ColorNet -- Estimating Colorfulness in Natural Images
This work addresses the need for accurate colorfulness measurement in image processing applications, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of measuring colorfulness in natural images by proposing ColorNet, the first deep learning-based metric for colorfulness estimation, which demonstrated superior performance over traditional methods both quantitatively and qualitatively.
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.