research-article

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Enabling Designers to Foresee Which Colors Users Cannot See

Authors:

Katharina Reinecke,

David R. Flatla,

Christopher BrooksAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 2693 - 2704

https://doi.org/10.1145/2858036.2858077

Published: 07 May 2016 Publication History

Abstract

Users frequently experience situations in which their ability to differentiate screen colors is affected by a diversity of situations, such as when bright sunlight causes glare, or when monitors are dimly lit. However, designers currently have no way of choosing colors that will be differentiable by users of various demographic backgrounds and abilities and in the wide range of situations where their designs may be viewed. Our goal is to provide designers with insight into the effect of real-world situational lighting conditions on people's ability to differentiate colors in applications and imagery. We therefore developed an online color differentiation test that includes a survey of situational lighting conditions, verified our test in a lab study, and deployed it in an online environment where we collected data from around 30,000 participants. We then created ColorCheck, an image-processing tool that shows designers the proportion of the population they include (or exclude) by their color choices.

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https://dl.acm.org/doi/10.1145/3657242.3657243
Beaudouin-Lafon ME JXia H(2023)Color Field: Developing Professional Vision by Visualizing the Effects of Color FiltersProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606828(1-16)Online publication date: 29-Oct-2023
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Index Terms

Enabling Designers to Foresee Which Colors Users Cannot See
1. Human-centered computing

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cover image ACM Conferences

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
Program Chairs:
Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

Copyright © 2016 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 07 May 2016

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CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

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https://dl.acm.org/doi/10.1145/3657242.3657243
Beaudouin-Lafon ME JXia H(2023)Color Field: Developing Professional Vision by Visualizing the Effects of Color FiltersProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606828(1-16)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606828
Tseng CQuadri GWang ZSzafir D(2023)Measuring Categorical Perception in Color-Coded ScatterplotsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581416(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581416
Meenakshi SSingla A(2023)A novel heuristic method for quantitative assessment of web accessibility for colorblindUniversal Access in the Information Society10.1007/s10209-023-01006-wOnline publication date: 19-Jun-2023
https://doi.org/10.1007/s10209-023-01006-w
Berndt WGaussoin R(2023)Predicting Munsell color for turfgrass leavesCrop Science10.1002/csc2.2084363:3(1566-1580)Online publication date: 24-Jan-2023
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Chintalapati SBragg JWang L(2022)A Dataset of Alt Texts from HCI PublicationsProceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3517428.3544796(1-12)Online publication date: 23-Oct-2022
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Gabbard JSmith MMerenda CBurnett GLarge D(2022)A Perceptual Color-Matching Method for Examining Color Blending in Augmented Reality Head-Up Display GraphicsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.304471528:8(2834-2851)Online publication date: 1-Aug-2022
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Bajammal MStocco AMazinanian DMesbah A(2022)A Survey on the Use of Computer Vision to Improve Software Engineering TasksIEEE Transactions on Software Engineering10.1109/TSE.2020.303298648:5(1722-1742)Online publication date: 1-May-2022
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Sandnes F(2021)Inverse Color Contrast Checker: Automatically Suggesting Color Adjustments that meet Contrast Requirements on the WebProceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3441852.3476529(1-4)Online publication date: 17-Oct-2021
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Nardini PChen MBöttinger MScheuermann GBujack R(2021)Automatic Improvement of Continuous Colormaps in Euclidean ColorspacesComputer Graphics Forum10.1111/cgf.1431340:3(361-373)Online publication date: 29-Jun-2021
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