Ojeda Juan, Nieves Juan Luis, Romero Javier
Appl Opt. 2017 Jul 1;56(19):G120-G127. doi: 10.1364/AO.56.00G120.
Despite the global and local daylight changes naturally occurring in natural scenes, the human visual system usually adapts quite well to those changes, developing a stable color perception. Nevertheless, the influence of daylight in modeling natural image statistics is not fully understood and has received little attention. The aim of this work was to analyze the influence of daylight changes in different high-order chromatic descriptors (i.e., color volume, color gamut, and number of discernible colors) derived from 350 color images, which were rendered under 108 natural illuminants with Correlated Color Temperatures (CCT) from 2735 to 25,889 K. Results suggest that chromatic and luminance information is almost constant and does not depend on the CCT of the illuminant for values above 14,000 K. Nevertheless, differences between the red-green and blue-yellow image components were found below that CCT, with most of the statistical descriptors analyzed showing local extremes in the range 2950 K-6300 K. Uniform regions and areas of the images attracting observers' attention were also considered in this analysis and were characterized by their patchiness index and their saliency maps. Meanwhile, the results of the patchiness index do not show a clear dependence on CCT, and it is remarkable that a significant reduction in the number of discernible colors (58% on average) was found when the images were masked with their corresponding saliency maps. Our results suggest that chromatic diversity, as defined in terms of the discernible colors, can be strongly reduced when an observer scans a natural scene. These findings support the idea that a reduction in the number of discernible colors will guide visual saliency and attention. Whatever the modeling is mediating the neural representation of natural images, natural image statistics, it is clear that natural image statistics should take into account those local maxima and minima depending on the daylight illumination and the reduction of the number of discernible colors when salient regions are considered.
尽管自然场景中自然发生的全球和局部日光变化存在，但人类视觉系统通常能很好地适应这些变化，形成稳定的颜色感知。然而，日光在自然图像统计建模中的影响尚未得到充分理解，也很少受到关注。这项工作的目的是分析日光变化对从350幅彩色图像中得出的不同高阶色度描述符（即颜色体积、色域和可辨别颜色数量）的影响，这些图像是在108种相关色温（CCT）从2735到25889K的自然光源下渲染的。结果表明，对于高于14000K的值，色度和亮度信息几乎是恒定的，并且不依赖于光源的CCT。然而，在该CCT以下发现了红绿色和蓝黄色图像分量之间的差异，分析的大多数统计描述符在2950K - 6300K范围内显示出局部极值。在该分析中还考虑了吸引观察者注意力的图像均匀区域和区域，并通过它们的斑块指数和显著性图来表征。同时，斑块指数的结果并未显示出对CCT的明显依赖性，值得注意的是，当用相应的显著性图对图像进行掩蔽时，可辨别颜色的数量平均减少了58%。我们的结果表明，当观察者扫描自然场景时，根据可辨别颜色定义的色度多样性可能会大幅降低。这些发现支持了这样一种观点，即可辨别颜色数量的减少将引导视觉显著性和注意力。无论何种建模在介导自然图像的神经表征、自然图像统计，很明显，自然图像统计应考虑那些取决于日光照明的局部最大值和最小值，以及在考虑显著区域时可辨别颜色数量的减少。
