Measuring Perceptual Color Differences of Smartphone Photographs

作     者：Wang, Zhihua Xu, Keshuo Yang, Yang Dong, Jianlei Gu, Shuhang Xu, Lihao Fang, Yuming Ma, Kede 

作者机构：The Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The School of Information Management Jiangxi University of Finance and Economics Nanchang China The Guangdong OPPO Mobile Telecommunications Corp. Ltd. China The School of Computer Science and Engineering University of Electronic Science and Technology China The School of Digital Media and Art Design Hangzhou Dianzi University Hangzhou China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Smartphones 

摘      要：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://***/hellooks/CDNet. © 2022, CC BY.