Recent years have seen steady growth in the popularity and availability of High Dynamic Range (HDR) content, particularly videos, streamed over the internet. As a result, assessing the subjective quality of HDR videos, which are generally subjected to compression, is of increasing importance. In particular, we target the task of full-reference quality assessment of compressed HDR videos. The state-of-the-art (SOTA) approach HDRMAX involves augmenting off-the-shelf video quality models, such as VMAF, with features computed on non-linearly transformed video frames. However, HDRMAX increases the computational complexity of models like VMAF. Here, we show that an efficient class of video quality prediction models named FUNQUE+ achieves SOTA accuracy. This shows that the FUNQUE+ models are flexible alternatives to VMAF that achieve higher HDR video quality prediction accuracy at lower computational cost.
@article{arxiv.2312.08524,
title = {A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos},
author = {Abhinau K. Venkataramanan and Cosmin Stejerean and Ioannis Katsavounidis and Alan C. Bovik},
journal= {arXiv preprint arXiv:2312.08524},
year = {2023}
}