Quantifying the spatial, temporal, angular and spectral structure of effective daylight in perceptually meaningful ways

A hyperspectral open-source imager (HOSI)

BACKGROUND

The spatial and spectral properties of the light environment underpin many aspects of animal behaviour, ecology and evolution, and quantifying this information is crucial in fields ranging from optical physics, agriculture/plant sciences, human psychophysics, food science, architecture and materials sciences. The escalating threat of artificial light at night (ALAN) presents unique challenges for measuring the visual impact of light pollution, requiring measurement at low light levels across the human-visible and ultraviolet ranges, across all viewing angles, and often with high within-scene contrast.

RESULTS

Here, I present a hyperspectral open-source imager (HOSI), an innovative and low-cost solution for collecting full-field hyperspectral data. The system uses a Hamamatsu C12880MA micro spectrometer to take single-point measurements, together with a motorised gimbal for spatial control. The hardware uses off-the-shelf components and 3D printed parts, costing around £350 in total. The system can run directly from a computer or smartphone with a graphical user interface, making it highly portable and user-friendly. The HOSI system can take panoramic hyperspectral images that meet the difficult requirements of ALAN research, sensitive to low light around 0.001 cd.m-2, across 320-880 nm range with spectral resolution of ~ 9 nm (FWHM) and spatial resolution of ~ 2 cycles per degree. The independent exposure of each pixel also allows for an extremely wide dynamic range that can encompass typical natural and artificially illuminated scenes, with sample night-time scans achieving full-spectrum peak-to-peak dynamic ranges of > 50,000:1.

CONCLUSIONS

This system's adaptability, cost-effectiveness and open-source nature position it as a valuable tool for researchers investigating the complex relationships between light, environment, behaviour, ecology and biodiversity, with further potential uses in many other fields.

Time-of-day perception in paintings

The spectral shape, irradiance, direction, and diffuseness of daylight vary regularly throughout the day. The variations in illumination and their effect on the light reflected from objects may in turn provide visual information as to the time of day. We suggest that artists' color choices for paintings of outdoor scenes might convey this information and that therefore the time of day might be decoded from the colors of paintings. Here we investigate whether human viewers' estimates of the depicted time of day in paintings correlate with their image statistics, specifically chromaticity and luminance variations. We tested time-of-day perception in 17th- to 20th-century Western European paintings via two online rating experiments. In Experiment 1, viewers' ratings from seven time choices varied significantly and largely consistently across paintings but with some ambiguity between morning and evening depictions. Analysis of the relationship between image statistics and ratings revealed correlations with the perceived time of day: higher "morningness" ratings associated with higher brightness, contrast, and saturation and darker yellow/brighter blue hues; "eveningness" with lower brightness, contrast, and saturation and darker blue/brighter yellow hues. Multiple linear regressions of extracted principal components yielded a predictive model that explained 76% of the variance in time-of-day perception. In Experiment 2, viewers rated paintings as morning or evening only; rating distributions differed significantly across paintings, and image statistics predicted people's perceptions. These results suggest that artists used different color palettes and patterns to depict different times of day, and the human visual system holds consistent assumptions about the variation of natural light depicted in paintings.

Misidentifying illuminant changes in natural scenes due to failures in relational colour constancy

The colours of surfaces in a scene may not appear constant with a change in the colour of the illumination. Yet even when colour constancy fails, human observers can usually discriminate changes in lighting from changes in surface reflecting properties. This operational ability has been attributed to the constancy of perceived colour relations between surfaces under illuminant changes, in turn based on approximately invariant spatial ratios of cone photoreceptor excitations. Natural deviations in these ratios may, however, lead to illuminant changes being misidentified. The aim of this work was to test whether such misidentifications occur with natural scenes and whether they are due to failures in relational colour constancy. Pairs of scene images from hyperspectral data were presented side-by-side on a computer-controlled display. On one side, the scene underwent illuminant changes and on the other side, it underwent the same changes but with images corrected for any residual deviations in spatial ratios. Observers systematically misidentified the corrected images as being due to illuminant changes. The frequency of errors increased with the size of the deviations, which were closely correlated with the estimated failures in relational colour constancy.