Enhancing VMAF through New Feature Integration and Model Combination
This work addresses video quality assessment for streaming applications, representing an incremental improvement over existing methods.
The authors tackled the problem of improving video quality assessment by enhancing VMAF through new feature integration and model combination, resulting in consistently higher correlation with subjective ground truth across eight HD video databases compared to the original VMAF and other benchmarks.
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.