Machine-learning based VMAF prediction for HDR video content

Abstract

This paper presents a methodology for predicting VMAF video quality scores for high dynamic range (HDR) video content using machine learning. To train the ML model, we are collecting a dataset of HDR and converted SDR video clips, as well as their corresponding objective video quality scores, specifically the Video Multimethod Assessment Fusion (VMAF) values. A 3D convolutional neural network (3D-CNN) model is being trained on the collected dataset. Finally, a hands-on demonstrator is developed to showcase the newly predicted HDR-VMAF metric in comparison to VMAF and other metric values for SDR content, and to conduct further validation with user testing.