# Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos

arXiv: https://arxiv.org/abs/2403.15061 · HTML: https://mlsanity.com/paper/2403.15061/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 86 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [Deep Tone Mapping Operator for High Dynamic Range Images](https://mlsanity.com/paper/1908.04197/) (2019)
- [A Simple Model for Subject Behavior in Subjective Experiments](https://mlsanity.com/paper/2004.02067/) (2020)
- [Subjective Assessment of High Dynamic Range Videos Under Different Ambient Conditions](https://mlsanity.com/paper/2209.10005/) (2022)
- [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)
- [Deep No-reference Tone Mapped Image Quality Assessment](https://mlsanity.com/paper/2002.03165/) (2020)

## Built on by (most cited first, 3 of 3)

- [Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content](https://mlsanity.com/paper/2404.13484/) (2024)
- [Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2404.13452/) (2024)
- [LumaFlux: Lifting 8-Bit Worlds to HDR Reality with Physically-Guided Diffusion Transformers](https://mlsanity.com/paper/2604.02787/) (2026)

## Abstract (arXiv)

High Dynamic Range (HDR) videos are able to represent wider ranges of contrasts and colors than Standard Dynamic Range (SDR) videos, giving more vivid experiences. Due to this, HDR videos are expected to grow into the dominant video modality of the future. However, HDR videos are incompatible with existing SDR displays, which form the majority of affordable consumer displays on the market. Because of this, HDR videos must be processed by tone-mapping them to reduced bit-depths to service a broad swath of SDR-limited video consumers. Here, we analyze the impact of tone-mapping operators on the visual quality of streaming HDR videos. To this end, we built the first large-scale subjectively annotated open-source database of compressed tone-mapped HDR videos, containing 15,000 tone-mapped sequences derived from 40 unique HDR source contents. The videos in the database were labeled with more than 750,000 subjective quality annotations, collected from more than 1,600 unique human observers. We demonstrate the usefulness of the new subjective database by benchmarking objective models of visual quality on it. We envision that the new LIVE Tone-Mapped HDR (LIVE-TMHDR) database will enable significant progress on HDR video tone mapping and quality assessment in the future. To this end, we make the database freely available to the community at https://live.ece.utexas.edu/research/LIVE_TMHDR/index.html