Inverse-tone-mapped HDR video quality assessment: A new dataset and benchmark

Abstract

The accurate quality assessment for inverse-tone-mapped videos (ITMVs) has become increasingly important due to the ongoing rapid development of inverse tone mapping (ITM) algorithms. We observed that most existing video quality assessment (VQA) datasets and objective VQA methods are based on two underlying assumptions: the videos are in standard dynamic range (SDR) domain and the distortions are introduced in the video degradation process. However, ITMVs are videos in high dynamic range (HDR) domain, and are accompanied by distortions introduced during the enhancement process, which are distinct from those in degradation process. We then pose the question: Can existing VQA methods objectively assess the quality of ITMVs? To address this question and foster the advancement of VQA methods, we contribute the first publicly available dataset specifically tailored for assessing the quality of ITMVs. The dataset is called “VQA4ITM”, which is an acronym for “Video Quality Assessment for Inverse Tone Mapping”. Specifically, 30 distortion-free, professional 4K Rec. 2020 10 bit 50fps HDR source videos and 12 ITM methods were carefully selected, and a total of 360 ITMVs were generated in the end. A subjective experiment was conducted in a professional HDR viewing environment and 6,098 valid quality scores were finally gathered to calculate the mean opinion score (MOS) of each ITMV. Furthermore, we perform benchmark experiments on the VQA4ITM dataset, revealing that existing VQA methods are unable to fairly assess the quality of ITMVs. The inadequate performance of existing VQA methods on the VQA4ITM dataset highlights the challenging nature of our dataset, as well as the imperative need for the development of more effective VQA algorithms in the context of ITM. To facilitate future research, the VQA4ITM dataset will be made publicly accessible.