Abstract
User interfaces for color selection consist of a visible screen representation, an input method, and the underlying conceptual organization of the color model. We report a two-way factorial, between-subjects variable experiment that tested the effect of high and low visual feedback interfaces on speed and accuracy of color matching for RGB and HSV color models. The only significant effect was improved accuracy due to increased visual feedback. Using color groups as a within-subjects variable, we found differences in performance of both speed and accuracy. We recommend that experimental tests adopt a color test set that does not show bias toward a particular model, but is based instead on a range of colors that would be most likely matched in practice by people using color selection software. We recomment the Macbeth Color Checker naturals, primaries, and grays. As a follow-up study, a qualitative case analysis of the way users navigated through the color space indicates that feedback helps users with limited knowledge of the model, allowing them to refine their match to a higher degree of accuracy. Users with very little or a lot of knowledge of the color model do not appear to be aided by increased feedback. In conclusion, we suggest that visual feedback and design of the interface may be a more important factor in improving the usability of a color selection interface than the particular color model used.
- AHLBERG, C. AND SHNEIDERMAN, B. 1994. Visual information seeking: Tight coupling of dynamic query filters with starfield displays. In Human Factors in Computing Systems: CHI '94 Conference Proceedings, ACM Press, New York, 313-317. Google Scholar
- ATWOOD, M. AND POLSON, P. 1976. A process model for water jug problems. Cogn. Psychol. 8, 191-216.Google Scholar
- BAUERSFELD, P. AND SLATER, J.L. 1991. User-oriented color interface design: Direct manipulation of color in context. In Human Factors in Computing Systems: CHI '91 Conference Proceedings, ACM Press, New York, 417-418. Google Scholar
- BERK, T., BROWNSTON, L., AND KAUFMAN, A. 1982. A human factors study of color notation systems for computer graphics. Commun. ACM 25, 8 (August), 547-550. Google Scholar
- DOUGLAS, S. AND KIRKPATRICK, T. 1996a. Do color models really make a difference? In Human Factors in Computing Systems: CHI '96 Conference Proceedings, ACM Press, New York, 399-405. Google Scholar
- DOUGLAS, S. AND KIRKPATRICK, T. 1996b. The effect of feedback on a color selection interface. In Graphics Interface '96 Conference Proceedings, Morgan Kaufmann, San Francisco, 47-53. Google Scholar
- FOLEY, J., VAN DAM, A., FEINER, S., AND HUGHES, J. 1990. Computer Graphics: Principles and Practice, 2nd ed., Addison-Wesley, Reading, MA. Google Scholar
- HANDEL, S. AND IMAI, S. 1972. The free classification of analyzable and unanalyzable stimuli. Percept. Psychophys. 12, 108-116.Google Scholar
- HALL, R. 1989. Illumination and Color in Computer Generated Imagery. Springer-Verlag, New York. Google Scholar
- HUNT, R. W.G. 1991. Measuring Color, 2d. ed. Ellis Horwood, New York.Google Scholar
- HURVICH, L.M. 1981. Color Vision. Sinauer, Sunderland, MA.Google Scholar
- JACOB, R. J. K., SIBERT, L. E., MCFARLANE, D. C., AND MULLEN, M.P. 1994. Integrality and separability of input devices. ACM Trans. Comput. Hum. Interact. 1, 1, 3-26. Google Scholar
- JUDD, D. B. AND WSYZECKI, G. 1975. Color in Business, Science, and Industry, 3rd. ed. Wiley, New York.Google Scholar
- MACBETH 1990. Color Checker Chart. Munsell Color, Baltimore, MD.Google Scholar
- MURCH, G. 1984. The effective use of color: Cognitive principles. TEKniques 8, 2, 25-31.Google Scholar
- MUNSELL, A.H. 1946. A Color Notation, 17th ed. Munsell Color, Newburgh, NY.Google Scholar
- NEWELL, A. AND SIMON, H. 1972. Human Problem Solving. Prentice-Hall, Englewood Cliffs, NJ. Google Scholar
- ROSNOW, R. AND ROSENTHAL, R. 1989. Definition and interpretation of interaction effects. Psychol. Bull. 105, 143-146.Google Scholar
- SCHWARZ, M., COWAN, W., AND BEATTY, J. 1987. An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Trans. Graph. 6, 2 (April), 123-158. Google Scholar
- SHNEIDERMAN, B. 1983. Direct manipulation: A step beyond programming languages. IEEE Comput. 16, 8 (August), 57-69.Google Scholar
- SMITH, A.R. 1978. Color gamut transform pairs. Comput. Graph. 12, 3 (August), 12-19. Google Scholar
- SMITH, A. R. AND LYONS, E. R. 1996. HWB--A more intuitive hue-based color model. J. Graph. Tools 1, 1, pp. 3-17. Google Scholar
- TEKTRONIX, INC. 1991. US Patent number 4,985,853.Google Scholar
- WELLS, E. 1994. A comparison of interactive color specification systems for human-com-puter interfaces. M.S. Thesis in Visualization Sciences, Texas A & M University.Google Scholar
- WICKELGREN, W.A. 1974. How to Solve Problems: Elements of a Theory of Problems and Problem Solving. Freeman, San Francisco.Google Scholar
Index Terms
- Model and representation: the effect of visual feedback on human performance in a color picker interface
Recommendations
ColorFingers: improved multi-touch color picker
SA '14: SIGGRAPH Asia 2014 Technical BriefsColorFingers is a WYSIWYG, Location Independent Touch (LIT) based color picking tool aimed to give unique and swift interaction in choosing color on touch based devices. It makes use of touch interface and the touch information of two fingers to select ...
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 ...
Detecting skin in face recognition systems: A colour spaces study
Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to ...
Comments