Measuring the Effects of Light Distribution on Spatial Brightness

Abstract

<p><b>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.</b></p> <p>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.</p> <p>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.</p> <p>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.</p>