A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos

• URL: http://arxiv.org/abs/2312.08524v1
• Date: Wed, 13 Dec 2023 21:24:00 GMT
• Title: A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos
• Authors: Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Alan C. Bovik
• Abstract summary: 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. Here, we show that an efficient class of video quality prediction models named FUNQUE+ achieves higher HDR video quality prediction accuracy at lower computational cost.
• Score: 36.26141980831573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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