A Simple Prediction Fusion Improves Data-driven Full-Reference Video Quality Assessment Models

Abstract

When developing data-driven video quality assessment algorithms, the size of the available ground truth subjective data may hamper the generalization capabilities of the trained models. Nevertheless, if the application context is known a priori, leveraging data-driven approaches for video quality prediction can deliver promising results. Towards achieving highperforming video quality prediction for compression and scaling artifacts, Netflix developed the Video Multi-method Assessment Fusion (VMAF) Framework, a full-reference prediction system which uses a regression scheme to integrate multiple perceptionmotivated features to predict video quality. However, the current version of VMAF does not fully capture temporal video features relevant to temporal video distortions. To achieve this goal, we developed Ensemble VMAF (E-VMAF): a video quality predictor that combines two models: VMAF and predictions based on entropic differencing features calculated on video frames and frame differences. We demonstrate the improved performance of E-VMAF on various subjective video databases. The proposed model will become available as part of the open source package in https://github. com/Netflix/vmaf.