# Perceptual Tone Mapping Model for High Dynamic Range Imaging

arXiv: https://arxiv.org/abs/2309.16975 · HTML: https://mlsanity.com/paper/2309.16975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Tone Mapping Operator for High Dynamic Range Images](https://mlsanity.com/paper/1908.04197/) (2019)
- [FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging](https://mlsanity.com/paper/2201.02625/) (2022)
- [FSITM: A Feature Similarity Index For Tone-Mapped Images](https://mlsanity.com/paper/1704.05624/) (2017)
- [Learning a self-supervised tone mapping operator via feature contrast masking loss](https://mlsanity.com/paper/2110.09866/) (2021)
- [Which tone-mapping operator is the best? A comparative study of perceptual quality](https://mlsanity.com/paper/1601.04450/) (2016)

## Built on by (most cited first, 1 of 1)

- [Evaluating Vision-Language Models for Image Quality Assessment using Psychophysical Data](https://mlsanity.com/paper/2603.24578/) (2026)

## Abstract (arXiv)

One of the key challenges in tone mapping is to preserve the perceptual quality of high dynamic range (HDR) images when mapping them to standard dynamic range (SDR) displays. Traditional tone mapping operators (TMOs) compress the luminance of HDR images without considering the surround and display conditions emanating into suboptimal results. Current research addresses this challenge by incorporating perceptual color appearance attributes. In this work, we propose a TMO (TMOz) that leverages CIECAM16 perceptual attributes, i.e., brightness, colorfulness, and hue. TMOz accounts for the effects of both the surround and the display conditions to achieve more optimal colorfulness reproduction. The perceptual brightness is compressed, and the perceptual color scales, i.e., colorfulness and hue are derived from HDR images by employing CIECAM16 color adaptation equations. A psychophysical experiment was conducted to automate the brightness compression parameter. The model employs fully automatic and adaptive approach, obviating the requirement for manual parameter selection. TMOz was evaluated in terms of contrast, colorfulness and overall image quality. The objective and subjective evaluation methods revealed that the proposed model outperformed the state-of-the-art TMOs.