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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| Published in | Computer graphics forum Vol. 34; no. 3; pp. 211 - 220 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 |
| DOI | 10.1111/cgf.12633 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Keim, Daniel A. Mittelstädt, Sebastian |
| Author_xml | – sequence: 1 givenname: Sebastian surname: Mittelstädt fullname: Mittelstädt, Sebastian organization: University of Konstanz, Germany – sequence: 2 givenname: Daniel A. surname: Keim fullname: Keim, Daniel A. organization: University of Konstanz, Germany |
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| Copyright | 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2015 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: Solteszova V., Turkay C., Price M.C., Viola I.: A perceptual-statistics shading model. IEEE Trans. Vis. Comput. Graphics 18, 12 (2012), 2265-2274. 2 – reference: Kopf J., Lischinski D.: Depixelizing pixel art. ACM Trans. on Graphics 30, 4 (2011), 99:1-99:8. 4 – reference: Eagleman D.M., Jacobson J.E., Sejnowski T.J.: Perceived luminance depends on temporal context. Nature 428, 6985 (2004), 854-856. 9 – reference: Mahy M., Eycken L., Oosterlinck A.: Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Res. Appl. 19, 2 (1994), 105-121. 4 – reference: Harrower M., Brewer C.: Colorbrewer.org: An online tool for selecting colour schemes for maps. The Cartographic Journal 40, 1 (2003), 27-37. 2 – reference: Stockman A.: Color vision mechanisms. PhD thesis, University of Pennsylvania, 2009. 9 – reference: Schott M., Pegoraro V., Hansen C., Boulanger K., Bouatouch K.: A directional occlusion shading model for interactive direct volume rendering. Comput. Graph. Forum 28 (2009), 855-862. 2 – reference: Liu Z., Heer J.: The effects of interactive latency on exploratory visual analysis. IEEE Trans. Vis. Comput. Graphics 20, 12 (2014), 2122-2131. 2, 9 – reference: Ware C.: Color sequences for univariate maps: Theory, experiments and principles. IEEE Comput. Graph. Appl. 8, 5 (1988), 41-49. 1, 2 – reference: Sedlmair M., Heinzl C., Bruckner S., Piringer H., Möller T.: Visual parameter space analysis: A conceptual framework. IEEE Trans. Vis. Comput. Graphics 20, 12 (2014), 2161-2170. 4 – reference: Cui G., Luo M.R., Rigg B., Roesler G., Witt K.: Uniform colour spaces based on the DIN99 colour-difference formula. Color Res. Appl. 27, 4 (2002), 282-290. 3 – reference: Curcio C.A., Sloan K.R., Packer O., Hendrickson A.E., Kalina R.E.: Distribution of cones in human and monkey retina: individual variability and radial asymmetry. Science 236, 4801 (1987), 579-582. 8 – reference: Hunt R. W. G., Pointer M.R.: Measuring Colour. John Wiley & Sons, 2011. 2 – reference: Levkowitz H., Herman G.T.: The design and evaluation of color scales for image data. IEEE Comput. Graph. Appl. 12, 1 (1992), 72-80. 2 – reference: Brewer C.A.: Prediction of simultaneous contrast between map colors with hunt's model of color appearance. Color Res. Appl. 21, 3 (1996), 221-235. 2 – reference: Blakeslee B., Pasieka W., McCourt M.E.: Oriented multiscale spatial filtering and contrast normalization: a parsimonious model of brightness induction in a continuum of stimuli including white, howe and simultaneous brightness contrast. Vision Research 45, 5 (2005), 607-615. 2 – reference: Zheng L., Wu Y., Ma K.-L.: Perceptually-based depth-ordering enhancement for direct volume rendering. IEEE Trans. Vis. Comput. Graphics 19, 3 (2013), 446-459. 2 – reference: Moroney N., Fairchild M.D., Hunt R.W., Li C., Luo M.R., Newman T.: The CIECAM02 color appearance model. In Proc. of the Color and Imaging Conference (2002), vol. 2002, pp. 23-27. 2 – reference: Tennekes M., de Jonge E.: Tree colors: Color schemes for tree-structured data. IEEE Trans. Vis. Comput. Graphics 20, 12 (2014), 2072-2081. 1 – reference: Belmore S.C., Shevell S.K.: Very-long-term and short-term chromatic adaptation: are their influences cumulative? Vision Research 51, 3 (2011), 362-366. 9 – reference: Janetzko H., Stoffel F., Mittelstädt S., Keim D.A.: Anomaly Detection for Visual Analytics of Power Consumption Data. Computers & Graphics 38 (2014), 27-37. 6 – reference: Hansen T., Gegenfurtner K.R.: Higher order color mechanisms: Evidence from noise-masking experiments in cone contrast space. Journal of vision 13, 1 (2013), 26. 9 – reference: Eskew R.T.: Higher order color mechanisms: A critical review. Vision Research 49, 22 (2009), 2686-2704. 9 – reference: Fairchild M.D.: Color Appearance Models. John Wiley & Sons, 2013. 2, 8 – reference: MacAdam D.L.: Visual sensitivities to color differences in daylight. JOSA 32, 5 (1942), 247-273. 4 – reference: Birkeland A., Turkay C., Viola I.: Perceptually uniform motion space. IEEE Trans. Vis. Comput. Graphics 20, 12 (2014), 1542-1554. 2 – reference: Gilchrist A., Kossyfidis C., Bonato F., Agostini T., Cataliotti J., Li X., Spehar B., Annan V., Economou E.: An anchoring theory of lightness perception. Psychological Review 106, 4 (1999), 795-809. 2 – reference: Webster M.A., Juricevic I., McDermott K.C.: Simulations of adaptation and color appearance in observers with varying spectral sensitivity. Ophthalmic and Physiological Optics 30, 5 (2010), 602-610. 8 – reference: Mittelstädt S., Stoffel A., Keim D.A.: Methods for compensating contrast effects in information visualization. Comput. Graph. Forum 33, 3 (2014), 231-240. 1, 2, 3, 6, 7, 8, 9 – reference: Cleveland W.S., McGill R.: A color-caused optical illusion on a statistical graph. The American Statistician 37, 2 (1983), 101-105. 2 – reference: Fairchild M.D., Johnson G.M.: iCAM framework for image appearance, differences, and quality. 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| SubjectTerms | 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 |
| Title | Efficient Contrast Effect Compensation with Personalized Perception Models |
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