A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos
Abhinau K. Venkataramanan Cosmin Stejerean Ioannis Katsavounidis Alan C. Bovik
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.