  The popularity of streaming videos with live, high-action content has led to
an increased interest in High Frame Rate (HFR) videos. In this work we address
the problem of frame rate dependent Video Quality Assessment (VQA) when the
videos to be compared have different frame rate and compression factor. The
current VQA models such as VMAF have superior correlation with perceptual
judgments when videos to be compared have same frame rates and contain
conventional distortions such as compression, scaling etc. However this
framework requires additional pre-processing step when videos with different
frame rates need to be compared, which can potentially limit its overall
performance. Recently, Generalized Entropic Difference (GREED) VQA model was
proposed to account for artifacts that arise due to changes in frame rate, and
showed superior performance on the LIVE-YT-HFR database which contains frame
rate dependent artifacts such as judder, strobing etc. In this paper we propose
a simple extension, where the features from VMAF and GREED are fused in order
to exploit the advantages of both models. We show through various experiments
that the proposed fusion framework results in more efficient features for
predicting frame rate dependent video quality. We also evaluate the fused
feature set on standard non-HFR VQA databases and obtain superior performance
than both GREED and VMAF, indicating the combined feature set captures
complimentary perceptual quality information.
