Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos
arxiv(2024)
摘要
High Dynamic Range (HDR) videos have enjoyed a surge in popularity in recent
years due to their ability to represent a wider range of contrast and color
than Standard Dynamic Range (SDR) videos. Although HDR video capture has seen
increasing popularity because of recent flagship mobile phones such as Apple
iPhones, Google Pixels, and Samsung Galaxy phones, a broad swath of consumers
still utilize legacy SDR displays that are unable to display HDR videos. As
result, HDR videos must be processed, i.e., tone-mapped, before streaming to a
large section of SDR-capable video consumers. However, server-side tone-mapping
involves automating decisions regarding the choices of tone-mapping operators
(TMOs) and their parameters to yield high-fidelity outputs. Moreover, these
choices must be balanced against the effects of lossy compression, which is
ubiquitous in streaming scenarios. In this work, we develop a novel, efficient
model of objective video quality named Cut-FUNQUE that is able to accurately
predict the visual quality of tone-mapped and compressed HDR videos. Finally,
we evaluate Cut-FUNQUE on a large-scale crowdsourced database of such videos
and show that it achieves state-of-the-art accuracy.
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