Numerical correlation between non-visual metrics and brightness metrics—implications for the evaluation of indoor white lighting systems in the photopic range

Abstract

Abstract From the beginning of the $$21\textrm{st}$$ 21 st century until today, the demand for lighting systems includes not only visual parameters (brightness, contrast perception, color quality), but also non-visual parameters. It is necessary to define the new non-visual parameters for the realization of the new concept of Human Centric Lighting (HCL) or Integrative Lighting. As a contribution to this approach, many international research groups have tried to quantify the non-visual parameters such as Circadian Stimulus by Rea et. al. in USA ( $$CS_{2018}$$ C S 2018 , $$CS_{2021}$$ C S 2021 ), Melanopic Equivalent Daylight ( $$D_{65}$$ D 65 ) illuminance, mEDI of the CIE S 026/E:2018 or the latest formula by Giménez et al., for the nocturnal melatonin suppression. Therefore, it is necessary to analyze the correlation between these non-visual metrics and brightness metrics such as the equivalent luminance of Fotios et al., or the latest brightness model of TU Darmstadt so that scientists, lighting engineers and lighting system users can correctly apply them in their work. In this context, this paper attempts to investigate and analyze these correlations between the three metric groups based on the database of 884 light sources of different light source technologies and daylight spectra. The obtained results show that the latest Circadian Stimulus model of Rea et. al. $$CS_{2021}$$ C S 2021 with the improvement of Circadian Light $$CL_{A,2021}$$ C L A , 2021 ( $$CL_{A\,2.0}$$ C L A 2.0 ) has solved the disadvantage of $$CS_{2018}$$ C S 2018 , especially for the interrupted point between warm and cold white (about $$3710\,K$$ 3710 K ) or the junction between negative and positive signal of the opponent channel ( $$B -(L+M)$$ B - ( L + M ) ). Moreover, these three metrics of the three research groups contain a high correlation coefficient, so that one metric can be transformed by linear functions to the other two parameters.