ColorNet: Estimating colorfulness in natural images

9th May 2019
ColorNet: Estimating colorfulness in natural images

This page contains related materials for learning-based colorfulness estimation.


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.


The implementation of the proposed model can be downloaded here.

Please kindly cite our paper in your publication if you use the proposed model:

  • E. Zerman, A. Rana, A. Smolic. “ColorNet – Estimating colorfulness in natural images.” IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, September 2019.
  author       = {Zerman, Emin and Rana, Aakanksha and Smolic, Aljosa},
  title        = {{ColorNet} - Estimating colorfulness in natural images},
  booktitle    = {International Conference on Image Processing ({ICIP})},
  month        = {Sept},
  year         = {2019},
  publisher    = {IEEE}


This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under the Grant Number 15/RP/2776.


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