Abstract
The increasing availability of affordable color raster graphics displays has made it important to develop a better understanding of how color can be used effectively in an interactive environment. Most contemporary graphics displays offer a choice of some 16 million colors; the user's problem is to find the right color.
Folklore has it that the RGB color space arising naturally from color display hardware is user-hostile and that other color models such as the HSV scheme are preferable. Until now there has been virtually no experimental evidence addressing this point.
We describe a color matching experiment in which subjects used one of two tablet-based input techniques, interfaced through one of five color models, to interactively match target colors displayed on a CRT.
The data collected show small but significant differences between models in the ability of subjects to match the five target colors used in this experiment. Subjects using the RGB color model matched quickly but inaccurately compared with those using the other models. The largest speed difference occurred during the early convergence phase of matching. Users of the HSV color model were the slowest in this experiment, both during the convergence phase and in total time to match, but were relatively accurate. There was less variation in performance during the second refinement phase of a match than during the convergence phase.
Two-dimensional use of the tablet resulted in faster but less accurate performance than did strictly one-dimensional usage.
Significant learning occurred for users of the Opponent, YIQ, LAB, and HSV color models, and not for users of the RGB color model.
- 1 BECKER, R. A., AND CHAMBERS, J.M. S--An Interactive Environment {or Data Analysis and Graphics. Wadsworth, 1984. Google Scholar
- 2 BERK, T., BROWNSTON, L., AND KAUFMAN, A, A human factors study of color notation systems for computer graphics. Commun. ACM 25, 8 (Aug. 1982), 547-550. Google Scholar
- 3 BERK, T., BROWNSTON, L., AND KAUFMAN, A. A new color-naming system for graphics languages. IEEE Comput. Graph. Appl. 2, 3 (May 1982), 37-44.Google Scholar
- 4 BOYNTON, R.M. Human Color Vision. Holt, Rinehart and Winston, New York, 1979.Google Scholar
- 5 CIE. Recommendations on Uniform Color Spaces, Color-Difference Equations, Psychrometric Color Terms. Bureau Central de la CIE (Supplement 2 of CIE Publication 15 (E-1.3.1) 1971), 1978.Google Scholar
- 6 COWAN, W. B. An inexpensive scheme for calibration of a colour monitor in terms of CIE standard coordinates. Comput. Graph. 17, 3 (July 1983), 315-321. Google Scholar
- 7 EVANS, K. B., TANNER, P. P., AND WEIN, M. Tablet-based valuators that provide one, two, or three degrees of freedom. Comput. Graph. 15, 3 (Aug. 1981), 91-97. Google Scholar
- 8 FOLEY, J. D., AND VAN DAM, A. Fundamentals of Interactive Computer Graphics. Addison- Wesley, Reading, Mass., 1982. Google Scholar
- 9 HUNT, R. W.G. The Reproduction o{ Colour. Fountain Press, 1975.Google Scholar
- 10 HURVlCH, L.M. Color Vision. Sinaur, Sunderland, Mass., 1981.Google Scholar
- 11 JOBLOVE, G. S., AND GREENBERG, D.P. Color spaces for computer graphics. Comput. Graph. 12, 3 (Aug. 1978), 20-25. Google Scholar
- 12 MUNSELL, A.H. A Color Notation. Munsell Color Company, 1939.Google Scholar
- 13 OSTLE, B., AND MENSING, R.W. Statistics in Research. The Iowa State University Press, 1975.Google Scholar
- 14 SCHWARZ, M.W. An empirical evaluation of interactive colour selection techniques. M. Math. dissertation, Dept. of Computer Science, Univ. of Waterloo, Waterloo, Ont., Canada, 1985.Google Scholar
- 15 SMITH, A.R. Color gamut transform pairs. Comput. Graph. 12, 3 (Aug. 1978), 12-19. Google Scholar
- 16 WALPOLE, R. E., AND MYERS, R. H. Probability and Statistics {or Engineers and Scientists. McMillan, New York, 1978.Google Scholar
- 17 WARE, C., AND BEATTY, J.C. Using colour as a tool in discrete data analysis. Rep. CS-85-21, Dept. of Computer Science, Univ. of Waterloo, Waterloo, Ont., Canada.Google Scholar
- 18 WYSZECKI, G., AND STILES, W.S. Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. Wiley, New York, 1982.Google Scholar
Index Terms
- An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models
Recommendations
Fusion of RGB and HSV colour space for foggy image quality enhancement
The physical properties of water cause light-prompted degradation of foggy images. The light quickly loses intensity as it goes in the water, depending upon the shading range wavelength. Visible light is consumed at the longest wavelength first. Red and ...
Skin color enhancement based on favorite skin color in HSV color space
Skin color enhancement based on favorite skin color is proposed to make skin color displayed on large screen flat panel TVs agree with human favorite skin color. A robust skin detection method in different intensity is obtained after analyzing the ...
Underwater image enhancement via integrated RGB and LAB color models
AbstractImages taken underwater suffers from color shift and poor visibility because the light is absorbed and scattered when it travels through water. To handle the issues mentioned above, we propose an underwater image enhancement method via ...
Highlights- A dedicated fractions-based method to tackle the color shifts of underwater images.
Comments