Artificial neural networks for optimising camera-based colour measurements of prints enhanced with pearlescent pigments

Abstract

© 2018 The Authors. Coloration Technology © 2018 Society of Dyers and Colourists Pearlescent pigments are widely used in printing due to their optical, chemical and physical properties. To analyse the effects of goniochromism they produce, the colorimetric characterisation of materials printed with pearlescent pigments requires multi-angular measurements. In this study, the colours of prints enhanced with pearlescent pigments were measured by means of a digital camera, relying on the empirical camera characterisation method. Since this method is time-consuming, it was altered to enable estimates of colorimetric values for different geometries to be measured on the basis of images captured at one viewing angle. This approach was based on the use of artificial neural networks which were shown to provide sufficient flexibility for the given task. The results indicate that the images obtained at the viewing angle of 45° aspecular (measuring geometry 45°/asp 45°) accurately estimate CIELab values for all of the tested measuring geometries. The proposed method is therefore not only time-efficient but also reduces the associated errors due to the camera's movement, and enables the estimation of colorimetric values for those viewing angles inaccessible by camera.