Efficient Contrast Effect Compensation with Personalized Perception Models

Color is one of the most effective visual variables and is frequently used to encode metric quantities. Contrast effects are considered harmful in data visualizations since they significantly bias our perception of colors. For instance, a gray patch appears brighter on a black background than on a w...

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Veröffentlicht in:Computer graphics forum Jg. 34; H. 3; S. 211 - 220
Hauptverfasser: Mittelstädt, Sebastian, Keim, Daniel A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.06.2015
Schlagworte:
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Color
Compensation
Computer graphics
Data visualization
I.3.3 [Computer Graphics]: Picture/Image Generation-Display Algorithms
Interactive
Optimization
Perception
Run time (computers)
Studies
Visualization
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung:Color is one of the most effective visual variables and is frequently used to encode metric quantities. Contrast effects are considered harmful in data visualizations since they significantly bias our perception of colors. For instance, a gray patch appears brighter on a black background than on a white background. Accordingly, the perception of color‐encoded data items depends on the surround in the rendered visualization. A method that compensates for contrast effects has been presented previously, which significantly improves the users’ accuracy in reading and comparing color encoded data. The method utilizes established perception models to compensate for contrast effects, assuming an average human observer. In this paper, we provide experiments that show a significant difference in the perception of users. We introduce methods to personalize contrast effect compensation and show that this outperforms the original method with a user study. We, further, overcome the major limitation of the original method, which is a runtime of several minutes. With the use of efficient optimization and surrogate models, we are able to reduce runtime to milliseconds, making the method applicable in interactive visualizations.
Bibliographie:ArticleID:CGF12633
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12633