# Deep chroma compression of tone-mapped images

arXiv: https://arxiv.org/abs/2409.16032 · HTML: https://mlsanity.com/paper/2409.16032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020)
- [ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content](https://mlsanity.com/paper/1803.02266/) (2018)
- [HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions](https://mlsanity.com/paper/2007.01628/) (2020)
- [Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss](https://mlsanity.com/paper/2005.07335/) (2020)
- [Deep Sky Modeling for Single Image Outdoor Lighting Estimation](https://mlsanity.com/paper/1905.03897/) (2019)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [ColorVideoVDP: A visual difference predictor for image, video and display distortions](https://mlsanity.com/paper/2401.11485/) (2024)
- [Hybrid Loss for Learning Single-Image-based HDR Reconstruction](https://mlsanity.com/paper/1812.07134/) (2018)
- [Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content](https://mlsanity.com/paper/1803.04815/) (2018)
- [A Gamut-Mapping Framework for Color-Accurate Reproduction of HDR Images](https://mlsanity.com/paper/1711.08925/) (2017)

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

- [Human-Aligned Evaluation of a Pixel-wise DNN Color Constancy Model](https://mlsanity.com/paper/2602.13887/) (2026)

## Abstract (arXiv)

Acquisition of high dynamic range (HDR) images is thriving due to the increasing use of smart devices and the demand for high-quality output. Extensive research has focused on developing methods for reducing the luminance range in HDR images using conventional and deep learning-based tone mapping operators to enable accurate reproduction on conventional 8 and 10-bit digital displays. However, these methods often fail to account for pixels that may lie outside the target display's gamut, resulting in visible chromatic distortions or color clipping artifacts. Previous studies suggested that a gamut management step ensures that all pixels remain within the target gamut. However, such approaches are computationally expensive and cannot be deployed on devices with limited computational resources. We propose a generative adversarial network for fast and reliable chroma compression of HDR tone-mapped images. We design a loss function that considers the hue property of generated images to improve color accuracy, and train the model on an extensive image dataset. Quantitative experiments demonstrate that the proposed model outperforms state-of-the-art image generation and enhancement networks in color accuracy, while a subjective study suggests that the generated images are on par or superior to those produced by conventional chroma compression methods in terms of visual quality. Additionally, the model achieves real-time performance, showing promising results for deployment on devices with limited computational resources.