As the project of the author’s Master’s thesis, the development of a spectral and colorimetric database of artist paint materials for acrylic paints was started. The goal of this research project was to:
- provide the academic resource of colorant spectral characteristics
- give scientific explanations on various paint-particular phenomena (paint mixing, gloss effects and color gamut expansion by varnishing)
These tasks were planned to satisfy possible interests on paint research from not only conservators in museums but also color educators in schools and color reproduction engineers in imaging companies.
First of all, the coverage of this research was narrowed down to matte acrylic paints that are made from traditional organic and synthetic pigments. That is, the paints of relatively brand-new colorants, fluorescent, metallic, and pearlescent pigments, were not considered herein.
The first mission of the database development was to build up the optical properties of artist paint colors based on spectrophotomeric measurement. The contents of the artist paint database were represented by spectral light characteristics, reflectance, absorption, and scattering, so as to simulate specific paint mixtures with the dataset. Since it is derived by a different concept from some encyclopaedic databases that have been introduced to show basic physical and optical parameters such as pigments’ refractive indices and light reflectivities for pigment-binder combinations, our colorant database will be useful for color science researchers as an academic resource.
The advantage of holding the spectral dataset in our database can help spectral im aging researchers in a couple of ways. According to the recent study on the spectral imaging camera system by Mohammadi and Berns, for example, it clarified that appropriate spectral-reflectance curves of a calibration target should be chosen rather than increasing the number of target paints for spectrally accurate calibration. For such a case, a colorant formulation using the spectral dataset will lead to determining the optimal recipes of acrylic paints providing appropriate reflectance curves to the development of a calibration paint target. Another possible usage of the spectral dataset is for pigment identification, the spectral matching technique of which is to find the recipes of paint mixtures, used in art, from a large amount of colorant spectral information.
The second mission was to simulate paint mixtures with mathematical calculations and verify the accuracy of the simulation performance. The simulation approach, based on the Kubelka-Munk theory, can be considered to have larger possible choices of paints and give better simulation results than gained by a traditional approach based on a look-up table method that characterizes a paint gradient in the L*a*b* coordinates. The spectral-based technique of paint mixing simulation is expected to reveal the contradiction of advocated paint-mixing theory, “yellow, red, and blue can make any colors because these colors are the primaries of the paint system”, to color educators because it is actually not. With regard to paint mixing theory, a professional artist, Michael Wilcox, experientially showed the contradiction by making a large amount of paint mixing palettes.
The third mission was to render paint color gamuts with selected primary paint mixtures and multiple paint mixtures based on the spectral database. Rendering a paint color gamut is an orthodox approach to visualize the possible color coverage of certain paint mixtures. In terms of color reproduction, additionally, it is interesting to compare color gamut volumes in the L*a*b* space among various coloration systems including the paint system. A general target for calibrating cameras such as the GretagMacbeth ColorChecker is expected to cover a wide gamut far beyond a paint system. Quantifying the color gamut volumes of the systems would be helpful not only estimate color reproduction accuracy but also to develop a paint-color rendering chart for spectral imaging camera.
The last mission was to formulate the gloss effects by varnishing. In conservation works in museums, applying, retouching, and removing a varnish coat are carried out with verifying the apperance of re-painted colors over or without a varnish coat by operators. In order to reproduce the original paint colors, it is necessary for conservators to understand how a new varnish coat makes paint colors changed and visually wet in advance. A scientific explanation on the mechanism of gloss by varnishing will guide conservators to take an appropriate step of choosing a varnish substance as well as an application method to produce their expected surface finishings.
