We have investigated the visual texture properties of effect coatings. The key question addressed in this work is how the visual texture properties of effect coatings can be visually assessed in a reproducible way.We show that it is possible to define two important visual texture parameters: Diffuse Coarseness and Glint Impression. These two parameters describe the visual texture under two different illumination conditions: diffuse and directional lighting, respectively. Strict definitions of these illumination conditions were found to be crucial when discussing visual texture of effect coatings. For each of these two visual texture parameters, experiments were set up, and observation procedures were designed that standardize the observation conditions. Visual tests were performed on both visual texture parameters. We found good results in terms of observer-repeatability and observer-accuracy. Our results show that if the visual texture of effect coatings needs to be visually characterized under different illumination conditions, such as different light booths or changing weather conditions, both diffuse and directional illumination should be included separately.
We have investigated how texture and color combine when assessing the appearance of special effect coatings. In a previous study, the most important aspects of the texture of special effect coatings were identified as diffuse coarseness and glint impression. In the present study, objective measurements of these parameters are carried out using a recently developed instrument, and these data are combined with spectrophotometer data under six geometries. The instrumental data and visual data are analyzed using statistical techniques. From these visual data we were able to create a total appearance score which strongly correlates with the measurements of color and texture differences using this instrument. In this way a calculation procedure has been developed yielding predictions on acceptability that correlate well with visual judgments. Also, the added value of reflection data close to the gloss angle is demonstrated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.