Prediction model for perceptual gloss by physical measurement of flat objects

Although a physical gloss exists as a physically measurable index, people can also perceive a perceptual gloss on object surfaces. However, the physical gloss does not always match the perceptual gloss. Because the physical gloss is calculated based only on the specular gloss and does not reflect other physical features that affect the perceptual gloss. Thus, we analysed the relationships between physical features and perceptual gloss by measuring many physical properties of object surfaces, including their physical gloss. We prepared 127 flat objects comprising three materials: paper, resin and metal plating. The perceptual gloss was visually evaluated for observation angles of 20°, 60° and 85° using a magnitude estimation method. Multiple measurements were conducted to obtain physical features such as the gloss unit (GU), haze, distinctness of image and high dynamic range luminance. We then constructed prediction models for the perceptual gloss using these physical features and multiple regression analyses. By combining these multiple physical quantities and using the GU in the power scale, the prediction accuracy was improved. By the optimal power index (0.33 for physical gloss in the common prediction model, independent of the observation angle), we found that human gloss perception may be related to brightness perception.