The influence of Fresnel effects on gloss perception

Influence of Fresnel effects on the glossiness and perceived depth of depth-scaled glossy objects

Fresnel effects, that is, shape-dependent changes in the strength of specular reflection from glossy objects, can lead to large changes in reflection strength when objects are scaled along the viewing axis. In an experiment, we scaled sphere-like bumpy objects with fixed material parameters in the depth direction and then measured with and without Fresnel effects how this influences the gloss impression, gloss constancy, and perceived depth. The results show that Fresnel effects in this case lead to a strong increase in gloss with depth, indicating lower gloss constancy than without them, but that they improve depth perception. In addition, we used inverse rendering to investigate the extent to which Fresnel effects in a rendered image limit the possible object shapes in the underlying scene. We found that, for a static monocular view of an unknown object, Fresnel effects by themselves provide only a weak constraint on the overall shape of the object.

Analysis of Gloss Unevenness and Bidirectional Reflectance Distribution Function in Specular Reflection

Gloss is associated significantly with material appearance, and observers often focus on gloss unevenness. Gloss unevenness is the intensity distribution of reflected light observed within a highlight area, that is, the variability. However, it cannot be analyzed easily because it exists only within the highlight area and varies in appearance across the reflection angles. In recent years, gloss has been analyzed in terms of the intensity of specular reflection and its angular spread, or the bidirectional reflectance distribution function (BRDF). In this study, we develop an apparatus to measure gloss unevenness that can alter the angle with an angular resolution of 0.02°. Additionally, we analyze the gloss unevenness and BRDF in terms of specular reflection. Using a high angular resolution, we measure and analyze high-gloss materials, such as mirrors and plastics, and glossy materials, such as photo-like inkjet paper and coated paper. Our results show that the magnitude of gloss unevenness is the largest at angles marginally off the center of the specular reflection angle. We discuss an approach for physically defining gloss unevenness based on the BRDF.

Visually Significant Dimensions and Parameters for Gloss

The appearance of a surface depends on four main appearance attributes, namely color, gloss, texture, and translucency. Gloss is an important attribute that people use to understand surface appearance, right after color. In the past decades, extensive research has been conducted in the field of gloss and gloss perception, with different aims to understand the complex nature of gloss appearance. This paper reviews the research conducted on the topic of gloss and gloss perception and discusses the results and potential future research on gloss and gloss perception. Our primary focus in this review is on research in the field of gloss and the setup of associated psychophysical experiments. However, due to the industrial and application-oriented nature of this review, the primary focus is the gloss of dielectric materials, a critical aspect in various industries. This review not only summarizes the existing research but also highlights potential avenues for future research in the pursuit of a more comprehensive understanding of gloss perception.

Glossy Appearance Editing for Heterogeneous Material Objects

Abstract With the proliferation of smartphones and social networking services, the opportunities for individuals to take photographs have increased exponentially. In a previous study, the perceived gloss of an object was reduced by its representing as a digital image and compared with a real object. It is also known that image editing, such as lossy image compression, can reduce the glossiness of an image. Therefore, the glossiness of real objects may be easily changed in digital images; thus, a method for appropriately editing the gloss in digital images is required for post-processing. In this study, we propose a gloss appearance editing method for various material objects in a single digital image. The proposed method consists of three steps: color space conversion, gloss detection, and gloss editing. The relationship between the proposed method and the respective reflection models of inhomogeneous objects, metallic objects, and translucent objects was analyzed. Consequently, we determined that the gloss editing of the proposed method is equivalent to editing the specular reflection component of an inhomogeneous object, the grazing reflection component of a metallic object, and the specular reflection component of a translucent object. We applied the proposed method to test images including objects of various materials and confirmed its effectiveness through a subjective evaluation by visual inspection and an objective evaluation using image statistics.

The effect of scene articulation on transparent layer constancy

In this article, we examine the influence of scene articulation on transparent layer constancy. We argue that the term articulation may be understood as an aspect of the more general concept naturalness of a stimulus that relates to the degree of enrichment compared with a minimal stimulus and to the extent to which a stimulus contains regularities that are typically found in natural scenes. We conducted two matching experiments, in which we used strongly reduced scenes and operationalized articulation by the number of background reflectances (numerosity). The results of the first experiment show that higher numerosity actually leads to an increase in transparent layer constancy when reflectances are randomly drawn from a fixed population. However, this advantage disappears if the spatial mean and the variation of the subset colors are controlled as in our second experiment. Furthermore, our results suggest that the mechanism underlying transparent layer constancy leads to a rather stable compromise between two matching criteria, namely, proximal identity and constant filter properties according to our perceptual model. For filters with an additive component, which appear more or less hazy, we observed improved recovered filter properties and correspondingly higher degrees of transparent layer constancy, suggesting an additional mechanism in this type of filter.

Translucency perception: A review

Translucency is an optical and a perceptual phenomenon that characterizes subsurface light transport through objects and materials. Translucency as an optical property of a material relates to the radiative transfer inside and through this medium, and translucency as a perceptual phenomenon describes the visual sensation experienced by humans when observing a given material under given conditions. The knowledge about the visual mechanisms of the translucency perception remains limited. Accurate prediction of the appearance of the translucent objects can have a significant commercial impact in the fields such as three-dimensional printing. However, little is known how the optical properties of a material relate to a perception evoked in humans. This article overviews the knowledge status about the visual perception of translucency and highlights the applications of the translucency perception research. Furthermore, this review summarizes current knowledge gaps, fundamental challenges and existing ambiguities with a goal to facilitate translucency perception research in the future.

Perceived roughness of glossy objects: The influence of Fresnel effects and correlated image statistics

The roughness of a shiny surface determines how sharp the reflected image of the surroundings is, and thus whether the surface appears highly glossy or more or less matte. In a matching experiment, subjects were asked to reproduce the perceived roughness of a given surface (standard) in a comparison stimulus (match), where the standard and the match could differ in both shape and illumination. To compare the effect of the reflection model on the accuracy of the settings, this was done for two different reflectance models (bidirectional reflectance distribution function [BRDF]). The matching errors were smaller, that is, the constancy under shape and illumination changes higher, when Fresnel effects were physically correctly reproduced in the reflectance model (Fresnel-BRDF) than when this was not the case (Ward-BRDF). The subjects' settings in the experiment can be predicted very well by two image statistics, one of which is based on the mean edge strength and the other on a local discrete cosine transform. In particular, these predictions also reflect the empirically observed advantage of the Fresnel-BRDF. These results show that the constancy of perceived roughness across context changes may depend on the BRDF used, with Fresnel effects playing a significant role. The good prediction of subjects' settings using the two image statistics suggests that local brightness variance, which affects both image statistics, can be used as a valid cue for surface roughness.

The joint role of geometry and illumination on material recognition

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly nonlinear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly nonlinear models and humans may use similar high-level factors for material recognition tasks.