Abstract

The visual effects of lighting on art paintings is an important aspect that should be considered by museum curators. The aim of this work was to determine the correlated color temperature (CCT) of daylight illumination preferred by observers when appreciating art paintings. Hyperspectral images of 11 oil paintings were collected at the museum, and the appearance of the paintings under daylight illuminants with CCT from 25,000K to 3600K was computed. In a psychophysical experiment using precise CRT reproductions of the paintings, observers had to adjust the CCT of the illuminant such that it produced the best visual impression. It was found that the distribution of observers’ preferences had a maximum at a CCT of about 5100K and that this value did not depend on whether the observers were undergraduate students or museum visitors or on the degree of adaptation to the color of the illumination. These results suggest that observers prefer a more bluish-white light than that normally used in museums.

© 2008 Optical Society of America

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References

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    [CrossRef] [PubMed]

2007 (2)

U. Leonards, R. Baddeley, I. D. Gilchrist, T. Troscianko, P. Ledda, and B. Williamson, "Mediaeval artists: masters in directing the observers' gaze," Curr. Biol. 17, R8-R9 (2007).
[CrossRef] [PubMed]

F. Martinez-Verdu, E. Perales, E. Chorro, D. de Fez, V. Viqueira, and E. Gilabert, "Computation and visualization of the MacAdam limits for any lightness, hue angle, and light source," J. Opt. Soc. Am. A 24, 1501-1515 (2007).
[CrossRef]

2006 (3)

D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, "Frequency of metamerism in natural scenes," J. Opt. Soc. Am. A 23, 2359-2372 (2006).
[CrossRef]

M. A. Aldaba, J. M. M. Linhares, P. D. Pinto, S. M. C. Nascimento, K. Amano, and D. H. Foster, "Visual sensitivity to color errors in images of natural scenes," Visual Neurosci. 23, 555-559 (2006).
[CrossRef]

P. D. Pinto, J. M. M. Linhares, J. A. Carvalhal, and S. M. C. Nascimento, "Psychophysical estimation of the best illumination for appreciation of Renaissance paintings," Visual Neurosci. 23, 669-674 (2006).
[CrossRef]

2005 (5)

C. You, "Visual equivalence of light-emitting diode white light," Opt. Eng. (Bellingham) 44, 111307 (2005).
[CrossRef]

D. Brown, D. Nicol, and I. Ferguson, "Investigation of the spectral properties of LED-based MR16 bulbs for general illumination," Opt. Eng. (Bellingham) 44, 111310 (2005).
[CrossRef]

H. E. Smithson, "Sensory computational and cognitive components of human colour constancy," Philos. Trans. R. Soc. London, Ser. B 360, 1329-1346 (2005).
[CrossRef] [PubMed]

J. M. M. Linhares, J. A. Carvalhal, S. M. C. Nascimento, M. H. Regalo, and M. C. V. P. Leite, "Estimating the best illuminant for art paintings by computing chromatic diversity," Perception 34, 88-89 (2005).

H. Liang, D. Saunders, and J. Cupitt, "A new multispectral imaging system for examining paintings," J. Imaging Sci. Technol. 49, 551-562 (2005).

2004 (3)

J. M. M. Linhares, S. M. C. Nascimento, D. H. Foster, and K. Amano, "Chromatic diversity of natural scenes," Perception 33, 65-65 (2004).

M. Scuello, I. Abramov, J. Gordon, S. Weintraub, and S. Weintra, "Museum lighting: optimizing the illuminant," Color Res. Appl. 29, 121-127 (2004).
[CrossRef]

M. Scuello, I. Abramov, J. Gordon, and S. Weintraub, "Museum lighting: Why are some illuminants preferred?" J. Opt. Soc. Am. A 21, 306-311 (2004).
[CrossRef]

2003 (1)

D. H. Foster, "Does colour constancy exist?" Trends Cogn. Sci. 7, 439-443 (2003).
[CrossRef] [PubMed]

2001 (3)

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, "Color and spectral analysis of daylight in southern Europe," J. Opt. Soc. Am. A 18, 1325-1335 (2001).
[CrossRef]

M. R. Luo, G. Cui, and B. Rigg, "The development of the CIE 2000 colour-difference formula: CIEDE2000," Color Res. Appl. 26, 340-350 (2001).
[CrossRef]

R. S. Berns, "The science of digitizing paintings for color-accurate image archives: a review," J. Imaging Sci. Technol. 45, 305-325 (2001).

1998 (2)

1993 (1)

H. Xu, "Color-rendering capacity of light," Color Res. Appl. 18, 267-269 (1993).
[CrossRef]

1990 (1)

R. G. Davis and D. N. Ginthner, "Correlated color temperature, illuminance level, and the Kruithof curve," J. Illum. Eng. Soc. 19, 27-38 (1990).

1985 (1)

E. J. Olszewski, "Distortions, shadows, and conservations in sixteenth century Italian art," Artibus et Historiae 6, 101-124 (1985).
[CrossRef]

1964 (1)

Appl. Opt. (1)

Artibus et Historiae (1)

E. J. Olszewski, "Distortions, shadows, and conservations in sixteenth century Italian art," Artibus et Historiae 6, 101-124 (1985).
[CrossRef]

Color Res. Appl. (4)

M. R. Luo, G. Cui, and B. Rigg, "The development of the CIE 2000 colour-difference formula: CIEDE2000," Color Res. Appl. 26, 340-350 (2001).
[CrossRef]

M. R. Pointer and G. G. Attridge, "The number of discernible colours," Color Res. Appl. 23, 52-54 (1998).
[CrossRef]

H. Xu, "Color-rendering capacity of light," Color Res. Appl. 18, 267-269 (1993).
[CrossRef]

M. Scuello, I. Abramov, J. Gordon, S. Weintraub, and S. Weintra, "Museum lighting: optimizing the illuminant," Color Res. Appl. 29, 121-127 (2004).
[CrossRef]

Curr. Biol. (1)

U. Leonards, R. Baddeley, I. D. Gilchrist, T. Troscianko, P. Ledda, and B. Williamson, "Mediaeval artists: masters in directing the observers' gaze," Curr. Biol. 17, R8-R9 (2007).
[CrossRef] [PubMed]

J. Illum. Eng. Soc. (1)

R. G. Davis and D. N. Ginthner, "Correlated color temperature, illuminance level, and the Kruithof curve," J. Illum. Eng. Soc. 19, 27-38 (1990).

J. Imaging Sci. Technol. (2)

H. Liang, D. Saunders, and J. Cupitt, "A new multispectral imaging system for examining paintings," J. Imaging Sci. Technol. 49, 551-562 (2005).

R. S. Berns, "The science of digitizing paintings for color-accurate image archives: a review," J. Imaging Sci. Technol. 45, 305-325 (2001).

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (4)

Opt. Eng. (Bellingham) (2)

C. You, "Visual equivalence of light-emitting diode white light," Opt. Eng. (Bellingham) 44, 111307 (2005).
[CrossRef]

D. Brown, D. Nicol, and I. Ferguson, "Investigation of the spectral properties of LED-based MR16 bulbs for general illumination," Opt. Eng. (Bellingham) 44, 111310 (2005).
[CrossRef]

Perception (2)

J. M. M. Linhares, S. M. C. Nascimento, D. H. Foster, and K. Amano, "Chromatic diversity of natural scenes," Perception 33, 65-65 (2004).

J. M. M. Linhares, J. A. Carvalhal, S. M. C. Nascimento, M. H. Regalo, and M. C. V. P. Leite, "Estimating the best illuminant for art paintings by computing chromatic diversity," Perception 34, 88-89 (2005).

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

H. E. Smithson, "Sensory computational and cognitive components of human colour constancy," Philos. Trans. R. Soc. London, Ser. B 360, 1329-1346 (2005).
[CrossRef] [PubMed]

Trends Cogn. Sci. (1)

D. H. Foster, "Does colour constancy exist?" Trends Cogn. Sci. 7, 439-443 (2003).
[CrossRef] [PubMed]

Visual Neurosci. (2)

P. D. Pinto, J. M. M. Linhares, J. A. Carvalhal, and S. M. C. Nascimento, "Psychophysical estimation of the best illumination for appreciation of Renaissance paintings," Visual Neurosci. 23, 669-674 (2006).
[CrossRef]

M. A. Aldaba, J. M. M. Linhares, P. D. Pinto, S. M. C. Nascimento, K. Amano, and D. H. Foster, "Visual sensitivity to color errors in images of natural scenes," Visual Neurosci. 23, 555-559 (2006).
[CrossRef]

Other (21)

J. M. M. Linhares, "Estimating chromatic diversity from hyperspectral images," M.Sc thesis (University of Manchester, 2005).

CIE, "Colorimetry," CIE 15:2004 (CIE, 2004).

E. Perales, F. Martinez-Verdu, V. Viqueira, M. J. Luque, and P. Capilla, "Computing the number of distinguishable colors under several illuminants and light sources," inThird IS&T European Conference on Colour Graphics, Imaging and Vision (Society for Imaging Science and Technology, 2006), pp. 414-419.

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. (Wiley, 1982).

L. da Vinci, Treatise on Painting (Codex. Urbinus Latinus 1270) (Princeton U. Press, 1956, 1270).

CIE, "Standard method of assessing the spectral quality of daylight simulators for visual appraisal and measurement of colour," CIE DS 012:2001 (CIE, 2001).
[PubMed]

CIE, "A method for assessing the quality of daylight simulators for colorimetry," CIE 51.2-1999 (CIE, 1999).

R. W. G. Hunt, Measuring Colour, 3rd ed. (Fountain, 1998).

W. S. Taft, J. W. Mayer, R. Newman, D. Stulik, and P. Kuniholm, The Science of Paintings (Springer, 2000).

K. Nassau, Color for Science, Art and Technology (Elsevier Science B.V., 1998).

T. B. Brill, Light: Its Interactions with Art and Antiques (Plenum, 1980).

R. Johnston-Feller, Color Science in the Examination of Museum Objects (Oxford U. Press, 2001).

G. Thomson, The Museum Environment, 2nd ed. (Butterworth-Heinemann, 1986).

CIE, "Control of damage to museum objects by optical radiation," CIE 157:2004 (CIE, 2004).

L. T. Maloney, "Physics-based approaches to modeling surface color perception," in Color Vision: from Genes to Perception, K.R.Gegenfurtner and L.T.Sharpe, eds. (Cambridge U. Press, 1999), pp. 387-416.

M. Kemp, The Science of Art (Yale University, 1990).

J. Turner, The Dictionary of Art (Macmillan, 1996).

J. F. Alves, Do tirar polo natural/Francisco D'Holanda: introdução notas e comentários de José da Felicidade Alves (Horizonte, 1984).
[PubMed]

A. R. Cortés, "Multispectral analysis and spectral reflectance reconstruction of art paintings," Ph.D. thesis (École Nationale Supérieure des Télécommunications, 2003).

C. Fischer and I. Kakoulli, "Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications," Rev. Conserv.3-12 (2006).

M. J. A. T. d. Carvalhal, "Digitalização de pintura artística com imagiografia hiperespectral," M.Sc. thesis (Universidade do Minho, 2004).

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Figures (7)

Fig. 1
Fig. 1

Color pictures of the 11 oil paintings used in the study. They were selected from the collection of Museu Nogueira da Silva in Braga, Portugal. The set consisted of 11 oil paintings, 7 from the Renaissance époque painted on wood (A–E, H and I) and 4 (F, G, J, and K) from the 20th century painted on canvas. Two of the more recent paintings (J and K) are from Henrique Medina, one (F) from Carlos Reis and the other (G) from Veloso Salgado. All four are by Portuguese painters.

Fig. 2
Fig. 2

(Left) Normalized daylight spectra for CCT of 3600 K , 6500 K ( D 65 ) , and 25,000 K . (Right) Locus of the chromaticities represented in the CIE ( x , y ) diagram of the daylight illuminants used in this study.

Fig. 3
Fig. 3

Percentage of pixels out of gamut (solid curve) for two of the paintings, H (left) and K (right), used in the experiment and the average chromatic error (dotted curve) expressed as Δ E ab * in CIELAB space resulting from clipping the out-of-gamut pixels. All values are expressed as function of the CIE x D coordinate of the illuminant. Notice the difference in the scales on the left and the right axes.

Fig. 4
Fig. 4

Observers’ responses (open symbols) for two paintings, H on the left and K on the right, represented by the frequency of illuminant selection expressed as a function of the CIE x D coordinate of the illuminant. Data based on 40 undergraduate observers and three trials per observer were obtained with the gray background. The solid curves represent Gaussian fits to the data, and the CCT indicated represents the CCT corresponding to the maximum value of each Gaussian. The data for other paintings and conditions show similar patterns, although with somewhat different peak positions depending on the painting.

Fig. 5
Fig. 5

Observers’ responses (open symbols) represented by the frequency of illuminant selection expressed as a function of the CIE x D coordinate of the illuminant for the four conditions of the experiment. Data are pooled over observers and paintings. The solid curves represent Gaussian fits to the data, and the CCT indicated represents the CCT corresponding to the maximum value of each Gaussian. In the figure are also represented (bars) the frequency of occurrence of the maxima of the distributions of observers’ responses for individual paintings (for visualization purposes the frequency was scaled by a factor of 10).

Fig. 6
Fig. 6

Relative variation in the number of colors for two of the paintings (A and B) expressed as a function of the CIE x D coordinate of the daylight illuminant.

Fig. 7
Fig. 7

Correlation between observers’ preferences for each painting quantified as the CCT corresponding to the maximum of observers’ preferences and the CCT estimated from the maximum of the number of colors for the corresponding painting. Only nine symbols are visible due to superposition. The plot was based on the experimental data obtained in the laboratory. The straight line represents an unweighted linear regression. The proportion of variance R 2 accounted for in the regression was 0.68, which is statistically significant ( p = 0.02 ) .

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