Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos

TL;DR

This paper introduces Cut-FUNQUE, an objective quality model that accurately predicts the visual quality of tone-mapped and compressed HDR videos, addressing challenges in streaming to SDR displays.

Contribution

We propose a novel, efficient quality model specifically designed for tone-mapped and compressed HDR videos, achieving state-of-the-art accuracy on a large-scale database.

Findings

Cut-FUNQUE outperforms existing models in predicting video quality.

The model is efficient and suitable for real-time streaming scenarios.

It demonstrates high correlation with human subjective assessments.

Abstract

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.