A neural response-based model to predict discomfort glare from luminance image. (May 2018)

• Record Type: Journal Article
• Authors: Safdar, M; Luo, M Ronnier; Mughal, M Farhan; Kuai, S; Yang, Y; Fu, L; Zhu, X
• Is Part Of: Lighting research & technology. Volume 50:Number 3(2018)
• Page Start: 416
• Page End: 428
• Publication Date: 2018-05
• Subjects: Lighting -- Periodicals; 621.32
• DOI: 10.1177/1477153516675910
• Languages: English
• ISSNs: 1477-1535

Abstract

It is well known that one of the problems of the current method for discomfort glare evaluation, called the unified glare rating, is the non-uniform luminance of the glare source. This paper addresses this issue by considering the spatial contrast of luminance as a measure of non-uniformity. An image-based metric is proposed to evaluate discomfort glare by modeling the neural response of human vision. The model takes an absolute luminance image as input and predicts visual discomfort based on the spatial distribution of the luminance of the stimulus and the background. The developed model was tested to predict subjective glare ratings based on an experiment conducted using non-uniform LED sources with symmetric and asymmetric patterns of LEDs, and its performance was compared with the unified glare rating. As expected, the unified glare rating predictions correlated well with the subjective glare evaluations of luminaires with symmetric patterns of LEDs (as they appear less non-uniform) but not for those with asymmetric patterns. Results showed that the developed model, named the Neural Response-based Glare Model, gave similar performance to unified glare rating for symmetric patterns but outperformed UGR for asymmetric patterns of LEDs.