Brightness of a Space in Terms of Variation of Luminance

Ko, Byoungwoo; Koga, Tomohiro; Lü, Bin; Hirate, Kotaroh; Mizuno, Masaaki; Sato, Naoyuki

doi:10.2150/jieij.97.429

“…This also makes it difficult to say that any individual study's results could not also be explained by alternative models. The only study here that does report multiple metrics is Ko et al (2013), and the only metric that they have in common with the other studies here is max/min luminance ratio. It performed relatively poorly in their study, with a correlation coefficient of 0.31 and R 2 of 0.096 (N=51, p<0.05) 20 , explaining only ~10% of the 20 Unfortunately, Ko et al did not plot the metrics they rejected, or present statistical information beyond correlation coefficients, making it hard to evaluate how large the errors were in practical terms.…”

Section: Discussionmentioning

confidence: 73%

Measuring the Effects of Light Distribution on Spatial Brightness

Sullivan¹

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By changing the light distribution it is possible to double the apparent amount of light in a space without any increase in its overall luminance. If one simply assumes that the apparent amount of light in a space — its spatial brightness — is described by its mean luminance (or similar measures) then substantial errors may be made. We carried out two experiments, measuring the brightness of 19 different model spaces. Our results demonstrate that making light distributions more non-uniform can make spaces appear both significantly brighter and significantly darker, depending on how the light distribution is changed. This challenges most existing studies in the field that argue that non-uniformity of the luminance distribution simply makes spaces look darker. Indeed, the observed pattern in brightness between our conditions cannot be consistently explained by a simple measure of the uniformity of the luminance distribution. We thus reject all previously proposed models of light distribution and spatial brightness. The best explanation of this and the apparent disagreements in the literature over the effects of non-uniformity appears to be that spatial brightness is affected by the qualitative appearance of the luminances in the space. Light sources and non-luminous surfaces have different effects. We propose a ‘duel’-process model of spatial brightness in which it is the sum of two opposed processes: the effects of the surfaces, and the effects of the light source(s). Non-uniform patterns of surface reflectance and illumination tend to make a space appear brighter. Non-uniformity as a result of a large difference between luminance of the light source(s) and the surfaces makes a space appear darker. If the light source is hidden from direct view its darkening effect is removed, which can make the space appear significantly brighter. Depending on the relative strength of these two processes, a non-uniform luminance distribution may thus appear either brighter or darker than a more uniform distribution. Additionally, we highlight issues demonstrated in both the failure of models previously proposed by the literature, and our exploration of potential implementations of the ‘duel’-process model. It is very easy to produce a good correlation with a defensible metric that will not generalise to other data sets. A metric having a good correlation in a study provides very little reason to actually believe it. If we wish to develop a model of the effects of light distribution that we can trust, we need to demonstrate its robustness by testing its underlying assumptions and showing them to be well supported. As we show, there is a large variety of these that need to be worked through.

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“…These range from simple linear correlations against convenience metrics (e.g. Hsieh, 2012;Kirsch, 2014;Ko et al, 2013), to more sophisticated attempts to construct new metrics (Kato and Sekiguchi, 2005), to attempts to apply psychophysical brightness models to spatial brightness (e.g. Kobayashi et al, 1998).…”

Section: Models Of the Relationship Between Light Distribution And Spatial Brightnessmentioning

confidence: 99%

“…As this was an uncontrolled field study of a range of office environments though this could be due to many reasons, and does not necessarily imply the model is wrong. Ko et al (2013) did check the max/min ratio, and found it to have a poor correlation with their results, but they were examining the brightness of abstract patterns viewed on screens. The extent to which these challenge the use of the max/min ratio for spatial brightness is minor, but may be worth noting.…”

Section: 6mentioning

confidence: 99%

“…However, we would expect it to fail if it was applied more broadly. Ko et al (2013) carried out several experiments looking at various gradient patterns presented on a computer screen using Magnitude Estimation. Their patterns varied in contrast, overall luminance, and the orientation of the gradient.…”

Section: Hsieh: Min/mean Ratio Of Horizontal Work Plane Illuminancementioning

confidence: 99%

“…The Checkerboard Window condition also fails to show non-uniformity using the standard deviation of the log luminance. This was not something that was considered at the time the experiment was being designed, as the Ko et al (2013) study proposing the metric was not encountered until after Experiment 2 had been completed. Overall, the Light Check condition was the most questionable in terms of whether or not it would show a significant effect, with the work of Kato et al (Kato and Hara, 2011;Kato and Sekiguchi, 2005) suggesting that its effects should be markedly attenuated by the small spread out apertures.…”

Section: Experimental Designmentioning

confidence: 99%

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Measuring the Effects of Light Distribution on Spatial Brightness

Sullivan¹

View full text Add to dashboard Cite

By changing the light distribution it is possible to double the apparent amount of light in a space without any increase in its overall luminance. If one simply assumes that the apparent amount of light in a space — its spatial brightness — is described by its mean luminance (or similar measures) then substantial errors may be made. We carried out two experiments, measuring the brightness of 19 different model spaces. Our results demonstrate that making light distributions more non-uniform can make spaces appear both significantly brighter and significantly darker, depending on how the light distribution is changed. This challenges most existing studies in the field that argue that non-uniformity of the luminance distribution simply makes spaces look darker. Indeed, the observed pattern in brightness between our conditions cannot be consistently explained by a simple measure of the uniformity of the luminance distribution. We thus reject all previously proposed models of light distribution and spatial brightness. The best explanation of this and the apparent disagreements in the literature over the effects of non-uniformity appears to be that spatial brightness is affected by the qualitative appearance of the luminances in the space. Light sources and non-luminous surfaces have different effects. We propose a ‘duel’-process model of spatial brightness in which it is the sum of two opposed processes: the effects of the surfaces, and the effects of the light source(s). Non-uniform patterns of surface reflectance and illumination tend to make a space appear brighter. Non-uniformity as a result of a large difference between luminance of the light source(s) and the surfaces makes a space appear darker. If the light source is hidden from direct view its darkening effect is removed, which can make the space appear significantly brighter. Depending on the relative strength of these two processes, a non-uniform luminance distribution may thus appear either brighter or darker than a more uniform distribution. Additionally, we highlight issues demonstrated in both the failure of models previously proposed by the literature, and our exploration of potential implementations of the ‘duel’-process model. It is very easy to produce a good correlation with a defensible metric that will not generalise to other data sets. A metric having a good correlation in a study provides very little reason to actually believe it. If we wish to develop a model of the effects of light distribution that we can trust, we need to demonstrate its robustness by testing its underlying assumptions and showing them to be well supported. As we show, there is a large variety of these that need to be worked through.

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