  High frame rate videos are increasingly getting popular in recent years,
driven by the strong requirements of the entertainment and streaming industries
to provide high quality of experiences to consumers. To achieve the best
trade-offs between the bandwidth requirements and video quality in terms of
frame rate adaptation, it is imperative to understand the effects of frame rate
on video quality. In this direction, we devise a novel statistical entropic
differencing method based on a Generalized Gaussian Distribution model
expressed in the spatial and temporal band-pass domains, which measures the
difference in quality between reference and distorted videos. The proposed
design is highly generalizable and can be employed when the reference and
distorted sequences have different frame rates. Our proposed model correlates
very well with subjective scores in the recently proposed LIVE-YT-HFR database
and achieves state of the art performance when compared with existing
methodologies.
