  VMAF is a machine learning based video quality assessment method, originally
designed for streaming applications, which combines multiple quality metrics
and video features through SVM regression. It offers higher correlation with
subjective opinions compared to many conventional quality assessment methods.
In this paper we propose enhancements to VMAF through the integration of new
video features and alternative quality metrics (selected from a diverse pool)
alongside multiple model combination. The proposed combination approach enables
training on multiple databases with varying content and distortion
characteristics. Our enhanced VMAF method has been evaluated on eight HD video
databases, and consistently outperforms the original VMAF model (0.6.1) and
other benchmark quality metrics, exhibiting higher correlation with subjective
ground truth data.
