  Perceptual video quality assessment models are either frame-based or
video-based, i.e., they apply spatiotemporal filtering or motion estimation to
capture temporal video distortions. Despite their good performance on video
quality databases, video-based approaches are time-consuming and harder to
efficiently deploy. To balance between high performance and computational
efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF)
framework, which integrates multiple quality-aware features to predict video
quality. Nevertheless, this fusion framework does not fully exploit temporal
video quality measurements which are relevant to temporal video distortions. To
this end, we propose two improvements to the VMAF framework: SpatioTemporal
VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video
features which are fed into a single or multiple regression models. To train
our models, we designed a large subjective database and evaluated the proposed
models against state-of-the-art approaches. The compared algorithms will be
made available as part of the open source package in
https://github.com/Netflix/vmaf.
