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

arXiv: https://arxiv.org/abs/2404.13452 · HTML: https://mlsanity.com/paper/2404.13452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [Deep Tone Mapping Operator for High Dynamic Range Images](https://mlsanity.com/paper/1908.04197/) (2019)
- [A Hitchhiker's Guide to Structural Similarity](https://mlsanity.com/paper/2101.06354/) (2021)
- [One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment](https://mlsanity.com/paper/2304.03412/) (2023)
- [FSITM: A Feature Similarity Index For Tone-Mapped Images](https://mlsanity.com/paper/1704.05624/) (2017)
- [FUNQUE: Fusion of Unified Quality Evaluators](https://mlsanity.com/paper/2202.11241/) (2022)
- [Making Video Quality Assessment Models Robust to Bit Depth](https://mlsanity.com/paper/2304.13092/) (2023)
- [Deep Reformulated Laplacian Tone Mapping](https://mlsanity.com/paper/2102.00348/) (2021)
- [Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information](https://mlsanity.com/paper/1810.08339/) (2018)
- [A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos](https://mlsanity.com/paper/2312.08524/) (2023)
- [Deep No-reference Tone Mapped Image Quality Assessment](https://mlsanity.com/paper/2002.03165/) (2020)
- [Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2403.15061/) (2024)

## Abstract (arXiv)

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.