CC BY 4.0Müller, ChristophChristophMüllerSteglich, StephanStephanSteglichGroß, SandraSandraGroßKremer, PaulPaulKremer2023-08-082023-08-082023https://publica.fraunhofer.de/handle/publica/447728https://doi.org/10.24406/publica-174010.1145/3587819.359394110.24406/publica-17402-s2.0-85163578118This 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.enVMAFvideo qualityHDRneural networksmachine learningconference paper