The joint effect of mesoscale and microscale roughness on perceived gloss

Qi, L., Chantler, M. J., Siebert, J. P. and Dong, J. (2015) The joint effect of mesoscale and microscale roughness on perceived gloss. Vision Research, 115(Part B), pp. 209-217. (doi: 10.1016/j.visres.2015.04.014) (PMID:25969141)

Abstract

Computer simulated stimuli can provide a flexible method for creating artificial scenes in the study of visual perception of material surface properties. Previous work based on this approach reported that the properties of surface roughness and glossiness are mutually interdependent and therefore, perception of one affects the perception of the other. In this case roughness was limited to a surface property termed bumpiness. This paper reports a study into how perceived gloss varies with two model parameters related to surface roughness in computer simulations: the mesoscale roughness parameter in a surface geometry model and the microscale roughness parameter in a surface reflectance model. We used a real-world environment map to provide complex illumination and a physically-based path tracer for rendering the stimuli. Eight observers took part in a 2AFC experiment, and the results were tested against conjoint measurement models. We found that although both of the above roughness parameters significantly affect perceived gloss, the additive model does not adequately describe their mutually interactive and nonlinear influence, which is at variance with previous findings. We investigated five image properties used to quantify specular highlights, and found that perceived gloss is well predicted using a linear model. Our findings provide computational support to the ‘statistical appearance models’ proposed recently for material perception.

• Item Type: Articles
• Keywords: Rough surfaces; Perceived gloss; Mesoscale roughness; Microscale roughness; Conjoint measurement
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Siebert, Dr Paul
• Authors: Qi, L., Chantler, M. J., Siebert, J. P., and Dong, J.
• College/School: College of Science and Engineering > School of Computing Science
• Research Group: Computer Vision and Graphics
• Journal Name: Vision Research
• Publisher: Elsevier
• ISSN: 0042-6989
• ISSN (Online): 1878-5646