVS). Specifically, we deploy a space-variant
generalized Gaussian distribution (SV-GGD) model and a space-variant
asynchronous generalized Gaussian distribution (SV-AGGD) model of mean
subtracted contrast normalized (MSCN) coefficients and products of neighboring
MSCN coefficients, respectively. We devise a foveated video quality predictor
that extracts radial basis features, and other features that capture
perceptually annoying rapid quality fall-offs. We find that FOVQA achieves
state-of-the-art (SOTA) performance on the new 2D LIVE-FBT-FCVR database, as
compared with other leading FIQA / VQA models. we have made our implementation
of FOVQA available at: http://live.ece.utexas.edu/research/Quality/FOVQA.zip.
