Abstract

In this study, the impact of starting point chromaticity and number of observers on memory color matching results was investigated. Matching data were obtained for 3 objects (neutral grey cube, yellow lemon and green apple) under a neutral white and a yellow background illumination. Memory color matchings were made for ten starting points of which eight chromaticities were symmetrically distributed along the hue circle and centered at the equal energy white (EEW) chromaticity of the neutral white background illumination; one starting point at the EEW chromaticity and one with the same chromaticity as the background. The matching track from starting point to the memory matched chromaticity was also recorded. It did not tend to cross over the central region towards the complementary hue, especially for experienced observers. The results also demonstrated a significant starting point bias, whereby the matched chromaticities were biased towards the chromaticity of the starting point. Starting point bias can be minimized by selecting three starting points symmetrically distributed around the expected memory color chromaticity. Furthermore, at least, ten observers are needed to achieve stable results for the grey cube and yellow lemon. For the green apple, the results are less conclusive and around 40 observers would be needed to obtain a stable average estimate for the chromaticity of the memory color. The large inter-observer variation may result from cultural differences or from natural variations in the “green” apple appearance. This study provides a well-founded guidance for future application of the memory color matching method.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Study of chromatic adaptation using memory color matches, Part I: neutral illuminants

Kevin A. G. Smet, Qiyan Zhai, Ming R. Luo, and Peter Hanselaer
Opt. Express 25(7) 7732-7748 (2017)

Study of chromatic adaptation using memory color matches, Part II: colored illuminants

Kevin A. G. Smet, Qiyan Zhai, Ming R. Luo, and Peter Hanselaer
Opt. Express 25(7) 8350-8365 (2017)

Study of chromatic adaptation via neutral white matches on different viewing media

Qiyan Zhai and Ming R. Luo
Opt. Express 26(6) 7724-7739 (2018)

References

  • View by:
  • |
  • |
  • |

  1. K. A. G. Smet, Q. Zhai, M. R. Luo, and P. Hanselaer, “Study of chromatic adaptation using memory color matches, Part I: neutral illuminants,” Opt. Express 25(7), 7732 (2017).
    [Crossref]
  2. K. A. G. Smet, Q. Zhai, M. R. Luo, and P. Hanselaer, “Study of chromatic adaptation using memory color matches, Part II: colored illuminants,” Opt. Express 25(7), 8350 (2017).
    [Crossref]
  3. S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
    [Crossref]
  4. K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy Build. 49, 216–225 (2012).
    [Crossref]
  5. K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Light. Res. Technol. 48(4), 393–411 (2016).
    [Crossref]
  6. S. Babilon, “On the Color Rendition of White Light Sources in Relation to Memory Preference,” Technische Universität, Darmstadt (2018).
  7. L. Arend and A. Reeves, “Simultaneous color constancy,” J. Opt. Soc. Am. A 3(10), 1743–1751 (1986).
    [Crossref]
  8. H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Philos. Trans. R. Soc., B 360(1458), 1329–1346 (2005).
    [Crossref]
  9. A. Tversky and D. Kahneman, “Judgment under Uncertainty: Heuristics and Biases,” Science 185(4157), 1124–1131 (1974).
    [Crossref]
  10. G. B. Chapman and E. J. Johnson, “Anchoring, activation, and the construction of values,” Organ. Behav. Hum. Decis. Process. 79(2), 115–153 (1999).
    [Crossref]
  11. M. G. Kent, S. Fotios, and S. Altomonte, “Discomfort glare evaluation: The influence of anchor bias in luminance adjustments,” Light. Res. Technol. 51(1), 131–146 (2019).
    [Crossref]
  12. Á Logadóttir, J. Christoffersen, and S. Fotios, “Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment,” Light. Res. Technol. 43(4), 403–422 (2011).
    [Crossref]
  13. G. A. Gescheider, Psychophysics: The Fundamentals (Psychology Press, 2013).
  14. Q. Zhai and M. R. Luo, “Study of chromatic adaptation via neutral white matches on different viewing media,” Opt. Express 26(6), 7724 (2018).
    [Crossref]
  15. D. H. Foster, “Color constancy,” Vision Res. 51(7), 674–700 (2011).
    [Crossref]
  16. M. D. Fairchild and L. Reniff, “Time-course of chromatic adaptation for color-appearance judgments,” J. Opt. Soc. Am. A 12(5), 824–833 (1995).
    [Crossref]
  17. O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation,” Vision Res. 40(14), 1813–1826 (2000).
    [Crossref]
  18. F. W. Billmeyer and P. J. Alessi, “Assessment of Color-Measuring Instruments,” Color Res. Appl. 6(4), 195–202 (1981).
    [Crossref]
  19. Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” Int. Comm. Illum. Tech. Rep. TN 001:201, (2014).
  20. P. D. W. Kirk and M. P. H. Stumpf, “Gaussian process regression bootstrapping: Exploring the effects of uncertainty in time course data,” Bioinformatics 25(10), 1300–1306 (2009).
    [Crossref]
  21. K. A. G. Smet, Y. Lin, B. V. Nagy, Z. Németh, G. L. Duque-Chica, J. M. Quintero, H.-S. Chen, R. M. Luo, M. Safi, and P. Hanselaer, “Cross-cultural variation of memory colors of familiar objects,” Opt. Express 22(26), 32308 (2014).
    [Crossref]

2019 (1)

M. G. Kent, S. Fotios, and S. Altomonte, “Discomfort glare evaluation: The influence of anchor bias in luminance adjustments,” Light. Res. Technol. 51(1), 131–146 (2019).
[Crossref]

2018 (1)

2017 (2)

2016 (1)

K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Light. Res. Technol. 48(4), 393–411 (2016).
[Crossref]

2014 (2)

2012 (1)

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy Build. 49, 216–225 (2012).
[Crossref]

2011 (2)

D. H. Foster, “Color constancy,” Vision Res. 51(7), 674–700 (2011).
[Crossref]

Á Logadóttir, J. Christoffersen, and S. Fotios, “Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment,” Light. Res. Technol. 43(4), 403–422 (2011).
[Crossref]

2009 (1)

P. D. W. Kirk and M. P. H. Stumpf, “Gaussian process regression bootstrapping: Exploring the effects of uncertainty in time course data,” Bioinformatics 25(10), 1300–1306 (2009).
[Crossref]

2005 (1)

H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Philos. Trans. R. Soc., B 360(1458), 1329–1346 (2005).
[Crossref]

2000 (1)

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation,” Vision Res. 40(14), 1813–1826 (2000).
[Crossref]

1999 (1)

G. B. Chapman and E. J. Johnson, “Anchoring, activation, and the construction of values,” Organ. Behav. Hum. Decis. Process. 79(2), 115–153 (1999).
[Crossref]

1995 (1)

1986 (1)

1981 (1)

F. W. Billmeyer and P. J. Alessi, “Assessment of Color-Measuring Instruments,” Color Res. Appl. 6(4), 195–202 (1981).
[Crossref]

1974 (1)

A. Tversky and D. Kahneman, “Judgment under Uncertainty: Heuristics and Biases,” Science 185(4157), 1124–1131 (1974).
[Crossref]

Alessi, P. J.

F. W. Billmeyer and P. J. Alessi, “Assessment of Color-Measuring Instruments,” Color Res. Appl. 6(4), 195–202 (1981).
[Crossref]

Altomonte, S.

M. G. Kent, S. Fotios, and S. Altomonte, “Discomfort glare evaluation: The influence of anchor bias in luminance adjustments,” Light. Res. Technol. 51(1), 131–146 (2019).
[Crossref]

Arend, L.

Babilon, S.

S. Babilon, “On the Color Rendition of White Light Sources in Relation to Memory Preference,” Technische Universität, Darmstadt (2018).

Billmeyer, F. W.

F. W. Billmeyer and P. J. Alessi, “Assessment of Color-Measuring Instruments,” Color Res. Appl. 6(4), 195–202 (1981).
[Crossref]

Blattner, P.

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” Int. Comm. Illum. Tech. Rep. TN 001:201, (2014).

Chapman, G. B.

G. B. Chapman and E. J. Johnson, “Anchoring, activation, and the construction of values,” Organ. Behav. Hum. Decis. Process. 79(2), 115–153 (1999).
[Crossref]

Chen, H.-S.

Christoffersen, J.

Á Logadóttir, J. Christoffersen, and S. Fotios, “Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment,” Light. Res. Technol. 43(4), 403–422 (2011).
[Crossref]

Deconinck, G.

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy Build. 49, 216–225 (2012).
[Crossref]

Dorsey, J.

S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
[Crossref]

Duque-Chica, G. L.

Fairchild, M. D.

Foster, D. H.

D. H. Foster, “Color constancy,” Vision Res. 51(7), 674–700 (2011).
[Crossref]

Fotios, S.

M. G. Kent, S. Fotios, and S. Altomonte, “Discomfort glare evaluation: The influence of anchor bias in luminance adjustments,” Light. Res. Technol. 51(1), 131–146 (2019).
[Crossref]

Á Logadóttir, J. Christoffersen, and S. Fotios, “Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment,” Light. Res. Technol. 43(4), 403–422 (2011).
[Crossref]

Gegenfurtner, K. R.

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation,” Vision Res. 40(14), 1813–1826 (2000).
[Crossref]

Gescheider, G. A.

G. A. Gescheider, Psychophysics: The Fundamentals (Psychology Press, 2013).

Hanselaer, P.

Johnson, E. J.

G. B. Chapman and E. J. Johnson, “Anchoring, activation, and the construction of values,” Organ. Behav. Hum. Decis. Process. 79(2), 115–153 (1999).
[Crossref]

Kahneman, D.

A. Tversky and D. Kahneman, “Judgment under Uncertainty: Heuristics and Biases,” Science 185(4157), 1124–1131 (1974).
[Crossref]

Kent, M. G.

M. G. Kent, S. Fotios, and S. Altomonte, “Discomfort glare evaluation: The influence of anchor bias in luminance adjustments,” Light. Res. Technol. 51(1), 131–146 (2019).
[Crossref]

Kirk, P. D. W.

P. D. W. Kirk and M. P. H. Stumpf, “Gaussian process regression bootstrapping: Exploring the effects of uncertainty in time course data,” Bioinformatics 25(10), 1300–1306 (2009).
[Crossref]

Lin, Y.

Logadóttir, Á

Á Logadóttir, J. Christoffersen, and S. Fotios, “Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment,” Light. Res. Technol. 43(4), 403–422 (2011).
[Crossref]

Luo, M. R.

Luo, R. M.

McNamara, A.

S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
[Crossref]

Nagy, B. V.

Németh, Z.

Ohno, Y.

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” Int. Comm. Illum. Tech. Rep. TN 001:201, (2014).

Pointer, M. R.

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy Build. 49, 216–225 (2012).
[Crossref]

Quintero, J. M.

Reeves, A.

Reniff, L.

Rinner, O.

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation,” Vision Res. 40(14), 1813–1826 (2000).
[Crossref]

Rushmeier, H.

S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
[Crossref]

Ryckaert, W. R.

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy Build. 49, 216–225 (2012).
[Crossref]

Safi, M.

Smet, K. A. G.

Smithson, H. E.

H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Philos. Trans. R. Soc., B 360(1458), 1329–1346 (2005).
[Crossref]

Stumpf, M. P. H.

P. D. W. Kirk and M. P. H. Stumpf, “Gaussian process regression bootstrapping: Exploring the effects of uncertainty in time course data,” Bioinformatics 25(10), 1300–1306 (2009).
[Crossref]

Tan, M.

S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
[Crossref]

Tversky, A.

A. Tversky and D. Kahneman, “Judgment under Uncertainty: Heuristics and Biases,” Science 185(4157), 1124–1131 (1974).
[Crossref]

Xue, S.

S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
[Crossref]

Zhai, Q.

Bioinformatics (1)

P. D. W. Kirk and M. P. H. Stumpf, “Gaussian process regression bootstrapping: Exploring the effects of uncertainty in time course data,” Bioinformatics 25(10), 1300–1306 (2009).
[Crossref]

Color Res. Appl. (1)

F. W. Billmeyer and P. J. Alessi, “Assessment of Color-Measuring Instruments,” Color Res. Appl. 6(4), 195–202 (1981).
[Crossref]

Energy Build. (1)

K. A. G. Smet, W. R. Ryckaert, M. R. Pointer, G. Deconinck, and P. Hanselaer, “A memory colour quality metric for white light sources,” Energy Build. 49, 216–225 (2012).
[Crossref]

J. Opt. Soc. Am. A (2)

Light. Res. Technol. (3)

K. A. G. Smet and P. Hanselaer, “Memory and preferred colours and the colour rendition of white light sources,” Light. Res. Technol. 48(4), 393–411 (2016).
[Crossref]

M. G. Kent, S. Fotios, and S. Altomonte, “Discomfort glare evaluation: The influence of anchor bias in luminance adjustments,” Light. Res. Technol. 51(1), 131–146 (2019).
[Crossref]

Á Logadóttir, J. Christoffersen, and S. Fotios, “Investigating the use of an adjustment task to set the preferred illuminance in a workplace environment,” Light. Res. Technol. 43(4), 403–422 (2011).
[Crossref]

Opt. Express (4)

Organ. Behav. Hum. Decis. Process. (1)

G. B. Chapman and E. J. Johnson, “Anchoring, activation, and the construction of values,” Organ. Behav. Hum. Decis. Process. 79(2), 115–153 (1999).
[Crossref]

Philos. Trans. R. Soc., B (1)

H. E. Smithson, “Sensory, computational and cognitive components of human colour constancy,” Philos. Trans. R. Soc., B 360(1458), 1329–1346 (2005).
[Crossref]

Proc. SPIE (1)

S. Xue, M. Tan, A. McNamara, J. Dorsey, and H. Rushmeier, “Exploring the use of memory colors for image enhancement,” Proc. SPIE 9014, 901411 (2014).
[Crossref]

Science (1)

A. Tversky and D. Kahneman, “Judgment under Uncertainty: Heuristics and Biases,” Science 185(4157), 1124–1131 (1974).
[Crossref]

Vision Res. (2)

D. H. Foster, “Color constancy,” Vision Res. 51(7), 674–700 (2011).
[Crossref]

O. Rinner and K. R. Gegenfurtner, “Time course of chromatic adaptation,” Vision Res. 40(14), 1813–1826 (2000).
[Crossref]

Other (3)

Y. Ohno and P. Blattner, “Chromaticity difference specification for light sources,” Int. Comm. Illum. Tech. Rep. TN 001:201, (2014).

S. Babilon, “On the Color Rendition of White Light Sources in Relation to Memory Preference,” Technische Universität, Darmstadt (2018).

G. A. Gescheider, Psychophysics: The Fundamentals (Psychology Press, 2013).

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (15)

Fig. 1.
Fig. 1. The distribution of the starting points SP1-SP4, SP6-SP9 (black spots) & SP5 (a red square) and the two backgrounds (blue stars) in the CIE 1976 u’10 v’10 chromaticity diagram. SP10 is not plotted here because it overlaps with SP5 under EEW background and is very close to SP1 under yellow background. The picture of each object is shown in the upper right corner. (a) Grey cube (b) Green apple (c) Yellow lemon
Fig. 2.
Fig. 2. The experiment setup. (a) Picture. (b) Side view schematic.
Fig. 3.
Fig. 3. (a) The reflectance spectra of the grey cube, green apple, yellow lemon (stimulus) and white background used in the experiment. (b) The radiance spectrum of two backgrounds (EEW and yellow).
Fig. 4.
Fig. 4. The recorded tracks for the achromatic matches with the grey cube from starting points (SP1 – SP10) to end points for two observers. In each track, the starting point is marked as a filled circle and the end point is marked as a square. (a) An experienced observer under the yellow illumination; (b) An experienced observer under EEW illumination; (c) An inexperienced observer under the yellow illumination; (d) An inexperienced observer under EEW illumination.
Fig. 5.
Fig. 5. The average number of steps in a matching track for each of the 10 starting points and two background chromaticities. In each graph, the error bar represents the standard deviation of the 10 observers. (a) Grey Cube (b) Green Apple (c) Yellow lemon.
Fig. 6.
Fig. 6. Distribution in the CIE 1976 u’10v’10 chromaticity diagram of the mean end points for all three familiar objects (mean over all 10 observers) for 10 starting points. (a) Yellow illumination (b) EEW illumination
Fig. 7.
Fig. 7. The relationship between the color difference between the average of n randomly selected starting points and the chromaticity of the EEW-illuminated object (DEsp) and the accuracy of the end points (DEep). Three rows represent the different corresponding colors (grey cube, yellow lemon, green apple); columns 1 and 2 represent yellow and EEW illuminations respectively.
Fig. 8.
Fig. 8. Schematic diagram of data processing for investigating the impact of the number of starting points on precision and accuracy.
Fig. 9.
Fig. 9. The impact of starting point numbers on MaxDEaccuracy value. In each graph, black, yellow and green curves represent grey cube, yellow lemon and green apple respectively. (a) Yellow illumination (b) EEW illumination
Fig. 10.
Fig. 10. The examples of the starting points distribution (nsp = 3) at different symmetry levels (5 columns) in terms of normalized DEsp for the three objects including grey cube, yellow lemon and green apple (3 rows). The name of each column represents the symmetry range. For example, (0,0.2] means the symmetry value is larger than 0 but smaller than 0.2. The red cross in each ellipse is the chromaticity of the object illuminated by EEW, which is the symmetry center of full set of 8 starting points. The black cross is the average of the three selected starting points. The red, blue and black points represent three selected starting points.
Fig. 11.
Fig. 11. The MaxDEaccuracy value as a function of the number of starting points at 8 symmetry thresholds of the starting point distribution. The three subfigures represent the grey cube, yellow lemon and green apple respectively. (a) Yellow illumination (b) EEW illumination
Fig. 12.
Fig. 12. The impact of starting point numbers on MaxDEaccuracy value determined with a symmetry threshold value of 0.3. In each graph, black, yellow and green curves represent the grey cube, yellow lemon and green apple, respectively. (a) Yellow illumination (b) EEW illumination
Fig. 13.
Fig. 13. Schematic diagram of the data processing work flow for investigating the impact of the observer number on accuracy.
Fig. 14.
Fig. 14. The impact of the number of observer number on the MaxDEaccuracy value. In each graph, red, blue and black curves represent grey cube, yellow lemon and green apple respectively. (a) Yellow illumination (b) EEW illumination.
Fig. 15.
Fig. 15. The fitted inter-observer variability ellipse of grey cube, yellow lemon and green apple at 95% confidence interval. (a) Yellow illumination (b) EEW illumination.

Metrics