Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of smartphone photography characterized by greater content complexities and learning-based image signal processors. In this article, we put together so far the largest image dataset for perceptual CD assessment, in which the photographic images are 1) captured by six flagship smartphones, 2) altered by Photoshop, 3) post-processed by built-in filters of the smartphones, and 4) reproduced with incorrect color profiles. We then conduct a large-scale psychophysical experiment to gather perceptual CDs of 30,000 image pairs in a carefully controlled laboratory environment. Based on the newly established dataset, we make one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics. Extensive experiments demonstrate that the optimized formula outperforms 33 existing CD measures by a large margin, offers reasonable local CD maps without the use of dense supervision, generalizes well to homogeneous color patch data, and empirically behaves as a proper metric in the mathematical sense. Our dataset and code are publicly available at https://github.com/hellooks/CDNet.

Measures for visual color differences (CDs) are pivotal in hardware and software upgrading of modern smartphone photography. Towards this goal, we construct currently the largest database for visual CDs of smartphone photography. Our database consists of 15, 335 natural images 1) captured by six latest flagship smartphones, 2) altered by Photoshop®, 3) post-processed by built-in filters of smartphones, and 4) reproduced with incorrect color profiles. Moreover, we conduct a large-scale psychophysical experiment to gather visual CDs of 30, 000 image pairs from 20 human subjects in a well-designed laboratory environment. Last, we apply our human-rated database to compare a total of 27 classical and recent CD metrics. We show that existing metrics are limited in assessing CDs of smartphone photography, and point out promising future directions of learning-based CD metrics.