Qi Lin, Chantler Mike J, Siebert J Paul, Dong Junyu
Department of Computer Science, Ocean University of China, Qingdao, China.
Texture Lab, Department of Computer Science, Heriot-Watt University, Edinburgh EH14 4AS, UK.
Vision Res. 2015 Oct;115(Pt B):209-17. doi: 10.1016/j.visres.2015.04.014. Epub 2015 May 9.
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.
计算机模拟刺激可以为在材料表面特性的视觉感知研究中创建人工场景提供一种灵活的方法。基于这种方法的先前研究报告称，表面粗糙度和光泽度特性相互依存，因此，对其中一个的感知会影响对另一个的感知。在这种情况下，粗糙度仅限于一种称为凹凸不平的表面特性。本文报告了一项关于在计算机模拟中，感知光泽度如何随与表面粗糙度相关的两个模型参数变化的研究：表面几何模型中的中尺度粗糙度参数和表面反射率模型中的微尺度粗糙度参数。我们使用真实世界环境地图来提供复杂照明，并使用基于物理的路径追踪器来渲染刺激。八名观察者参与了一个二项迫选实验，并根据联合测量模型对结果进行了测试。我们发现，尽管上述两个粗糙度参数都显著影响感知光泽度，但加法模型并不能充分描述它们的相互作用和非线性影响，这与先前的研究结果不同。我们研究了用于量化镜面高光的五个图像属性，并发现使用线性模型可以很好地预测感知光泽度。我们的研究结果为最近提出的用于材料感知的“统计外观模型”提供了计算支持。
