Share Email Print

Proceedings Paper

Building perceptual color maps for visualizing interval data
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In visualization, a 'color map' maps a range of data values onto a scale of colors. However, unless a color map is e carefully constructed, visual artifacts can be produced. This problem has stimulated considerable interest in creating perceptually based color maps, that is, color maps where equal steps in data value are perceived as equal steps in the color map [Robertson (1988); Pizer (1981); Green (1992); Lefkowitz and Herman, 1992)]. In Rogowitz and Treinish, (1996, 1998) and in Bergman, Treinish and Rogowitz, (1995), we demonstrated that color maps based on luminance or saturation could be good candidates for satisfying this requirement. This work is based on the seminal work of S.S. Stevens (1966), who measured the perceived magnitude of different magnitudes of physical stimuli. He found that for many physical scales, including luminance (cd/m2) and saturation (the 'redness' of a long-wavelength light source), equal ratios in stimulus value produced equal ratios in perceptual magnitude. He interpreted this as indicating that there exists in human cognition a common scale for representing magnitude, and we scale the effects of different physical stimuli to this internal scale. In Rogowitz, Kalvin, Pelahb and Cohen (1999), we used a psychophysical technique to test this hypothesis as it applies to the creation of perceptually uniform color maps. We constructed color maps as trajectories through three-color spaces, a common computer graphics standard (uncalibrated HSV), a common perceptually-based engineering standard for creating visual stimuli (L*a*b*), and a space commonly used in the graphic arts (Munsell). For each space, we created color scales that varied linearly in hue, saturation, or luminance and measured the detectability of increments in hue, saturation or luminance for each of these color scales. We measured the amplitude of the just-detectable Gaussian increments at 20 different values along the range of each color map. For all three color spaces, we found that luminance-based color maps provided the most perceptually- uniform representations of the data. The just-detectable increment was constant at all points in the color map, with the exception of the lowest-luminance values, where a larger increment was required. The saturation-based color maps provided less sensitivity than the luminance-based color maps, requiring much larger increments for detection. For the hue- based color maps, the size of the increment required for detection varied across the range. For example, for the standard 'rainbow' color map (uncalibrated HSV, hue-varying map), a step in the 'green' region required an increment 16 times the size of the increment required in the 'cyan' part of the range. That is, the rainbow color map would not successfully represent changes in the data in the 'green' region of this color map. In this paper, we extend this research by studying the detectability of spatially-modulated Gabor targets based on these hue, saturation and luminance scales. Since, in visualization, the user is called upon to detect and identify patterns that vary in their spatial characteristics, it is important to study how different types of color maps represent data with varying spatial properties. To do so, we measured modulation thresholds for low-(0.2 c/deg) and high-spatial frequency (4.0 c/deg) Gabor patches and compared them with the Gaussian results. As before, we measured increment thresholds for hue, saturation, and luminance modulations. These color scales were constructed as trajectories along the three perceptual dimensions of color (hue, saturation, and luminance) in two color spaces, uncalibrated HSV and calibrated L*a*b. This allowed us to study how the three perceptual dimensions represent magnitude information for test patterns varying in spatial frequency. This design also allowed us to test the hypothesis that the luminance channel best carries high-spatial frequency information while the saturation channel best represents low spatial-frequency information (Mullen 1985; DeValois and DeValois 1988).

Paper Details

Date Published: 2 June 2000
PDF: 13 pages
Proc. SPIE 3959, Human Vision and Electronic Imaging V, (2 June 2000); doi: 10.1117/12.387169
Show Author Affiliations
Alan David Kalvin, IBM Thomas J. Watson Research Ctr. (United States)
Bernice E. Rogowitz, IBM Thomas J. Watson Research Ctr. (United States)
Adar Pelah, Univ. of Cambridge (United Kingdom)
Aron Cohen, IBM Thomas J. Watson Research Ctr. (United States)

Published in SPIE Proceedings Vol. 3959:
Human Vision and Electronic Imaging V
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?