# Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information

arXiv: https://arxiv.org/abs/1810.08339 · HTML: https://mlsanity.com/paper/1810.08339/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos](https://mlsanity.com/paper/2403.15061/) (2024)
- [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)

## Abstract (arXiv)

Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possible distortions in a tone-mapped image, information from different scales and different levels should be considered when predicting tone-mapped image quality. So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model. After being aggregated, the extracted features are mapped to quality predictions by regression. The proposed method is tested on the largest public database for TMIQA and compared to existing no-reference methods. The experimental results show that the proposed method achieves better performance.