  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.
