ColorNet -- Estimating Colorfulness in Natural Images

TL;DR

This paper introduces a novel deep learning-based metric for estimating the colorfulness of natural images, addressing dataset limitations and outperforming traditional methods.

Contribution

It presents the first deep learning model for colorfulness estimation and a method to create an annotated dataset by combining existing databases.

Findings

Deep learning model outperforms traditional metrics

Effective dataset augmentation via database alignment

Quantitative and qualitative validation of the proposed method

Abstract

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.