light distribution and spatial brightness: relative ...

Sullivan, J.T., Donn, M. LIGHT DISTRIBUTION AND SPATIAL BRIGHTNESS: RELATIVE IMPORTANCE OF …

LIGHT DISTRIBUTION AND SPATIAL BRIGHTNESS: RELATIVE IMPORTANCE OF THE WALLS, CEILING, AND FLOOR 1

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Sullivan, J.T. , Donn, M. Victoria University of Wellington, Wellington, NEW ZEALAND [email protected]

Abstract Review of the literature on the effects of light distribution on spatial brightness raises suggestions that the uniformity of the light distribution, where the light is coming from, and the pattern of light may affect spatial brightness. Uniformity appears to potentially have large effects – however there is some disagreement around their direc tion. Unfortunately, while the commonly suggested hypothesis that light from the walls has a greater influence on spatial brightness than light from other surfaces is plausible, there is a lack of strong evidence to support it in the literature. We carried out a pilot study using brightness matching to examine t he effects of light distribution on spatial brightness, varying the balance of light coming from the walls, floor, and ceiling. Substantial effects of uniformity were found, equivalent to up to a 40% shift in mean luminance. As in the other brightness matching study in the litera ture, we found that less uniform spaces appeared brighter, contradicting the trends found in other studies. Resolving this significant discrepancy is important for further research in this area. Findings on the relative influence of the walls, floor, and ceiling were inconclusive, and sugge st that if effects do exist they are likely to be relatively weak. Keywords: spatial brightness, brightness matching, light distribution, uniformity, literature review

1 Introduction Apparent brightness has long been acknowledged as an important element in the lighting of spaces (Cuttle, 2004; Hopkinson and Collins, 1970; Waldram, 1969) . Unfortunately, the metric most commonly used to measure the light levels in a space – horizontal workplane illuminance – is a very poor predictor of it (Cuttle, 2013; Duff et al., 2015). Because of this, over the years people have suggested alternative metrics as improvements, such as mean luminance, or mean room surface exitance (Cuttle, 2010; Loe et al., 1994). While they can provide significant improvements to prediction, measures such as mean luminance still fail to fully describe the brightness impression of a space (Fotios et al., 2014; Kirsch, 2014). Part of this is due to the effects of colour and spectral power distribution (Fotios et al., 2014). It is also due to the effects of light distribution (Kirsch, 2014) – an area which has seen comparatively little research (Boyce, 2014a). In this paper we discuss the literature on the effects of light distribution on spatial brightness, and present a pilot study using brightness matching to measure the effects of changing uniformity and the relative luminances of the walls, floor, and ceiling .

2 Background What the literature says about the potential effects of light distribution on spatial brightness can be broadly divided into three categories: 

The effects of uniformity/magnitude of variation



The effects of location/where the light is coming from

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The effects of pattern of light

We will discuss each in turn.


2.1 Effects of uniformity The first point that should be taken from the literature is that uniformity does appear to affect perceived spatial brightness (Aya et al., 2015; Hsieh, 2012; Kato and Sekiguchi, 2005; Kirsch, 2014; Kobayashi et al., 1998; Tiller and Veitch, 1995) . The majority of the studies on the subject suggest that more uniform lighting appears brighter than less uniform lighting given the same overall light level (Aya et al., 2015; Hsieh, 2012; Kato and Sekiguchi, 2005; Kirsch, 2014; Kobayashi et al., 1998). However, some studies suggest effects in the opposite direction. Tiller and Veitch (1995) carried out the only psychophysical matching study that we are aware of in the literature on light distribution and spatial brightness. They compared two mocked -up rooms with “uniform” and “non-uniform” distributions of illumination on the walls. They found that the room with “non-uniform” illumination required ~10% less light to appear as bright as the “uniform” room. Similarly, in a study looking at preferred appearance of offices using rendered images, Newsham et al. (2004) found a small correlation in the same direction between brightness and 2 uniformity, here measured as the root mean s quare of the luminances in the scene (r = 0.13). Note however that in Newsham et al. (2005), while they did find the same correlation between brightness and uniformity, they also found that it did not improve predictive power when added to mean luminance compared to just using mean luminance. The next question we might ask here is how substantial the effect of light distribution is. This is somewhat difficult to answer, due to methodological issues in the literature. Many studies use subjective rating scales to assess brightness. As Tiller and Veitch (1995) discussed, such scales do not really support scaling of brightness -luminance relationships, because they really just give ordinal data. Nevertheless, we can get a rough indication. Tiller and Veitch (1995) carried out a brightness matching study. They found that the room with non-uniform wall illumination required 5-10% less light to appear equally as bright as the one with more uniform wall illumination. Unfortunately, the paper does not report any quantifiable information about the degree of uniformity, making it impossible to scale anything. Examining the studies of Aya et al. (2015) (who used magnitude estimation), and Kirsch (2014) (using rating scales), suggests that over the range of light distributions they assessed, the effects could be roughly equivalent to as much as a 30-50% change in luminance. While we should be careful about reading too much into these experiments – since rating scales do not, strictly speaking, support such scaling (Tiller and Veitch, 1995) and magnitude estimation is prone to a number of significant biases (Poulton, 1989) – taken together they do at least seem to indicate that uniformity may have a substantial impact on spatial brightness. Overall, the literature suggests that more uniformly lit spaces should, in general, appear brighter – perhaps substantially so. However, some disagreement about the direction of the effects raises the possibility that the relationship may be more complicated than this.

2.2 Effects of location: relative importance of the walls, floor, and ceiling A number of studies have suggested that where the light is coming from/is placed affects the apparent brightness of a space. Most commonly, the suggestion is that the walls are particularly important (Flynn, 1977; Houser et al., 2002; Iwai et al., 2001; Kato and Sekiguchi, 2005; Kirsch, 2014; Loe et al., 1994; van Ooyen et al., 1987) . There has also been the occasional suggestion that the ceiling may be more important than the floor or workplane (Houser et al., 2002; Kirsch, 2014). Kato and Sekiguchi (2005) examined the perceived brightness of a 2 x 2 x 2m space. Two points are worth making here. Firstly, that in a 2 x 2 x 2m space, an observer’s view would be heavily dominated by the walls. Thus, their findings may simply be interpreted as saying that brightness follows mean luminance of the field of view – which in very small spaces will be dominated by the walls. Secondly, in their second experiment, in the wall lighting condition all the walls were lit. Compare this to the ceiling lighting condition, and we see that since the walls are in total four times the area of the ceiling, the ceiling condition would have to be less uniform to maintain the same average luminance in the room. This difference could also


readily explain the difference in brightness between conditions. Thus, this study cannot be taken as evidence that walls have greater influence upon brightness than other parts of th e room. Houser et al. (2002) examined the perceived appearance of a space lit by varying ratios of indirect and direct lighting. Horizontal workplane illuminance at a point was he ld constant between conditions. They found that the brightness of the walls and ceiling had stronger correlations with overall brightness than did the brightness of the floor and desks. This does not, however, mean that the walls and ceiling are more important. Rather, it is due to the fact that as they were holding horizontal workplane illuminance constant, most of the variation in the light levels in the room was on the walls and ceiling – effectively, they were the main changes in overall room light level. Flynn et al. (1973) compared lighting arrangements that consisted of downlighting focussed onto a central table, and the same downlighting with low level wall lighting added. They also examined a condition with only wall lighting. “Light level” was controlled by keeping the horizontal illuminance on the table constant. From this study, they suggested that wall lighting seemed to produce more positive impressions, including making the room appear brighter. An additional study (Flynn, 1977) examining the addition of wall lighting to a limited area in the front field of view, found similar results. We should be careful about drawing such conclusions. Firstly, horizontal illuminance is a very poor metric for predicting spatial brightness (Duff et al. 2015). Secondly, while the limited information about the luminances in the study make it difficult to be sure, it would appear that the mean surface luminances in the wall lighting conditions may have been higher – which would be a perfectly good reason for it to be brighter. Another old paper that is occasionally referenced in discussions of the value of wall luminance is van Ooyen et al. (1987), who claimed that: “wall luminance contributes most to the way the room is experienced” (p. 155). However, the article is short, with limited detail on their methodology or analysis. For example, they report using 38 semantic scales to assess the lit environments, but do not actually say what the specific questions were. Basic summary statistics are likewise unreported. Furthermore, their reported results are about preferred luminances, rather than brightness, and judging by the reported methodology, the statement about the importance of wall luminance is most likely in comparison to desk luminance. It would hardly be surprising if the luminance of the walls was more important to the overall impressions of a room than the luminances of a few tables with substantially less surface area. And finally, it is not readily apparent that the claim about the im portance of wall luminance is actually supported by the data presented in the article. The article cannot be considered to provide any evidence for the hypothesis that wall luminance has particular importance to the impressions of a space. A recent study examining the effects of lighting the walls is de Vries et al. (2015). Unfortunately, in their study wall luminance co-varied with overall luminance, so the fact that adding wall lighting made the space brighter says nothing about the relative importance of the walls. Kirsch (2014) in his first experiment assessed the perceived brightness of a mock office space, comparing the effect of increasing the wall/ceiling luminances to increasing the surrounding area illuminance. Surrounding area illuminance varied from 100 -300 lux, while wall/ceiling 2 luminance ranged from 11-75 cd/m . As they predicted, changing wall/ceiling luminance had substantially greater impact on overall room brightness than changing surrounding area illuminance. This does not, however, indicate that the walls or ceiling are of special importance to spatial brightness impression. The results are simply due to the fact that changing the level of illumination falling on a dark floor has very little impact on the overall light luminance level in the space. As he demonstrated, mean luminance provides an excellent model of his results. His, and a couple of other studies also suggest that taking the mean luminance from areas focussed around the walls provides slightly better predictions of brightness than the mean luminance of the broader field (the 40° band for Kirsch (2014) and Van Den Wymelenberg (2012), the wall in front of the viewer for Iwai et al. (2001)). As a side note here, it is worth


noting that the paper that originally pushed the 40° band (Loe et al., 1994), was recently reanalysed by Oi and Mansfield (2015), who found that the correlation coefficients varied little from 30°-60° and out to the full 78° image, suggesting that the width of band had limited significance. Thus, that study should not be used as evidence here. In general, it must be said there is a lack of evidence that the walls are of particular importance to the brightness impression of a space. The strongest evidence comes from a few studies finding slightly better correlations when they use luminances calculated from areas that prioritise the walls – which cannot be considered particularly strong evidence. Nevertheless, it should be noted that the hypothesis is still plausible. The role of location in the effects of glare is well known (Boyce, 2014b; Kim and Kim, 2010), and it would not be unreasonable to suggest that brightness could be similarly affected – with the walls tending to appear brighter because they tend to lie in the centre of vision . Indeed, a couple of studies looking at small flashed targets indicated that they appeared brightest when in the fovea (Greenstein and Hood, 1981; Osaka, 1980), though other studies did not find any effects (Pöppel and Harvey Jr, 1973; Zihl et al., 1980) , and a recent review noted that there had been little research on the subject (Strasburger et al., 2011). Only a few studies have suggested that the ceiling may have more importan ce than the floor. Houser et al. (2002) and Kirsch (2014) do not, however, as previously discussed, provide any evidence that could support such claims. Part of the problem here is that the idea that the walls are important actually has different meanings in different studies. The most pure form of the “wall importance” hypothesis – what we are interested in – would be to suggest that, all else being equal, the appearance of the walls (for example, their luminance) has a greater influence on the overall im pression of the lighting in the space than the ceiling or the floor. Collins et al. (1990) speculated at this hypothesis, though they noted that their study did not provide any evidence on the subject. A number of studies, however, when they make statements about the importanc e of walls, are looking at the issue from the perspective of horizontal workplane illuminance, and standard lighting practice. Effectively, what they mean by “the walls are important” is the walls can have significant effects on the overall impression of t he space in ways not reflected by horizontal workplane illuminance. Others effectively just say that when the walls are the dominant part of the field of view, lighting them will be the most effective way to raise the mean light levels people observe. Additionally, confounding the distribution changes with overall light level or other aspects of uniformity is common.

2.3 Effect of pattern A few studies have suggested that the way the light is distributed throughout the space – its pattern – may affect perceived brightness (Kato and Sekiguchi, 2005; Kirsch, 2014; Moore et al., 2003). Moore et al. (2003) in a field study of offices, found a correlation between brightness and the “skew” of the luminance distribution (the ratio between median and mean). Scenes with a positive skew had larger darker areas, and were perceived as dimmer, while the opposite was true for negatively scenes. This suggests that brightness is affected by not only the overall light level, and the overall amount of variance, but also the pattern of the distribution. Kato and Sekiguchi (2005), in their 3rd experiment, examined a room lit through different combinations of the walls (for example, one wall lit, two walls lit etc.). Of particular interest here is the difference they found between the two “two w all” conditions – one lit from two adjacent walls, and the other lit from two opposite walls. Despite their mean luminance being the same, the room lit from two opposite walls appear brighter. This again suggests that the pattern of light is also important to brightness. They demonstrated that the difference could be accounted for using their metric of directional diffusivity to measure uniformity, if you smoothed it by the size of a person’s visual field.

2.4 Summary To sum up, based on the literature we can s ay the following:




How uniform the light distribution in a space is affects its brightness.



The trend appears to generally be one where more uniform spaces appear brighter, however there is some disagreement in the literature, and the relationship may be mo re complicated than this.



The magnitude of the effect may potentially be quite substantial, and may be able to have an impact equivalent to changing the luminance by as much as 50%.



The hypothesis that light in different areas may have different levels of effect – in particular that the walls may have greater influence than other surfaces – is plausible. However, the literature does not provide sufficient evidence to support such claims. Further study specifically targeted at this area is needed.



There is reason to suggest that the pattern of light distribution may affect brightness, however there are only a couple of studies pertaining to this.

An additional point that should be made is that the various studies on the effects of light distribution use a wide range of metrics to quantify elements like uniformity. However, it is not clear what the “correct” metric to use is. This poses challenges to research on the subject, as well as making comparisons between studies difficult. To try to address some of the gaps identified in the literature, a pilot study was carried out focussing on two main points: 1) To get a better understanding of the effect of uniformity on the brightness -luminance relationship. By using a brightness matching methodology following Tiller and Veitch (1995) we get better descriptions of the magnitude of the effects, and can avoid the numerous biases that methods like magnitude estimation are prone to (Poulton, 1989). HDR luminance mapping provides detailed measurements of the light distribution that can then be related to the effects found. 2) To attempt to identify potential differences in the effects of the walls, floor, and ceiling, the light distribution was varied by adjusting the rel ative luminances of the different surfaces.

3 Methodology 3.1 Experimental design The method used was a brightness matching task, wherein participants were instructed to adjust the light level in a test space until it appeared to them to be as bright as a refere nce space. Eight different test conditions were used, each with different light distributions. Testing was spread out over three sessions on different days, each roughly 50 minutes long, in order to minimise participant fatigue. Test condition order was varied randomly between each session. In each session, participants viewed each condition three times, for a total of nine matches for each condition. At the start of each session, participants were given several practice matches, while they spent 10 minutes adapting. Participants were given written instructions based off those described in Tiller and Veitch (1995) and Fotios and Cheal (2011) to read at the start of the test. Brightness was defined as the amount of light in the space. Participants adjusted their seat height until their eyes were levelled half way up the height of the space. They were instructed to adopt a viewing position such that their chin was just over the edge of the spaces’ opening, in order to make sure that it filled their field of view. They then looked back and forth between the spaces to compare them. In order to address the biases discussed in Fotios et al. (2008), a null condition was used. Test condition results are discussed relative to this, rather than the reference condition. The reference condition was held constant in order to avoid the response contraction bias (Fotios and Cheal, 2007). Thus, each test condition is “set” by participants to the sam e brightness. The starting setting was varied in order to prevent participants from learning how far to turn the dial, and turning it the same distance each time. The dial itself was a gear, and thus lacked obvious positional cues. The starting levels were based off the amount of light measured being released into the space, and were set at 15, 30, 60, 200, and 260 lux – settings designed to be spread around the light level of the reference condition.


3.2 Experimental set-up Participants viewed side-by-side model spaces as shown in the figure below. The spaces were designed to keep the source of the light out of view, so participants only saw light reflected off the internal surfaces of the test space. Light to each space was provided by two compact fluorescent lamps, diffused through a polycarbonate sheet and white diffusing box before being released into the space. Light level was controlled using a shutter system to change the amount of light being released into the diffusing box. This was done to avoid the colour shift that can occur when lights are dimmed electronically (Fotios and Cheal, 2011). Participants controlled the shutters using a dial connected by a ge ar and chain-drive system.

Figure 1 – Plan and side view diagrams illustrating the experimental set -up The light levels selected by participants were monitored and recorded using an illuminance meter placed inside the diffusing box to measure the light entering the space. Exposure bracketed photographs of each space were taken using a Nikon D200 DSLR camera with a full-frame fisheye lens to capture the luminances of the field of view. The multip le exposures were then combined into HDR images using the WebHDR interface for hdrgen (JALOXA, 2014), and analysed using hdrscope (Kumaragurubaran, 2012). The HDR images were calibrated against measurements from a Minolta LS-110 luminance meter. The resulting luminance statistics were then scaled using the illuminances recorded for participants’ match settings in order to calculate their match luminances. The eight different test conditions are summarised below. Light distributions were varied by changing the surface reflectivities. The reference space was set to a mean luminance of 0.82 2 cd/m . Table 1 - Details of test conditions

Null condition wall/ceil/floor refl: 27%/52%/52% w:c:f lum ratio: 1:0.9:0.9 Max/Min: 65 Max/Mean: 7.4 Min/Mean: 0.11 Mean/Median: 1.5

White wall wall/ceil/floor refl: 78%/3%/3% w:c:f lum ratio: 1:0.1:0.1 Max/Min: 434 Max/Mean: 10 Min/Mean: 0.02 Mean/Median: 2.5

Black wall wall/ceil/floor refl: 3%/78%/78% w:c:f lum ratio: 1:10.3:9.8 Max/Min: 396 Max/Mean: 5.9 Min/Mean: 0.01 Mean/Median: 1.6

Black floor wall/ceil/floor refl: 40%/78%/3% w:c:f lum ratio: 1:0.8:0.2 Max/Min: 310 Max/Mean: 7.8 Min/Mean: 0.03 Mean/Median: 2.3


Black ceiling wall/ceil/floor refl: 40%/3%/78% w:c:f lum ratio: 1:0.2:0.8 Max/Min: 306 Max/Mean: 8.4 Min/Mean: 0.03 Mean/Median: 2.3

Dark grey floor wall/ceil/floor refl: 30%/78%/15% w:c:f lum ratio: 1:1:0.3 Max/Min: 154 Max/Mean: 7.3 Min/Mean: 0.05 Mean/Median: 2.1

Dark grey ceiling wall/ceil/floor refl: 30%/15%/78% w:c:f lum ratio: 1:0.3:1 Max/Min: 140 Max/Mean: 7.3 Min/Mean: 0.05 Mean/Median: 1.9

Checkerboard Check refl: 3%/78% w:c:f lum ratio: 1:0.6:0.6 Max/Min: 417 Max/Mean: 10.5 Min/Mean: 0.03 Mean/Median: 1.8

3.3 Participants Four people, all post-graduate students at the university, participated in the experiments. Two were male, two were female. All between 20-30 years of age. Two wore visual correction.

4 Results The most important finding in the results is that the less uniform spaces require less light to match the brightness of the reference space. The effect is most clearly shown by the white wall and checkerboard conditions – the least uniform. On all subjects, they consistently require substantially less light than the null condition to match its brightness – on average 3640% less. This is substantial, though in line with estimates in the literature. However, it is also in the opposite direction to what is generally predicted by other studies, and follows Tiller and Veitch (1995) in finding that non-uniformity increases brightness. This large discrepancy is a problem that needs to be solved if progress is to be made on understanding the effects of light distribution on spatial brightness. One possibility is that it is due to methodology – the studies that show this trend are brightness matching studies, unlike most of the literature. Further study is needed.

Figure 2 – Brightness match results for the 4 participants. Error bars show 95% confidence intervals.


Figure 3 – Results for all 4 participants normalised against their null condition setting

Figure 4 – Correlation between uniformity ratio (max/mean) and average match setting

Regarding possible effects of the walls, floor, and ceiling the results are inconclusive. Most of the subjects show large differences between the white wa ll and black wall conditions – however, this could the black wall condition being much closer to the null condition on some uniformity metrics (e.g. max/mean, mean/median). Thus, while brightness impression being dominated by the walls could be a possible explanation for the difference, we cannot rule out a simple uniformity-based explanation. The various ceiling/floor conditions have generally similar results to each other, and so if there is a difference in the influence of the ceiling and floor, it is probably quite small, and may need more precise readings or more extreme stimuli to detect. We need to be able to account for the effects of uniformity to investigate this, however this is difficult when it is not clear what the “correct” way to measure and define uniformity is.

5 Conclusions Uniformity of light distribution appears to have significant impact on spatial brightness. However, this are significant discrepancies in the literature, with some studies, such as this one, indicating that non-uniform spaces should appear brighter, and other studies finding the opposite. The discrepancies may be related to the brightness matching methodology. Resolving this issue is vital to further investigations of the subject. Whether or not the walls, floor, and ceilin g have different levels of influence ton spatial brightness is unknown, with no strong evidence provided by the literature. This study suggests that if there is such an effect, it is likely small, and may be difficult to identify.

Acknowledgements This research was supported by a Victoria University Doctoral Scholarship.

References AYA, K., YOSHIZAWA, N., YAMAGUCHI, H., HARA, N., KATO, M., MIKI, Y., 2015. The relationship between the brightness of overall space and the brightness in the specific visual field in the non-uniform illuminated space, in: Proceedings of 28th CIE Session 2015. CIE, Manchester, UK, pp. 1466–1470. BAUER, B., 2009. Does Stevens’s Power Law for Brightness Extend to Perceptual Brightness Averaging? Psychol. Rec. 59, 171–185. BOYCE, P., 2014a. Editorial: Light distribution – a missing variable. Light. Res. Technol. 46, 617–617. doi:10.1177/1477153514556940 BOYCE, P., 2014b. Human Factors in Lighting, Third Edition. CRC Press.


COLLINS, B.L., FISHER, W., GILLETTE, G., MARANS, R.W., 1990. Second-Level Post-Occupancy Evaluation Analysis. J. Illum. Eng. Soc. 19, 21–44. doi:10.1080/00994480.1990.10747961 CUTTLE, C., 2013. A new direction for general lighting practice. Light. Res. Technol. 45, 22–39. doi:10.1177/1477153512469201 CUTTLE, C., 2010. Towards the third stage of the lighting profession. Light. Res. Technol. 42, 73–93. doi:10.1177/1477153509104013 CUTTLE, C., 2004. Brightness, lightness, and providing “a preconceived appearance to the interior.” Light. Res. Technol. 36, 201–214. doi:10.1191/1365782804li115oa DE VRIES, H.J.A., HEYNDERICKX, I.E.J., DE KORT, Y.A.W., DE RUYTER, B., 2015. Wall illumination - beyond room appraisal, in: Proceedings of 28th CIE Session 2015. CIE, Manchester, UK, pp. 284–290. DUFF, J., KELLY, K., CUTTLE, C., 2015. Perceived adequacy of illumination, spatial brightness, horizontal illuminance and mean room surface exitance in a small office. Light. Res. Technol. 1477153515599189. doi:10.1177/1477153515599189 FLYNN, J.E., 1977. A study of subjective responses to low energy and nonuniform lighting systems. Light. Des. Appl. 7, 6–15. FLYNN, J.E., SPENCER, T.J., MARTYNIUK, O., HENDRICK, C., 1973. Interim Study of Procedures for Investigating the Effect of Light on Impression and Behavior. J. Illum. Eng. Soc. 3, 87–94. doi:10.1080/00994480.1973.10732231 FOTIOS, S.A., CHEAL, C., 2011. Brightness matching with visual fields of different types. Light. Res. Technol. 43, 73–85. doi:10.1177/1477153510369478 FOTIOS, S.A., CHEAL, C., 2007. Evidence for response contraction bias in side-byside matching tasks. Light. Res. Technol. 39, 159–169. doi:10.1177/1365782807076490 FOTIOS, S.A., HOUSER, K.W., CHEAL, C., 2008. Counterbalancing Needed to Avoid Bias in Side-By-Side Brightness Matching Tasks. LEUKOS 4, 207–223. doi:10.1582/LEUKOS.2008.04.04.001 FOTIOS, S., ATLI, D., CHEAL, C., HARA, N., 2014. Lamp spectrum and spatial brightness at photopic levels: Investigating prediction using S/P ratio and gamut area. Light. Res. Technol. 1477153514542295. doi:10.1177/1477153514542295 GREENSTEIN, V.C., HOOD, D.C., 1981. Variations in brightness at two retinal locations. Vision Res. 21, 885–891. doi:10.1016/0042-6989(81)90189-9 HOPKINSON, R.G., COLLINS, J.B., 1970. The Ergonomics of Lighting. Macdonald & Co. (Publishers) Ltd, London. HOUSER, K.W., TILLER, D.K., BERNECKER, C.A., MISTRICK, R.G., 2002. The subjective response to linear fluorescent direct/indirect lighting systems. Light. Res. Technol. 34, 243–260. doi:10.1191/1365782802li039oa HSIEH, M., 2012. The energy-saving effect and prediction method under various illuminance distribution types. Build. Environ. 58, 145–151. doi:10.1016/j.buildenv.2012.07.001 IWAI, W., SAITO, Y., SUMI, S., SAKAGUTI, T., 2001. Sensation of Brightness for a Living Room with Downlights. J. Light Vis. Environ. 25, 1_66 –1_72. doi:10.2150/jlve.25.1_66 JALOXA, 2014. WebHDR [WWW Document]. URL http://www.jaloxa.eu/webhdr/index.shtml (accessed 1.15.16). KATO, M., SEKIGUCHI, K., 2005. “Impression of Brightness of a Space” Judged by Information from the Entire Space. J. Light Vis. Environ. 29, 123–134. doi:10.2150/jlve.29.123 KIM, W., KIM, J.T., 2010. A distribution chart of glare sensation over the whole visual field. Build. Environ. 45, 922–928. doi:10.1016/j.buildenv.2009.09.013


KIRSCH, R., 2014. Lighting Quality and Energy Efficiency in Office Spaces (Ph.D. Thesis). Technical University Berlin, Berlin. KOBAYASHI, S., NAKAMURA, Y., INUI, M., 1998. Impressions of Overall Brightness in a Non-Uniformly Illuminated Space. J. Light Vis. Environ. 22, 1_34–1_41. doi:10.2150/jlve.22.1_34 KUMARAGURUBARAN, V., 2012. hdrscope. University of Washington, Seattle. LOE, D.L., MANSFIELD, K.P., ROWLANDS, E., 1994. Appearance of lit environment and its relevance in lighting design: Experimental study. Light. Res. Technol. 26, 119–133. doi:10.1177/096032719402600301 MOORE, T., CARTER, D.J., SLATER, A.I., 2003. A qualitative study of occupant controlled office lighting. Light. Res. Technol. 35, 297–314. doi:10.1191/1365782803li090oa NEWSHAM, G.R., MARCHAND, R.G., VEITCH, J.A., 2004. Preferred surface luminances in offices, by evolution (No. NRCC-46976). NRC Institute for Research in Construction, Ottawa, ON. NEWSHAM, G.R., RICHARDSON, C., BLANCHET, C., VEITCH, J.A., 2005. Lighting quality research using rendered images of offices. Light. Res. Technol. 37, 93 – 112. doi:10.1191/1365782805li132oa OI, N., MANSFIELD, K.P., 2015. Lighting quality: possibility of luminance distribution as its determinant, in: Proceedings of 28th CIE Session 2015. CIE, Manchester, UK, pp. 1111–1120. OSAKA, N., 1980. Brightness exponent as a function of retinal eccentricity in the peripheral visual field: Effects of dark and light adaptation. Percept. Psychophys. 27, 519–523. doi:10.3758/BF03198679 PÖPPEL, E., HARVEY JR, L.O., 1973. Light-difference threshold and subjective brightness in the periphery of the visual field. Psychol. Forsch. 36, 145 –161. doi:10.1007/BF00424967 POULTON, E.C., 1989. Bias in Quantifying Judgements. Taylor & Francis. RUDD, M.E., 2013. Edge integration in achromatic color perception and the lightness– darkness asymmetry. J. Vis. 13, 18. doi:10.1167/13.14.18 STEVENS, S.S., 1961. To Honor Fechner and Repeal His Law. Science, New Series 133, 80–86. STEVENS, S.S., 1960. The psychophysics of sensory function. Am. Sci. 48, 226–253. STRASBURGER, H., RENTSCHLER, I., JÜTTNER, M., 2011. Peripheral vision and pattern recognition: A review. J. Vis. 11, 13–13. doi:10.1167/11.5.13 TILLER, D.K., VEITCH, J.A., 1995. Perceived room brightness: Pilot study on t he effect of luminance distribution. Light. Res. Technol. 27, 93–101. doi:10.1177/14771535950270020401 VAN DEN WYMELENBERG, K.G., 2012. Evaluating Human Visual Preference and Performance in an Office Environment Using Luminance-based Metrics (Ph.D.). University of Washington, United States -- Washington. VAN OOYEN, M. h. f., VAN DE WEIJGERT, J. a. c., BEGEMANN, S. h. a., 1987. Preferred Luminances in Offices. J. Illum. Eng. Soc. 16, 152–156. doi:10.1080/00994480.1987.10748695 VLADUSICH, T., 2013. Gamut relativity: A new computational approach to brightness and lightness perception. J. Vis. 13, 14. doi:10.1167/13.1.14 WALDRAM, J.M., 1969. IES Diamond Jubilee Lecture: Light and the community Part 2 The growth of lighting. Light. Res. Technol. 1, 78–85. doi:10.1177/14771535690010020601 WRIGHT, M.S., HILL, S.L., COOK, G.K., BRIGHT, K.T., 1999. The perception of lighting quality in a non-uniformly lit office environment. Facilities 17, 476–484. doi:10.1108/02632779910293479 ZIHL, J., LISSY, P., PÖPPEL, E., 1980. Brightness perception in the visual field. Psychol. Res. 41, 297–304. doi:10.1007/BF00308875