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 paper, 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.

翻译：测量感知色差在现代智能手机摄影中具有重要意义。尽管色差测量历史悠久，但大多数色差度量方法受限于同质色块的心理物理数据或数量有限的简单自然摄影图像。因此，现有色差度量方法能否推广至内容复杂度更高、采用基于学习的图像信号处理器的智能手机摄影时代，仍存在疑问。本文构建了迄今为止最大的感知色差评估图像数据集，其中摄影图像的生成方式包括：1）由六款旗舰智能手机拍摄；2）通过Photoshop进行编辑；3）经智能手机内置滤镜后处理；4）使用错误的色彩配置文件复现。我们在严格受控的实验室环境下开展大规模心理物理实验，收集了30,000对图像对的感知色差数据。基于新建数据集，我们首次尝试构建基于轻量级神经网络的可端到端学习色差公式，该公式是多种先前度量的推广形式。大量实验表明，优化后的公式以较大优势优于33种现有色差度量方法，无需密集监督即可生成合理的局部色差图，对同质色块数据具有良好的泛化能力，并且在数学意义上经验性地表现为恰当的度量。我们的数据集和代码已公开发布于https://github.com/hellooks/CDNet。