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Enhancement of Pixel-based Video Quality Models using Meta-data


 Current state-of-the-art pixel-based video quality models for 4K resolution do not have access to explicit meta information such as resolution and framerate and may not include implicit or explicit features that model the related effects on perceived video quality. In this paper,
 we propose a meta concept to extend state-of-the-art pixel-based models and develop hybrid models incorporating meta-data such as framerate and resolution. Our general approach uses machine learning to incooperate the meta-data to the overall video quality prediction. To this aim, in our study,
 we evaluate various machine learning approaches such as SVR, random forest, and extreme gradient boosting trees in terms of their suitability for hybrid model development. We use VMAF to demonstrate the validity of the meta-information concept. Our approach was tested on the publicly available
 AVT-VQDB-UHD-1 dataset. We are able to show an increase in the prediction accuracy for the hybrid models in comparison with the prediction accuracy of the underlying pixel-based model. While the proof-of-concept is applied to VMAF, it can also be used with other pixel-based models.
