The aim of this article is to propose a model to automatically predict visual judgement of sparkle and graininess of special effect pigments used in industrial coatings. Many applications in the paint and coatings, printing and plastics industry rely on multi-angle color measurements with the aim of properly characterizing the appearance, i.e., the color and texture of the manufactured surfaces. However, when it comes to surfaces containing effect pigments, these methods are in many cases insufficient and it is particularly texture characterization methods that are needed. There are two attributes related to texture that are commonly used: (1) diffuse coarseness or graininess and (2) sparkle or glint impression. In this paper, we analyzed visual perception of both texture attributes using two different psychophysical studies of 38 samples painted with effect coatings including different effect pigments and 31 test persons. Our previous work has shown a good agreement between a study using physical samples with one that uses high-resolution photographs of these sample surfaces. We have also compared the perceived (1) graininess and (2) sparkle with the performance of two commercial instruments that are capable of capturing both attributes. Results have shown a good correlation between the instruments’ readings and the psychophysical studies. Finally, we implemented computational models predicting these texture attributes that have a high correlation with the instrument readings as well as the psychophysical data. By linear scaling of the predicted data using instruments readings, one can use the proposed model for the prediction of graininess and both static and dynamic sparkle values.

本文的目的是提出一个模型来自动预测工业涂料中使用的特殊效果颜料的闪光和颗粒度的视觉判断。油漆和涂料、印刷和塑料行业的许多应用依赖于多角度颜色测量，目的是正确表征外观，即制造表面的颜色和纹理。然而，当涉及到含有效果颜料的表面时，这些方法在许多情况下是不够的，尤其需要纹理表征方法。有两个与纹理相关的常用属性：(1) 漫反射粗糙或颗粒感和 (2) 闪光或闪光印象。在本文中，我们对 38 个涂有效果涂层（包括不同效果颜料）的样品和 31 名测试人员进行了两项不同的心理物理学研究，分析了两种纹理属性的视觉感知。我们之前的工作表明，使用物理样品的研究与使用这些样品表面的高分辨率照片的研究之间存在良好的一致性。我们还将感知的 (1) 颗粒感和 (2) 闪光与能够捕获这两种属性的两种商业仪器的性能进行了比较。结果表明仪器读数与心理物理学研究之间存在良好的相关性。最后，我们实施了计算模型来预测这些与仪器读数以及心理物理数据具有高度相关性的纹理属性。