##plugins.themes.bootstrap3.article.main##

Jacobus Gideon van der Westhuyzen

Abstract

The researcher working in the field of illumination can use a number of measurement methods, depending on the requirement. Many practical evaluation methods employ varieties of a configuration where test stations are positioned next to or opposite each other for comparison purposes. Test stations can consist of test booths or even full-sized test rooms when using indoor evaluations. The number of test booths for indoor application may differ from one to three. Most colour and light perception studies thus depend on human observers to provide some feedback or input. It is the human observer who uses illumination devices and it is important that human response to these products be measured. Results achieved from these measurements can, in turn, be used by the lighting designer to optimize the design of illumination products. Many studies in the field of colour perception and/or matching rely on the method of “psychophysics” where humans are used. Reliability of results achieved when using human observers can be questioned. Results achieved with psychometric testing cannot be compared with an established benchmark and/or standard as such a benchmark was itself generated using human observers, endangering the researchers to become entangled in a closed circle of cause and effect where one depends on the other. This study investigated the reliability of human observers when studying colour perception. Repeatability of results achieved when using human observers was measured. A test was repeated using the same set of observers. Even this approach is not safe as human memory may serve the observer so well, that results can be selected, or at least influenced, on the basis of memory and not perception. The solution was to implement extended time between measurements, thereby hoping that sufficient time will lapse to erase details from the observer’s memory. A colour matching task had to be completed to test observers of two distinct age groups. One group was older than 50 years of age and the second group was younger than 40 years of age. The two groups were tasked to complete the same colour matching test at the beginning of a year and after one year. Test procedures and test equipment were identical before and after the year.

##plugins.themes.bootstrap3.article.details##

##plugins.themes.bootstrap3.article.details##

Keywords

Psychophysical colour matching, LED, test booth, chromaticity coordinates, age, colour rendering

References
[1] Aries, M.B.C., Aarts, M.P.J., & Van Hoof, J. (2015). Daylight and Health: A Review of the Evidence and Consequences for the Built Environment. Lighting Research & Technology, 47, 6 - 27.
[2] Pechacek, C.S., Andersen, M., & Lockley, S.W. (2008). Preliminary Method for Prospective Analysis of the Circadian Efficacy of (Day)Light with Applications to Healthcare Architecture. Leukos, 5(1), 1 -26.
[3] Ohno, Y., & Fein, M. (2013) Vision Experiment on White Light Chromaticity for Lighting. [Online]. http://cltc.ucdavis.edu/sites/default/files/files/publication/2-yoshi-ohno-mira-fein-white-light-chromaticity-vision-experiment.pdf

[4] Rea, M.S., & Freyssinier, J.P. (2010). Color Rendering: Beyond Pride and Prejudice. Color Research and Application, 35(6), 401 - 409.
[5] Dangol, R., Islam, M., Hyvarinen, M., Bhusal, P., Puolakka, M., & Halonen, L. (2013). Subjective Preferences and Colour Quality Metrics of LED Light Sources. Lighting Research & Technology, 45, 666 - 688.
[6] Islam, M.S., Dangol, R., Hyvarinen, M., Bhusal, P., Puolakka, M., & Halonen, L. (2013). User Preferences for LED Lighting in Terms of Light Spectrum.. Lighting Research & Technology, 45, 641 - 665.
[7] Narendran, N., & Deng, L. (2002). Color Rendering Properties of LED light sources. SPIE Proc. 4776, . 61 - 67.
[8] Sandor, N., & Schanda, J. (2006). Visual Colour-Rendering Experiments. Lighting Research & Technology, 225-239.
[9] Spaulding, J.M., Thompson, M.R., & Levin, R.E. (2011). Human Preference in Tunable Solid State Lighting. SPIE Proc. 7954, 795403-1 to 795403-11.
[10] Wei, M., Houser, K.W., David, A., & Krames, M.R. (2014). Perceptual responses to LED illumination with colour rendering indices of 85 and 97. Lighting Research & Technology. 0, 1 - 18.
[11] Thompson, M., O'Reilly, U., & Levin, R.E. (2007). Psychophysical Evaluations of Various Color Rendering from LED-based Architectural Lighting. SPIE Proc. 6669, 66690Y-1 to 66690Y-12.
[12] Houser, K.W., Wei, M., David, A., & Krames, M.R. (2014). Whiteness Perception under LED Illumination. Journal of the Illuminating Engineering Society Of North America. 10, 165 - 180.
[13] Fotios, S.A., & Cheal, C. (2007). Evidence For Response Contraction Bias in Side-By-Side Matching tasks. Lighting Research & Technology. 39(2), 159 - 169.
[14] McCamy, C.S., & Davidson, M.H. (1976). A Color-Rendition Chart. Journal of Applied Photographic Engineering. 2(3), 95 - 99.
[15] Moore, T., Carter, D.J., & Slater, A.L. (2002). A Field Study of Occupant Controlled Lighting in Offices. Lighting Research & Technology. 34(3), 191 - 205.
[16] Zukauskas, A., & Vaicekauskas, R. (2011). LEDs in Lighting with Tailored Color Quality. International Journal of High Speed Electronics and Systems. 20(2), 287 - 301.
[17] Heeger, D. J. (2018, August). color-tutorial.xlsx-CNS@NYU. [Online]. www.cns.nyu.edu/~david/courses/perceptionAdvanced/Tutorials/color-tutorial.xlsx

[18] OSRAM. (2013). Thermal Management of Golden Dragon LED. [Online]. https://www.osram-os.com/Graphics/XPic8/00165240_0.pdf/Thermal%20Management%20of%20Golden%20DRAGON%20LED.pdf

[19] OSRAM. (2014). Reliability of the DRAGON Product Family. [Online]. https://www.osram-os.com/Graphics/XPic8/00165204_0.pdf/Reliability%20of%20the%20DRAGON%20Product%20Family.pdf

[20] Israel, G.D. (2013) University of Florida. [Online]. https://edis.ifas.ufl.edu/pdffiles/PD/PD00600.pdf

[21] Elliot, S.L., Hardy, J.L., Webster, M.A., & Werner, J.S. (2007). Aging and blur adaptation. Journal of Vision. 7(6).
[22] Schefrin, B.E., & Werner, J.S. (1993). Age-related changes in the color appearance of broadband surfaces. Color Research and Application. 18(6), 380 - 389.
[23] Method of Measuring and Specifying Colour Rendering Properties of Light Sources.: Commission Internationale De L'Eclairage (CIE), 1995.
[24] OSRAM Tools & Resources. [Online]. www.osram-americas.com/en-us/tools-and-resources/Pages/led-color-calculator.aspx

[25] Chroma-Q Product Support. (2018) [Online]. http://www.chroma-q.com/support/colour-rendering-index.asp

[26] Statistics Kingdom. (2018) [Online]. http://www.statskingdom.com/170median_mann_whitney.html

[27] Houser, K., Mossman, M., Smet, K., & Whitehead, L. (2015). Tutorial: Color Rendering and its Applications in Lighting. Leukos: Journal of the Illuminating Engineering Society of North America. 00, 1 - 20.
[28] Sylvania. (2018) [Online]. http://www.usailighting.com/stuff/contentmgr/files/1/f386756b8bcf5da65a8d3c26a56b82e7/misc/macadam_faqs.pdf

[29] Ohno, Y. (2011). Calculation of CCT and Duv and Practical Conversion Formulae. in CORM 2011 Conference, Gaithersburg MD.
[30] Padfield, J.(2018). The National Gallery. [Online]. http://research.ng-london.org.uk/scientific/spd/?page=info
Section
Articles

Most read articles by the same author(s)