Preferred Surface Luminances in Offices, by Evolution

Abstract

/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. Society, 33, 1, pp. 14-29, 2004-02-01 Preferred surface luminances in offices, by evolution Newsham, G. R.; Marchand, R. G.; Veitch, J. A. office, and rated them for attractiveness. The image was projected onto a screen at realistic luminances and 54% of full size. The images in the series were geometrically identical, but the luminances of important surfaces were independently manipulated. Initially, the combinations of luminances were random, but as the session continued a genetic algorithm was used to generate new images that generally retained features of prior images that were rated most highly. As a result, the images presented converged on an individual's preferred combination of luminances. The results demonstrated that this technique was effective in reaching a participant's preferred combination of luminances. There were significant differences in room appearance ratings of the most attractive image compared to other images, and the differences were in the expected direction. Factor analysis of ratings of the most attractive images revealed a factor structure with some similarity to that obtained when people rated real office spaces. Furthermore, preferred luminances were similar to those chosen by people in real settings, as was the variation in preferences between individuals. Journal of the Illuminating EngineeringFinally, subjective ratings of brightness, uniformity and attractiveness were significantly related to luminances in the image.

Validation of an online protocol for assessing the luminous environment

“…He highlighted that the same findings were found in both experiments. Similarly, Newsham et al 17,19 highlighted the consistency found between the optimal luminous environments obtained for each observer of a panel of 40 observers, who judged the CGI of an office room on a computer, and data collected from a real-world test in which observers adjust lighting according to their preferences. However, virtual environment tests have limits, 18 especially when used for experiments about glare or colour rendering.…”

Contrast measures for predicting perceptual effects of daylight in architectural renderings

“…Flynn introduced the use of semantic differential scales to gather subjective assessments of daylight quality in terms of visual clarity, spaciousness, evaluation, relaxation, social prominence, complexity, modifying influence, and spatial modifiers 8 . Numerous studies thereafter have employed the use of these scales to conduct daylight quality research in real spaces and simulated or photographed views 10,30,40,41 . For the proposed study, the authors have focused on bi-polar semantic differential scales associated with complexity and spatial modifiers as well as visual interest.…”

“…To conduct qualitative lighting research using digital images, existing studies have applied subjective rating methods to measure impressions of lighting composition in HDR photographs 11,[30][31][32] as well as rendered images of a simulated office environment 10 . These experiments have asked participants to view a series of images and then respond to semantic differential ratings 8 for pleasantness, contrast, brightness, spaciousness, and/or distribution, which were then compared to photometric measurements taken from the digital images.…”

Measuring the dynamics of contrast & daylight variability in architecture: A proof-of-concept methodology

“…To evaluate the visual impacts of luminosity within interior architecture, existing research has relied on average luminance or "brightness," threshold luminance, and luminance variation (or standard deviation) in line with occupant surveys to establish trends in preference. Survey-based studies most commonly rely on high-dynamic-range HDR images, digital photographs or renderings produced through Radiance, which provide an expanded range of photometric information, allowing us to evaluate characteristics such as brightness and contrast [19,20]. Some studies have found that both mean luminance and luminance variation within an office environment contribute to occupant preference [21], whereas others have discovered that luminance distribution across an occupant's field-of-view [22] as well as the strength of variation are factors of preference [23].…”