Color rendering evaluation model for lighting of traditional Chinese freehand brushwork paintings in museums
Source abstract
Abstract
Traditional Chinese freehand brushwork paintings displayed in museums require lighting sources with high color rendering accuracy. However, due to their unique visual characteristics—such as low saturation, broad halos, and an emphasis on tonal gradation—existing general-purpose color rendering evaluation methods are often inadequate. In this study, a full-scale (1:1) replica of a traditional Chinese painting exhibition hall was constructed in a laboratory. Sixty representative spectral power distributions (SPDs) were used as lighting conditions, and four representative paintings were selected for evaluation. A color fidelity assessment was conducted with 34 participants. Five principal spectral components were extracted from each SPD and correlated with the fidelity evaluations using a neural network algorithm. Based on this analysis, a targeted color rendering evaluation model for lighting in traditional Chinese freehand brushwork painting exhibitions was developed. This model predicts the fidelity presentation value of a light source, with a validated average relative error of 9.7%.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Color Science and Applications · Color perception and design · Digital Media and Visual Art
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
Show source-provided references
1. National Cultural Heritage Administration. The 13th Five-Year Plan for the Development of National Cultural Relics[Z]. (2017).
2. Administration N. C. H. Nearly one billion people enter the museum [EB/OL]. http://www.sach.gov.cn/art/2019/1/10/art_1027_153349.html (2019).
3. Pinto, P. D., Linhares, J. M. M. & Nascimento, S. M. C. Correlated color temperature preferred by observers for illumination of artistic paintings. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 25, 623–630, https://doi.org/10.1364/JOSAA.25.000623 (2008).10.1364/JOSAA.25.000623 ↗
4. C.I. de L´Eclairage, CIE 157: 2004 Control of damage to museum objects by optical radiation, Vienna, Austria: CIE, 2004.
5. Standardization Administration of the People's Republic of China. Code for Lighting Design of Museums [S] (GB/T 23863–2024), (China Standards Press, Beijing, 2024).
6. Gao, F. et al. Traditional Chinese painting classification based on painting techniques. Chin. J. Comput.40, 2871–2882 (2017).
7. Hong, D. On the concept of ‘freehand’- meticulous painting and freehand brushwork painting. Art. Lit. Masses 15, 116 (2010).
8. Dang, R. et al. Chromaticity shifts due to light exposure of inorganic pigments used in traditional Chinese painting. Light. Res. Technol. 49, 818–828, https://doi.org/10.1177/1477153516644866 (2017).10.1177/1477153516644866 ↗
9. Royer, M. P. Tutorial: background and guidance for using the ANSI/IES TM-30 method for evaluating light source color rendition. LEUKOS J. Illuminat. Eng. Soc. North Am. 1–41. https://doi.org/10.1080/15502724.2020.1860771 (2021).10.1080/15502724.2020.1860771 ↗
10. Boyce, P. R. & Stampfli, J. R. LRT Digest 3: new colour metrics and their use. Light. Res. Technol. 51, 657–681, https://doi.org/10.1177/1477153519850006 (2019).10.1177/1477153519850006 ↗
11. Royer, M., Wilkerson, A. & Wei, M. Human perceptions of colour rendition at different chromaticities. Light. Res. Technol. 50, 965–994, https://doi.org/10.1177/1477153517725974 (2018).10.1177/1477153517725974 ↗
12. Innes, M. Correspondence: In support of the IES method of evaluating light source colour rendition. Light. Res. Technol. 47, 1029–1034, https://doi.org/10.1177/1477153515617392 (2015).10.1177/1477153515617392 ↗
13. Feltrin, F. et al. Impact of illumination correlated color temperature, background lightness, and painting color content on color appearance and appreciation of paintings. Leukos 16, 25–44, https://doi.org/10.1080/15502724.2018.1522261 (2020).10.1080/15502724.2018.1522261 ↗
14. Royer, M. P. et al. Human perceptions of colour rendition vary with average fidelity, average gamut, and gamut shape. Light. Res. Technol. 49, 966–991, https://doi.org/10.1177/1477153516663615 (2016).10.1177/1477153516663615 ↗
15. Luo, H. W. et al. Using LED technology to build up Museum lighting environment. in Proc. 12th Conference of AIC Colour, Tyne, UK, pp. 1757–1760, (2013).
16. Zhai, Q., Luo, M., Liu, X. The impact of illuminance and colour temperature on viewing fine art paintings under LED lighting. Light. Res. Technol. 1477153514541832. https://doi.org/10.1177/1477153514541832 (2014).10.1177/1477153514541832 ↗
17. Zhai, Q. Y., Luo, M. R. & Liu, X. Y. The impact of LED lighting parameters on viewing fine art paintings. Light. Res. Technol. 48, 711–725, https://doi.org/10.1177/1477153515578468 (2016).10.1177/1477153515578468 ↗
18. F. Szabó, R. Kéri, P. Csuti. The preferred conditions of LED lighting for fine art paintings: the influence of illuminance level and correlated colour temperature. in 1st International Museum Lighting Symposium, (London), pp. 50–52 (2017).
19. Wei, M., Bao, W. & Huang, H. P. Consideration of light level in specifying light source color rendition. LEUKOS 16, 55–65, https://doi.org/10.1080/15502724.2018.1448992 (2020).10.1080/15502724.2018.1448992 ↗
20. Bao W., Wei M. Change of Gamut Size for Producing Preferred Color Appearance from 20 to 15000 lux (Taylor & Francis, 2021). https://doi.org/10.1080/15502724.2019.1587621.10.1080/15502724.2019.1587621 ↗
21. Illuminating Engineering Society. ANSI/IES RP-30-20, Recommended Practice Lighting Museums[S], (Illuminating Engineering Society, New York, 2020).
22. Nascimento, S. M. C. & Masuda, O. Best lighting for visual appreciation of artistic paintings—experiments with real paintings and real illumination. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 31, A214, https://doi.org/10.1364/JOSAA.31.00A214 (2014).10.1364/JOSAA.31.00A214 ↗
23. Li, Z. et al. Indicators for lighting Chinese paintings in museums based on the protection standards and color preferences. Energy Build. 299, 9, https://doi.org/10.1016/j.enbuild.2023.113610 (2023).10.1016/j.enbuild.2023.113610 ↗
24. Dang, R., Wang, S., Sun, X., Tang, M. & Yang, D. The mathematical model for colour preference in Chinese painting within museum lighting environments. Color. Technol. 141, 238–248, https://doi.org/10.1111/cote.12778 (2025).10.1111/cote.12778 ↗
25. Dang, R. et al. The influence rules of illumination light source color parameters on the color trueness of chinese painting and its mathematical model. Leukos. 1–11. https://doi.org/10.1080/15502724.2025.2461300 (2025).10.1080/15502724.2025.2461300 ↗
26. Dang, R. et al. Data-based modeling of the light source color indicators on the Color Discrimination Index of the Forbidden City typical color paintings. Opt. Express 33, 8065–8082 (2025).10.1364/OE.547888 ↗
27. David, A. et al. Development of the IES method for evaluating the color rendition of light sources. Opt. Express 23, 15888–15906, https://doi.org/10.1364/OE.23.015888 (2015).10.1364/OE.23.015888 ↗
28. Niu, Kecheng. Chinese Painting in Colors: The Historical Development of Chinese Painting Styles and Forms (Hunan Fine Arts Publishing House, 2002).
29. Pan, T. A History of Chinese Painting (Unity Press, 2011).
30. Yu, F. & Liu, L. Research on the Colors of Chinese Paintings (Beijing United Publishing Co, 2013).
31. Rundquist, D. C. & Di, L. Band-moment analysis of imaging-spectrometer data. 55, 3–208 (1989).
32. Shams-Nateri, A. et al. Colorimetric Study on Polyamides Dyeing With Weld and Pomegranate Peel Natural Dyes. Cloth. Text. Res. J. 32, 124–115, https://doi.org/10.1177/0887302X14525658 (2014).10.1177/0887302X14525658 ↗
33. Das, A. & Mandal, M. Study of color degradation in package prints: Analyzing kinetics with principal component analysis. Color Res. Appl.49, 15, https://doi.org/10.1002/col.22924 (2024).10.1002/col.22924 ↗
34. Kaiser, H. F. The Application of Electronic Computers to Factor Analysis. Educ. Psychol. Meas. 20, 141–151, https://doi.org/10.1177/001316446002000116 (1960).10.1177/001316446002000116 ↗
35. Vol, N. Principal Component Analysis (2nd ed) (Book). J. Am. Stat. Assoc. 98, 1082–1083, https://doi.org/10.2307/30045356 (2003). page.10.2307/30045356 ↗
36. Gollisch, T. & Meister, M. Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina. Neuron 65, 150–164, https://doi.org/10.1016/j.neuron.2009.12.009 (2010).10.1016/j.neuron.2009.12.009 ↗
37. Liu X. L., Yin Z. Q., Yuan Y. S. “Visual neurophysiology,” (People’s Medical Publishing House 600, 2011).
38. Gazzaniga, M. S., Ivry, R. B. & Mangun, G. R. Cognitive neuroscience: the biology of the Mind. Q. Rev. Biol. (2002).
39. Feng, Y. Q. et al. Performance prediction and optimization of an organic Rankine cycle (ORC) for waste heat recovery using back propagation neural network. Energy Convers. Manag. 226, 113552. https://doi.org/10.1016/j.enconman.2020.113552 (2020).10.1016/j.enconman.2020.113552 ↗
40. Govindaraju, R. S. Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. 5, 115–123 (2000).10.1061/(ASCE)1084-0699(2000)5:2(115) ↗
41. Govindaraju, R. S. Artificial neural networks in hydrology. II: hydrologic applications. J. Hydrol. Eng. 5, 124–137 (2000).10.1061/(ASCE)1084-0699(2000)5:2(124) ↗
42. Dang, R., Guo, W., Luo, T. Correlated colour temperature index of lighting source for polychrome artworks in museums. Build. Environ. 185 https://doi.org/10.1016/j.buildenv.2020.107287 (2020).10.1016/j.buildenv.2020.107287 ↗
43. Royer, M. P. Tutorial: background and guidance for using the ANSI/IES TM-30 method for evaluating light source color rendition. LEUKOS 18, 191–231 (2021).10.1080/15502724.2020.1860771 ↗
44. Ghasemi, A. & Zahediasl, S. Normality tests for statistical analysis: a guide for non statisticians. Int. J. Endocrinol. Metab. 10, 486–489 (2012).10.5812/ijem.3505 ↗
45. Albaum, G., Best, R. & Hawkins, D. Continuous vs discrete semantic differential rating scales. Psychol. Rep. 49, 83–86 (1981).10.2466/pr0.1981.49.1.83 ↗
46. Khanh, T., Bodrogi, P., Guo, X., Vinh, Q. & Fischer, S. Colour preference, naturalness, vividness and colour quality metrics, part 5: a colour preference experiment at 2000 lx in a real room. Light. Res. Technol. 51, 262–279, https://doi.org/10.1177/1477153517737133 (2019).10.1177/1477153517737133 ↗
47. Wang, Y. et al. Low cycle fatigue life prediction of titanium alloy using genetic algorithm-optimized BP artificial neural network. Int. J. Fatigue 172, 107609 (2023).10.1016/j.ijfatigue.2023.107609 ↗