  We consider the problem of capturing distortions arising from changes in
frame rate as part of Video Quality Assessment (VQA). Variable frame rate (VFR)
videos have become much more common, and streamed videos commonly range from 30
frames per second (fps) up to 120 fps. VFR-VQA offers unique challenges in
terms of distortion types as well as in making non-uniform comparisons of
reference and distorted videos having different frame rates. The majority of
current VQA models require compared videos to be of the same frame rate, but
are unable to adequately account for frame rate artifacts. The recently
proposed Generalized Entropic Difference (GREED) VQA model succeeds at this
task, using natural video statistics models of entropic differences of temporal
band-pass coefficients, delivering superior performance on predicting video
quality changes arising from frame rate distortions. Here we propose a simple
fusion framework, whereby temporal features from GREED are combined with
existing VQA models, towards improving model sensitivity towards frame rate
distortions. We find through extensive experiments that this feature fusion
significantly boosts model performance on both HFR/VFR datasets as well as
fixed frame rate (FFR) VQA databases. Our results suggest that employing
efficient temporal representations can result much more robust and accurate VQA
models when frame rate variations can occur.
