Gloss evaluation and prediction of achromatic low‐gloss textured surfaces from the automotive industry

In this article, we investigate the benefit of including texture information in models of gloss perception of low-gloss textured achromatic plastic surfaces from the automotive industry. 4 models are compared: two gloss prediction models including texture information, one using data from reflectometry (M1) and one using data from goniophotometry (M2), and two models using data from reflectometry (M3) or goniophotometry (M4) alone. Both texture-corrected models (M1-M2) outclass the uncorrected intensity-based models, mainly because they are made texture invariant. Although the texture-corrected reflectometer-based prediction (M1) correlates rather well with sensory data, a more consistent fit is obtained by mixing textural to goniophotometric data (M2). This can be explained by the fact that contrast gloss is better than multiangle specular gloss at reflecting the observer's gloss evaluation strategy. (c) 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 154-164, 2016