A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos
This work addresses the need for efficient quality assessment of compressed HDR videos, which is important for streaming services, but it is incremental as it builds on existing FUNQUE+ models.
The paper tackled the problem of assessing the quality of compressed High Dynamic Range (HDR) videos by proposing the FUNQUE+ models, which achieve state-of-the-art accuracy with lower computational cost compared to existing methods like HDRMAX and VMAF.
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.