  The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently
emerged as a state-of-the-art approach to video quality prediction, that now
pervades the streaming and social media industry. However, since VMAF requires
the evaluation of a heterogeneous set of quality models, it is computationally
expensive. Given other advances in hardware-accelerated encoding, quality
assessment is emerging as a significant bottleneck in video compression
pipelines. Towards alleviating this burden, we propose a novel Fusion of
Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing
and by using a transform that is sensitive to visual perception to boost
accuracy. Further, we expand the FUNQUE framework to define a collection of
improved low-complexity fused-feature models that advance the state-of-the-art
of video quality performance with respect to both accuracy, by 4.2\% to 5.3\%,
and computational efficiency, by factors of 3.8 to 11 times!
