Spectral tuning of luminance cameras: A theoretical model and validation measurements

Abstract

Presently, luminance distribution measurement devices, using High Dynamic Range technology, are increasingly used as they provide a lot of relevant data related to the lit environment at once. However, the accuracy of these devices can be a concern. It is expected that the accuracy would be improved by incorporating the effect of the camera spectral responsivity and the spectral power distribution of the illuminant under which the measurements are conducted. This study introduces two optimization criteria incorporating these aspects to improve the spectral match and the performance of luminance distribution measurement devices. Both criteria are tested in a theoretical model and in practical measurements using two cameras and three illuminants: LED, halogen and fluorescent. Both methodologies support the hypothesis that the conventional method to determine the luminance introduces spectral mismatches that can be limited by optimizing relative to the spectral responsivity of the camera. Additionally, substantial evidence was found, by both the theoretical model and the validation measurements, that the spectral power distribution of the illuminant also has an effect on the performance.

HDR Imaging for Luminance and Melanopic Radiance: Cameras and Spectral Power Distributionsract Title

“…Additionally, 205 SPDs, as illustrated in Figure 2, of light sources that are commercially available were collected , containing LEDs (117), fluorescents (35), incandescent (17), halogens (31), metal halides (4) and sodium pressure lamps (1) from 300 nm to 900 nm with steps of 0,5 nm originating from the LSPDD database by Roby and Aubé (2012). The luminance, considering one single pixel, was calculated based on simulations, using MATLAB r2017a, for all 205 SPDs using the spectral responsivities of the six cameras and t wo distinct luminance models that were proposed by Kruisselbrink et al (Kruisselbrink et al, 2019). These models determine the weighting factors of the R, G, and B tristimuli according to the conventional and a spectral mismatch indicator ( 1 ′ ) optimization.…”

Collection of papers accepted for the 5th CIE Symposium on Colour and Visual Appearance, April 21–22, 2020, Hong Kong, CN

“…Additionally, 205 SPDs, as illustrated in Figure 2, of light sources that are commercially available were collected , containing LEDs (117), fluorescents (35), incandescent (17), halogens (31), metal halides ( 4) and sodium pressure lamps (1) from 300 nm to 900 nm with steps of 0,5 nm originating from the LSPDD database by Roby and Aubé (2012). The luminance, considering one single pixel, was calculated based on simulations, using MATLAB r2017a, for all 205 SPDs using the spectral responsivities of the six cameras and t wo distinct luminance models that were proposed by Kruisselbrink et al (Kruisselbrink et al, 2019). These models determine the weighting factors of the R, G, and B tristimuli according to the conventional and a spectral mismatch indicator (𝑓 1 ′ ) optimization.…”

“…The weighting factors that were found were applied in a similar fashion as the conventional method. For an extensive explanation of this model we refer to (Kruisselbrink et al, 2019).…”

“…The luminance is generally calculated using fixed weighting factors for the R, G, and B tristimuli originating from the translation of the sRGB to XYZ colour space, irrespective of camera spectral responsivity and scene illuminant. It is believed that this model which uses these fixed weighting factors has an negative impact on the measurement accuracy as they can introduce significant spectral mismatches due to its assumptions (Cauwerts et al, 2019;Kruisselbrink et al, 2019). First, the Spectral Power Distribution (SPD) is generally not identical to the CIE Standard Illuminant D65.…”

Literature review - Energy saving potential of user-centered integrated lighting solutions

“…In addition to the above, an attempt to retrieve spectral information has been reported, using luminous maps generated via HDR imaging, aimed at low-cost, long-term monitoring of circadian potential of spaces (Jung and Inanici, 2019). The reliability of the method needs to be improved since the measurement is affected by various factor, for example, the illuminant (Kruisselbrink, Dangol, Rosemann and van Loenen, 2019). Addition, it is reported that camera specific calibration can lead to robust results (Cauwerts, Jost and Deroisy, 2019) .…”