  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.
