Enabling Designers to Foresee Which Colors Users Cannot See

CHI, pp. 2693-2704, 2016.

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Keywords:
Color Differentiabilityonline color differentiationDesign Softwareenviron­ mentprocessing toolMore(11+)
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We developed WebCDT, an online color differentiation test, verified that it is sensitive to changes in situational lighting conditions, and deployed it online

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 background...More

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Introduction
  • The inability to see on-screen content due to challenging lighting conditions is a commonly experienced but largely unexplored phenomenon.
  • Previous work has shown that the color vision can be substantially distorted by a variety of situational lighting conditions, such as different combinations of bright.
  • Contact the Owner/Author.
  • Copyright is held by the owner/author(s).
  • Http://dx.doi.org/10.1145/2858036.2858077 sunlight, artificial lighting, glossy screens, and monitor set­ tings [37] and that these factors can reduce perceived color contrast, inhibiting readability and information uptake [14]
  • CHI’16, May 07–12, 2016, San Jose, CA, USA ACM 978-1-4503-3362-7/16/05. http://dx.doi.org/10.1145/2858036.2858077 sunlight, artificial lighting, glossy screens, and monitor set­ tings [37] and that these factors can reduce perceived color contrast, inhibiting readability and information uptake [14]
Highlights
  • The inability to see on-screen content due to challenging lighting conditions is a commonly experienced but largely unexplored phenomenon
  • Previous work has shown that our color vision can be substantially distorted by a variety of situational lighting conditions, such as different combinations of bright
  • Range of Devices and Monitor Settings Asked about the type of monitor, most participants re­ ported that they were using built-in laptop monitors (47.33%), 26.48% used PCs with external monitors, and 26.24% were using monitors integrated in tablet computers or mobile phones
  • SUMMARY AND DISCUSSION While color choices have been the subject of much prior work, it has previously been impossible to foresee the con­ sequences of these choices in increasingly diverse digital en­ vironments
  • We developed WebCDT, an online color differentiation test, verified that it is sensitive to changes in situational lighting conditions, and deployed it online
  • To evaluate the effect of our population’s wide range of color differentiation abilities on viewing colors in digital environ­ ments, we developed an image-processing tool that can be used by designers
Methods
  • To be able to recruit and study a large and diverse popula­ tion, the authors deployed the WebCDT on the online experimentation platform LabintheWild.
  • In­ stead of receiving financial compensation, participants were able to test their “color age”: Upon completion of the study, The experiment concluded with a participant demographic questionnaire, including their country of residence, gender, age, highest education level, and whether they have any known color vision deficiencies or corrected-to-normal vi­ sion.
Results
  • Range of Devices and Monitor Settings Asked about the type of monitor, most participants re­ ported that they were using built-in laptop monitors (47.33%), 26.48% used PCs with external monitors, and 26.24% were using monitors integrated in tablet computers or mobile phones
  • This suggests that at least 73.6% of participants were using monitors adjustable in tilt-angle, which could result in varying color vision abilities even throughout the test.
Conclusion
  • SUMMARY AND DISCUSSION

    While color choices have been the subject of much prior work, it has previously been impossible to foresee the con­ sequences of these choices in increasingly diverse digital en­ vironments.
  • The goal of this work was to quantify how many users cannot differentiate colors in a given user interface or image and varying situa­ tional lighting conditions.
  • The authors developed WebCDT, an online color differentiation test, verified that it is sensitive to changes in situational lighting conditions, and deployed it online.
  • Applying ColorCheck to commonly viewed digi­ tal content, the authors showed that 88% of the participants are unable to differentiate some colors in websites and infographics.
  • 10% of the participants can only distinguish around 50% of the colors in an average website/infographic
Summary
  • Introduction:

    The inability to see on-screen content due to challenging lighting conditions is a commonly experienced but largely unexplored phenomenon.
  • Previous work has shown that the color vision can be substantially distorted by a variety of situational lighting conditions, such as different combinations of bright.
  • Contact the Owner/Author.
  • Copyright is held by the owner/author(s).
  • Http://dx.doi.org/10.1145/2858036.2858077 sunlight, artificial lighting, glossy screens, and monitor set­ tings [37] and that these factors can reduce perceived color contrast, inhibiting readability and information uptake [14]
  • CHI’16, May 07–12, 2016, San Jose, CA, USA ACM 978-1-4503-3362-7/16/05. http://dx.doi.org/10.1145/2858036.2858077 sunlight, artificial lighting, glossy screens, and monitor set­ tings [37] and that these factors can reduce perceived color contrast, inhibiting readability and information uptake [14]
  • Objectives:

    The goal of this work was to quantify how many users cannot differentiate colors in a given user interface or image and varying situa­ tional lighting conditions.
  • Methods:

    To be able to recruit and study a large and diverse popula­ tion, the authors deployed the WebCDT on the online experimentation platform LabintheWild.
  • In­ stead of receiving financial compensation, participants were able to test their “color age”: Upon completion of the study, The experiment concluded with a participant demographic questionnaire, including their country of residence, gender, age, highest education level, and whether they have any known color vision deficiencies or corrected-to-normal vi­ sion.
  • Results:

    Range of Devices and Monitor Settings Asked about the type of monitor, most participants re­ ported that they were using built-in laptop monitors (47.33%), 26.48% used PCs with external monitors, and 26.24% were using monitors integrated in tablet computers or mobile phones
  • This suggests that at least 73.6% of participants were using monitors adjustable in tilt-angle, which could result in varying color vision abilities even throughout the test.
  • Conclusion:

    SUMMARY AND DISCUSSION

    While color choices have been the subject of much prior work, it has previously been impossible to foresee the con­ sequences of these choices in increasingly diverse digital en­ vironments.
  • The goal of this work was to quantify how many users cannot differentiate colors in a given user interface or image and varying situa­ tional lighting conditions.
  • The authors developed WebCDT, an online color differentiation test, verified that it is sensitive to changes in situational lighting conditions, and deployed it online.
  • Applying ColorCheck to commonly viewed digi­ tal content, the authors showed that 88% of the participants are unable to differentiate some colors in websites and infographics.
  • 10% of the participants can only distinguish around 50% of the colors in an average website/infographic
Tables
  • Table1: Regression model analyzing the influence of situational lighting conditions, devices, and demographics on par­ ticipants’ ellipsoid volumes, R2 = .06, p < .0001, F(8,4077) = 33.15, p < .0001
  • Table2: Comparison of situational lighting conditions and demographics of the 10% participants with the worst color differentiability, and the remaining 90%. Tests of the equality of two proportions report on Pearson’s chi-squared test
Download tables as Excel
Related work
  • The ability to differentiate colors is vital for information up­ take in many areas, from reading maps and understanding in­ formation visualizations, to the interpretation of medical im­ agery. However, color differentiability can be severely im­ pacted by a number of situational factors, such as monitor settings and lighting conditions [36] or perceptual abilities [8, 12]. Crucially, the effects of insufficiently differentiable col­ ors can range from frustration to critical safety issues [8].

    Effects of Situational Lighting Factors on Color Vision Prior work has mentioned the importance of adjusting mon­ itors and lighting in order to optimize viewing conditions. Ware [36, p.90], for example, suggests ensuring that “the room should have a standard light level and illuminant color” and that “only a minimal amount of light should be allowed to fall on the monitor screen” in order to perceive computergenerated colors similarly to colors in a room. This is of course difficult to achieve for users with handheld devices and laptops. Liu et al [29] investigated the impact of am­ bient lighting on such handheld devices (tablets and mobile phones) used in the medical domain. They found that ambient lighting conditions (simulating dark, office, and outdoor envi­ ronments) had a significant effect on visual task performance on mobile displays with participants performing best in dark conditions. Their findings support other research that has found the perceived image quality on mobile phone screens to decrease when ambient brightness levels were increased [23, 27, 28, 16]. Recently, Kim et al [22] addressed the diffi­ culties around knowing which colors online fashion products have when viewed in different lighting conditions by develop­ ing an approach to crowdsource color perception. By gener­ ating a “CrowdColor”, their system approximates the “real” color by averaging users’ color perception. The study used to evaluate CrowdColor was conducted in lab using two con­ trolled lighting conditions and two different mobile devices; the current version of CrowdColor is therefore “limited to a controlled environment only” [22, p.483].
Funding
  • Our goal is to provide designers with insight into the effect of real-world situational lighting conditions on people’s abil­ ity to differentiate colors in applications and imagery
  • Demonstrates how a population’s color dif­ ferentiation ability can be measured, modeled, and used by designers to understand the impact diverse situational light­ ing conditions have on color perception
  • Demonstrates that WebCDT is sensitive to changes in environmental lighting
  • Evaluates the effect of situational and demographic conditions on participants’ ability to dis­ criminate colors, with major findings being that ambi­ ent and monitor brightness, age, gender, and self-reported color vision deficiencies all impact users’ color differenti­ ation abilities in digital environments
  • Demonstrates how ColorCheck can be used to evaluate the effect of our participants’ varying color differ­ entiation abilities when viewing digital content using 450 website screenshots and 3,000 infographics
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