TU Darmstadt / ULB / TUprints

Prediction accuracy of L- and M-cone based human pupil light models

Zandi, Babak ; Klabes, Julian ; Khanh, Tran Quoc (2021):
Prediction accuracy of L- and M-cone based human pupil light models. (Publisher's Version)
In: Scientific Reports, 10 (1), Springer Nature, ISSN 2045-2322,
DOI: 10.26083/tuprints-00018628,

Available under CC BY 4.0 International - Creative Commons, Attribution.

Download (1MB) | Preview
Item Type: Article
Origin: Secondary publication via sponsored Golden Open Access
Status: Publisher's Version
Title: Prediction accuracy of L- and M-cone based human pupil light models
Language: English

Multi-channel LED luminaires offer a powerful tool to vary retinal receptor signals while keeping visual parameters such as color or brightness perception constant. This technology could provide new fields of application in indoor lighting since the spectrum can be enhanced individually to the users’ favor or task. One possible application would be to optimize a light spectrum by using the pupil diameter as a parameter to increase the visual acuity. A spectral- and time-dependent pupil model is the key requirement for this aim. We benchmarked in our work selected L- and M-cone based pupil models to find the estimation error in predicting the pupil diameter for chromatic and polychromatic spectra at 100 cd/m2. We report an increased estimation error up to 1.21 mm for 450 nm at 60–300 s exposure time. At short exposure times, the pupil diameter was approximately independent of the used spectrum, allowing to use the luminance for a pupil model. Polychromatic spectra along the Planckian locus showed at 60–300 s exposure time, a prediction error within a tolerance range of ± 0.5 mm. The time dependency seems to be more essential than the spectral dependency when using polychromatic spectra.

Journal or Publication Title: Scientific Reports
Journal volume: 10
Number: 1
Publisher: Springer Nature
Collation: 14 Seiten
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Light Technology
Date Deposited: 22 Jul 2021 07:33
Last Modified: 22 Jul 2021 07:33
DOI: 10.26083/tuprints-00018628
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-186288
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/18628
Actions (login required)
View Item View Item