1. To understand the function of colour signals in nature, we require robust quantitative analytical frameworks to enable us to estimate how animal and plant colour patterns appear against their natural background as viewed by ecologically relevant species. Due to the quantitative limitations of existing methods, colour and pattern are rarely analysed in conjunction with one another, despite a large body of literature and decades of research on the importance of spatio‐chromatic colour pattern analyses. Furthermore, key physiological limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor abundances and receptor noise levels are rarely considered together in colour pattern analyses.
2. Here, we present a novel analytical framework, called the Quantitative Colour Pattern Analysis (QCPA). We have overcome many quantitative and qualitative limitations of existing colour pattern analyses by combining calibrated digital photography and visual modelling. We have integrated and updated existing spatio‐chromatic colour pattern analyses, including adjacency, visual contrast and boundary strength analysis, to be implemented using calibrated digital photography through the Multispectral Image Analysis and Calibration (MICA) Toolbox.
3. This combination of calibrated photography and spatio‐chromatic colour pattern analyses is enabled by the inclusion of psychophysical colour and luminance discrimination thresholds for image segmentation, which we call ‘Receptor Noise Limited Clustering’, used here for the first time. Furthermore, QCPA provides a novel psycho‐physiological approach to the modelling of spatial acuity using convolution in the spatial or frequency domains, followed by ‘Receptor Noise Limited Ranked Filtering’ to eliminate intermediate edge artefacts and recover sharp boundaries following smoothing. We also present a new type of colour pattern analysis, the ‘local edge intensity analysis’ as well as a range of novel psycho‐physiological approaches to the visualization of spatio‐chromatic data.
4. QCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, free and open source toolbox and introduces a range of novel analytical and data‐visualization approaches. These analyses and tools have been seamlessly integrated into the MICA toolbox providing a dynamic and user‐friendly workflow.

中文翻译：

---

定量颜色图案分析（QCPA）：用于分析自然颜色图案的综合框架

1. 为了了解颜色信号在自然界中的功能，我们需要强大的定量分析框架，以使我们能够估计动，植物颜色模式在自然背景下如何被生态相关物种所观察到。由于现有方法的定量局限性，尽管有大量文献和数十年来关于时空色彩色图案分析的重要性的研究，但很少将颜色和图案彼此结合进行分析。此外，动物视觉系统的关键生理局限性，例如空间敏锐度，光谱敏感性，感光体丰度和受体噪声水平，很少在颜色模式分析中一起考虑。
2. 在这里，我们提出了一种新颖的分析框架，称为定量颜色图案分析（QCPA）。通过将校准的数字摄影和视觉建模相结合，我们克服了现有色彩模式分析的许多定量和定性限制。我们已经集成和更新了现有的时空色彩色图案分析，包括邻接，视觉对比和边界强度分析，将通过多光谱图像分析和校准（MICA）工具箱使用校准的数字摄影来实施。
3. 通过将用于图像分割的心理颜色和亮度辨别阈值包括在内，可以将校准的摄影和时空色模式分析结合起来，这在本文中是首次使用，我们称之为“受体噪声受限聚类”。此外，QCPA为空间敏锐度建模提供了一种新颖的心理生理方法，即使用空间或频域中的卷积，然后进行“受体噪声有限排序滤波”，以消除中间边缘伪像并在平滑后恢复锐利边界。我们还提出了一种新型的颜色模式分析，“局部边缘强度分析”以及一系列新颖的心理生理方法，以使时空色数据可视化。
4. QCPA将新颖的和现有的模式分析框架整合到了我们希望成为一个统一，免费和开源的工具箱中，并引入了一系列新颖的分析和数据可视化方法。这些分析和工具已无缝集成到MICA工具箱中，从而提供了动态且用户友好的工作流程。