Zhihua Wang, Researcher

Measuring perceptual color differences (CDs) is of great importance in modern black smartphone photography. Despite their long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural 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 the largest image dataset for perceptual CD assessment to date}, in which the natural 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 color patch data, and empirically behaves as a proper metric in the mathematical sense.

Bio

Zhihua Wang earned his B.E. degree from the China University of Mining and Technology, Xuzhou,

China, in 2014, his M.S. degree from the National University of Defense Technology, Changsha,

China, in 2016, and his Ph.D. degree from the Department of Computer Science at the City University

of Hong Kong, Kowloon, Hong Kong, in 2022, under the guidance of Dr. Kede Ma. Currently, he works

as a PostDoc with Dr. Jing Liao, conducting research on perceptual image processing, computational

vision, computational photography, and multimedia forensics. During his Ph.D. training, his research

mainly focused on image quality assessment and image color difference assessment.

The Inter-Society Color Council advances the knowledge of color as it relates to art, science, industry and design.
Each of these fields enriches the others, furthering the general objective of color education.