# ColorNet -- Estimating Colorfulness in Natural Images

arXiv: https://arxiv.org/abs/1908.08505 · HTML: https://mlsanity.com/paper/1908.08505/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

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

- [SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution](https://mlsanity.com/paper/2201.04364/) (2022)
- [ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces](https://mlsanity.com/paper/2503.05247/) (2025)
- [Beyond Fixed Luminance: Towards Panchromatic and Orthochromatic Image Colorization](https://mlsanity.com/paper/2608.10798/) (2026)

## Abstract (arXiv)

Measuring the colorfulness of a natural or virtual scene is critical for many applications in image processing field ranging from capturing to display. In this paper, we propose the first deep learning-based colorfulness estimation metric. For this purpose, we develop a color rating model which simultaneously learns to extracts the pertinent characteristic color features and the mapping from feature space to the ideal colorfulness scores for a variety of natural colored images. Additionally, we propose to overcome the lack of adequate annotated dataset problem by combining/aligning two publicly available colorfulness databases using the results of a new subjective test which employs a common subset of both databases. Using the obtained subjectively annotated dataset with 180 colored images, we finally demonstrate the efficacy of our proposed model over the traditional methods, both quantitatively and qualitatively.