  We consider the problem of conducting frame rate dependent video quality
assessment (VQA) on videos of diverse frame rates, including high frame rate
(HFR) videos. More generally, we study how perceptual quality is affected by
frame rate, and how frame rate and compression combine to affect perceived
quality. We devise an objective VQA model called Space-Time GeneRalized
Entropic Difference (GREED) which analyzes the statistics of spatial and
temporal band-pass video coefficients. A generalized Gaussian distribution
(GGD) is used to model band-pass responses, while entropy variations between
reference and distorted videos under the GGD model are used to capture video
quality variations arising from frame rate changes. The entropic differences
are calculated across multiple temporal and spatial subbands, and merged using
a learned regressor. We show through extensive experiments that GREED achieves
state-of-the-art performance on the LIVE-YT-HFR Database when compared with
existing VQA models. The features used in GREED are highly generalizable and
obtain competitive performance even on standard, non-HFR VQA databases. The
implementation of GREED has been made available online:
https://github.com/pavancm/GREED
