Department of Computer Science and Technology University of Cambridge, Cambridge, UK.
Applied Perception Science Group Meta, Sunnyvale, CA, USA.
J Vis. 2024 Apr 1;24(4):5. doi: 10.1167/jov.24.4.5.
The contrast sensitivity function (CSF) is a fundamental visual model explaining our ability to detect small contrast patterns. CSFs found many applications in engineering, where they can be used to optimize a design for perceptual limits. To serve such a purpose, CSFs must explain possibly a complete set of stimulus parameters, such as spatial and temporal frequency, luminance, and others. Although numerous contrast sensitivity measurements can be found in the literature, none fully explains the complete space of stimulus parameters. Therefore, in this work, we first collect and consolidate contrast sensitivity measurements from 18 studies, which explain the sensitivity variation across the parameters of interest. Then, we build an analytical contrast sensitivity model that explains the data from all those studies. The proposed castleCSF model explains the sensitivity as the function of spatial and temporal frequencies, an arbitrary contrast modulation direction in the color space, mean luminance, and chromaticity of the background, eccentricity, and stimulus area. The proposed model uses the same set of parameters to explain the data from 18 studies with an error of 3.59 dB. The consolidated contrast sensitivity data and the code for the model are publicly available at https://github.com/gfxdisp/castleCSF/.
对比敏感度函数（CSF）是解释我们检测小对比度模式能力的基本视觉模型。CSF 在工程中有许多应用，可以用于优化设计以适应感知极限。为了达到这样的目的，CSF 必须解释可能的完整的刺激参数集，如空间和时间频率、亮度等。尽管文献中可以找到许多对比敏感度测量，但没有一个能完全解释刺激参数的完整空间。因此，在这项工作中，我们首先从 18 项研究中收集和整合了对比敏感度测量结果，这些结果解释了对感兴趣的参数的敏感度变化。然后，我们构建了一个分析对比敏感度模型，该模型解释了来自所有这些研究的数据。所提出的 castleCSF 模型将灵敏度解释为空间和时间频率、颜色空间中任意对比度调制方向、背景的平均亮度和色度、偏心度和刺激区域的函数。该模型使用相同的参数集来解释来自 18 项研究的数据，误差为 3.59dB。整合的对比敏感度数据和模型的代码可在 https://github.com/gfxdisp/castleCSF/ 上公开获取。
