A comparison of perceived lighting characteristics in simulations versus real-life setup

A comparison of perceived lighting characteristics in simulations versus real-life setup B. Salters*, P. Seuntiens Philips Research Eindhoven, High Tech Campus 34, Eindhoven, The Netherlands ABSTRACT Keywords: Simulation, perception, atmosphere, lighting, comparison, ANOVA. In the design of professional luminaires, improving visibility has always been a core target. Recently, it has become clearer that especially for consumer lighting, generating an appropriate atmosphere and pleasant feeling is of almost equal importance. In recent studies it has been shown that the perception of an atmosphere can be described by four variables: cosiness, liveliness, tenseness, and detachment. In this paper we compare the perception of these lighting characteristics when viewed in reality with the perception when viewing a simulated picture. Replacing reality by a picture on a computer screen such as an LCD monitor, or a piece of paper, introduces several differences. These include a reduced dynamic range, reduced maximum brightness and quantization noise in the brightness levels, but also in a different viewing angle, and a different adaption of the human visual system. Research has been done before to compare simulations with photographs, and simulations with reality. These studies have focused on physical variables, such as brightness and sharpness, but also on naturalness and realism. We focus on the accuracy of a simulation for the prediction of the actual goal of a lot of luminaires: atmosphere creation. We investigate the correlation between perceptual characteristics of the atmosphere of a real-world scene and a simulated image of it. The results show that for all 4 tested atmosphere words similar main effects and similar trends (over color temperature, fixtures, intensities) can be found in both the real life experiments and the simulation experiments. This implies that it is possible to use simulations on a screen or printout for the evaluation of atmosphere characteristics. 1. INTRODUCTION The optical performance of a luminaire design in terms of luminance distribution can be simulated quite well by means of raytracing software. Results are then available in a mathematical way, such as graphs or intensity plots. These are not very suitable to rate a certain design on perceptual aspects such as cosiness or diffuseness. Results can also be shown as a photo-realistic rendering. A lot of research has been done already investigating the accuracy of such renderings. Most of this work has focused on mathematical accuracies, or similarities with the real-world situation. However, the true goal of a luminaire designer quite often is to create a certain atmosphere; it hence would be useful to investigate to what degree simulations have a predictive value for these parameters. 1.1 Atmosphere parameters The relationship between artificial light and the performance of the human visual system has been investigated extensively by many researchers (e.g. Boyce 1, Baron et al. 2, and Veitch 3 ) These studies were mainly focused on investigating the way people perceive different light characteristics and the way it affects human cognition and task performance. Recently, designers and researchers realized that light should not only facilitate visibility, but can also create an appropriate atmosphere. With the introduction of LED lighting, designers and architects have more flexibility to evoke emotions and have the ability to create certain atmospheres in a room using artificial light. Nevertheless, despite the increased interest in atmosphere creation, to date not much is known about the effect of lighting on the perceived atmosphere. *bart.salters@philips.com; phone +31 40 2747846

In a previous study on atmosphere perception (Vogels, 2008) 4, a questionnaire to assess and quantify the perceived atmosphere of an environment was developed and verified. Perceived atmosphere is an affective evaluation of the environment and is not an affective state, such as mood and emotion. However, atmosphere has the potential to influence people s mood and emotions. In a number of follow-up studies, the relationship between white light and perceived atmosphere has been confirmed using the atmosphere questionnaire (Vogels et al., 2008) 5. These studies found interesting relationships between light characteristics of white light and the perceived atmosphere in an empty room. Furthermore, in a recent study it was shown that the influence of light characteristics (for white light) on the perceived atmosphere is independent of the contextual situation (Vogels and Bronckers, 2009) 6. In contrast to the controlled studies of Vogels et al. and Vogels and Bronckers, a more practical approach was taken in Seuntiens and Vogels (2008) 7. In that study, the relationship between perceived atmosphere and light characteristics of white and colored light for a given contextual setting was investigated by asking fifteen professional lighting designers to design four atmospheres (cosy, relaxing, activating and exciting). The results of that study showed that all lighting designers were able to discriminate between atmospheres in terms of light characteristics for a given standard living room. In addition, similarities were found between the lighting designers with regard to the chosen light characteristics for the four atmospheres. Finally, a follow-up study with end-users showed that people were able to clearly recognize the cosy, relaxing, activating and exciting atmospheres (Seuntiens, 2008) 8. Based on our previous research, it is clear that a good lighting design consists of both diffuse lighting and accent lighting. People intuitively understand what is meant by a diffusely reflective surface. On the other hand, the definition of a diffuse light source is less clear, and the perception of diffuse light (from a user perspective) has been hardly investigated at all. A diffuse light source does not necessarily create a room that is perceived as diffuse, and vice versa. 1.2 Previous work on accuracy of simulations Several studies have been done in the past comparing real-world scenes with images of the real world. The common route to generate an image has been to use a camera to record an image of the real world scene, and to show it subsequently on a screen. As most real-world scenes have a dynamic range that exceeds the capability of the camera, multi-exposure capturing has been used to record the high dynamic range (HDR) information. This HDR image in turn has to be reduced in dynamic range in order to show it on a normal screen, by means of a tone mapping operator (TMO). The choice of TMO has a large influence on the perceived image 9. The specifications of the monitor also influence the results. Finally, the viewing angle of a screen can hardly be compared to that of a real room. Similar arguments hold for a print on paper. An alternative to taking pictures of an existing scene is photo-realistic rendering of a luminaire design. This approach consists of building a model of a certain situation (e.g. a room with certain luminaires), and simulating the resulting light distribution. The simulation result again consists of a picture, which in turn has to be shown on a display. Similar to the scenario with real pictures, the simulation typically has a higher dynamic range than the display to be used. Again, tone mapping operators are required to reduce the dynamic range. Regardless of the means of generating the final images, the ultimate goal of many computer images is to show them to human observers. Exactly what users will do with these images largely determines which aspects of the final computer image are important. In cases such as photography, a realistic depiction of the original scene is often the goal. This calls for settings, renderings and TMO that optimize this aspect 10. Other research, such as by Yoshida 11 has focused on aspects including naturalness, uniformity and detail of the depiction, or verifying the results 12. These variables are usually of less importance to lighting designers, who are typically interested in creating an atmosphere. Although the most detailed or most accurate rendering might do very well, it might also be too expensive, or too time consuming to generate. Given the importance of atmosphere creation to lighting design, we investigated the suitability of photo-realistic rendering for investigating the atmosphere perception of a room. 1.3 Simulating atmosphere lighting designs An advantage of using photo-realistic rendering is that human perception studies can be conducted for nonexistent situations. Depending on the speed of the photo-realistic rendering, users in a perception test can even be asked to adjust certain parameters of the design to their liking. Assuming a near-real time rendering, optimization of a design can be done in a strictly virtual approach. This would vastly increase the design speed of a luminaire that meets certain perceptual characteristics, assuming sufficient correlation between the perception of reality and the perception of an image on a computer screen, which in turn is generated by means of ray tracing of a model. Exactly this correlation is

what we investigate in this paper, with a specific focus on the atmosphere characteristics cosiness, relaxing, activating and exciting. For this kind of perceptual research, there are two options. The first is to have an actual prototype of a certain design, mount it in a standardized room, and collect statistical data from a set of test observers. This procedure incurs substantial costs, both for the production of working prototypes, as well as the cost for the perception research. It takes a substantial amount of time, from optical design to obtaining the final statistical results. The second option is to use a simulation result for perception research, but this is only possible if simulations have enough of a predictive value. For this investigation we have built a room where several luminaires can be tested and perception studies can be done. The same room has been simulated in detail in the raytracing program LightTools, and photorealistic renderings have been generated with this same program. Although LightTools is not primarily used to generate photo-realistic renderings, it does provide the benefit of having exact numerical values for lighting characteristics such as the intensity distribution. Finally, several different perceptual questions have been investigated statistically in three scenarios: in the real-world experimental room, using a picture shown on a normal LCD monitor, and printed on paper. We will discuss similarities and differences between the results of all three tests. In this way, relationships can be established between all aspects in designing luminaires: design, simulation, prototyping, and perception studies. The main goal of our research was: Explore the correlation between the perception of different methods of visualizing light effects (real world, photo, LCD), by studying people s perception of diffuseness and atmosphere, for different lighting configurations in a typical office room. 2.1 Experimental setup of the room. 2. TESTING ENVIRONMENT Our room was a typical office layout. The room was equipped with 6 Philips Savio down lights which were evenly distributed in the ceiling. Each Savio consisted of 4 CCFL lamps with a color temperature of 2700K and 4 CCFL lamps with a color temperature of 6500K; each set of 4 could be addressed independently. The room contained 6 downlight spots with an opening angle of 18 degrees, evenly distributed in the ceiling. Additionally, 6 spots with an opening angle of 11 degrees were placed in line on one side of the room illuminating the vertical wall. All the spots had a color temperature of 3000K. The walls were painted with color RAL 9001, which is white. The layout and room dimensions are shown below in Figure 1. 18 degree angle downlight spots 11 degree angle downlight spots Figure 1. Model in LightTools of the layout of the office room, with all luminaires shown.

Figure 2. Ceiling plan of the room showing positions of the various light sources. In total 15 different light settings were created. The real-world experiments were done in the experimental room and corresponding LabView software; renderings for the same settings were made in LightTools. The settings were created using both the spotlight and Savio luminaires, providing general lighting and accent lighting. The 15 light settings differed in intensity, color temperature, and spatial distribution. The settings for the diffuse lightsources (Savios) are shown in the next figure. Figure 3. Different spatial distributions of the Savio down lights. Four different arrangements are shown (6, 4, 3, and 2 Savios); both a CCT of 6500K, and a CCT of 2700K were used, resulting in 8 different arrangements. All settings had a illuminance level of 500 lux measured under a Savio at 1 meter above the floor. The spatial distribution of all six Savios on was also presented at 250 lux and 20 lux, for both CCT levels. The layout of the spots with a large beam and small beam is shown in Figure 2. The spots with an 18 degree beam angle were shown at high intensity and low intensity. The spots with the small beam angle only at high intensity. The color temperature was 3000K for both beam angles. This resulted in the following 15 settings. Table 1. Overview of luminaire settings Description Intensity CCT Description Intensity CCT 1 2x Savio Full (K) 2700 9 6x Savio Half (K) 2700 2 3x Savio Full 2700 10 6x Savio Min. 2700 3 4x Savio Full 2700 11 6x Savio Half 6500 4 6x Savio Full 2700 12 6x Savio Min. 6500 5 2x Savio Full 6500 13 Spot 11 o Full 3000 6 3x Savio Full 6500 14 Spot 18 o Full 3000 7 4x Savio Full 6500 15 Spot 18 o Min. 3000 8 6x Savio Full 6500 2.2 LightTools model of the room Various software tools are available for creating a photo-realistic rendering; well-known packages include Maya, 3D studio max, Indigo and others. All of them can produce very convincing, realistic looking pictures. We have opted for a different program: LightTools, by Optical Research Associates (ORA). LightTools is mainly used as a raytracing program to quantitatively simulate optical systems. The output is physically correct, and moreover, can be numerically

verified. This ensures that the model which is being used describes the real setup. It also ensures that approximations, which are inevitably necessary, do not substantially influence the light distributions in our room. The geometric dimensions of walls, tables, ceilings and floors have been copied with an accuracy of 1 cm. The position of all luminaires is accurate to the same scale. Standard reflection coefficients have been used for the plastered walls, ceiling tiles and carpet. As a result, the light distribution closely matches reality. We have numerically verified this with some samples; e.g. the illuminance directly underneath a Savio luminaire was 500 lux in reality and the simulation confirmed this result. 2.3 Simulations and output format The simulation accuracy of any model goes up with the total number of rays traced. For our simulation we used 25 million rays, for each scenario. This means that in scenarios with more luminaires on, less rays per luminaire were traced. The camera viewpoint was (x,y,z) = (6300, 1860,1100), and is indicated in Figure 2. The output was rendered to file in the EXR high dynamic range format, at a resolution of 1920x1080 pixels, with 32-bits per channel (96 bits total). The resulting output consisted of 15 pictures; each picture representing one scenario. The total dynamic range covered varies per scenario, but usually is in the order of 3 orders of magnitude. This excludes direct lines of view from the camera into the luminaire, where a much higher brightness was recorded. The numerical results from raytracing enable us to get an absolute value for the brightness; maximum values in the order of ~1000 cd/m 2 were measured on the wall directly in front of a spotlight; minimum values around 1 cd/m 2 were measured under the table, near the wall. Using a Konica Minolta CL-200 illuminance meter, sample measurements were taken, to confirm that the illuminance indeed corresponds to the simulated results. 2.4 Tone mapping and final result One method of converting an HDR image for displaying on a screen or print on paper is tone mapping. The total dynamic range is compressed in such a way that the full dynamic range of the output medium can be used for displaying the image. Several different tone mapping algorithms are known 11, all of which are suitable in various situations. We have chosen to use the adaptive logarithmic tone mapping algorithm proposed by Drago 13, which is known for its good results and reasonably fast processing. An example of a resulting picture is shown in the next figure (left), with a photograph from the same room next to it (right), for comparison. Figure 4. Comparison of simulation (left) and photograph (right). Several differences can be observed. From a geometric point of view, an important difference is that the picture was not taken at exactly the same height as the simulated picture; the change in perspective is mostly visible on the table. Another geometric difference can be found in the slight distortion that is present in the photograph, though not in the simulation. The ceiling grid is more visible in the simulation, because of limitations in the rendering. Also there are several small objects that have not been included in the simulation, such as a few non-functioning luminaires and a small sprinkler on the ceiling. Due to their small size, the influence on the distribution of light in the room is expected to be negligible. We have verified this visually.

3.1 Participants 3. PERCEPTION STUDIES In this study, 9 males and 3 females participated in the real life experiment in our experimental room and 10 males and 2 females participated in the simulation experiment judging the renderings on a Philips Brilliance 240B LCD monitor and a photographic print. All participants were employees of Philips Research, and they had little or no knowledge about the experimental set-up (variables in the experiment). 3.2 Procedure The 15 light settings were presented to the participants in random order to account for possible order effects. The room illumination (5000K) for the experiment was 30 lux measured vertically at the display and 500 lux measured horizontally on the photographic prints. The aspect ratio of the pictures on the LCD and the photographic prints was 16:9. The height of the display was 32.5 cm resulting in a viewing angle of 18.5 degrees at 1 meter distance. The size of the photographic print was 29.5 x 16.5cm, and the picture was placed in front of the observers at a distance of 50 cm resulting in a viewing angle of 18.5 degrees. A high dynamic range image with a resolution of 1920x1080 was used as input, and default settings in Photoshop were used to prepare the image for printing. The participants had to fill out a 7-point questionnaire for each light setting. The questionnaire is shown below. Please fill out the questions below. Base your judgments on the perception of the ENTIRE room. The first questions are related to diffuseness perception. The last four questions are related to atmosphere perception. Diffuseness perception: Low High Overall diffuseness O O O O O O O Contrast O O O O O O O Uniformity O O O O O O O Shadow visibility O O O O O O O Atmosphere perception: Cosiness O O O O O O O Liveliness O O O O O O O Tenseness O O O O O O O Detachment (business-like) O O O O O O O 3.3 Experimental results In this section, the results for diffuseness and atmosphere perception of the real life experiment (room) and the simulation experiments (display and photo) are discussed. We refer to these three different experimental conditions as presentation. The focus of the analyses for both diffuseness perception and atmosphere perception will be the direct comparison between the results obtained in the real room and in the simulation experiments. The statistical tool for the analyses of variance (ANOVA) of the data was SPSS. 3.4 Diffuseness perception In general, the ANOVA results for diffuseness perception show a significant main effect of presentation (p<0.001) and a main effect of fixtures (p<0.001). The ANOVA analyses on the diffuseness scores showed no significant main effect of intensity (p=0.11) and color temperature (p=0.15). The results of the overall diffuseness scores (averaged over intensity and color temperature) including the error bars for the different presentations and fixtures are depicted in Figure 5.

Figure 5. Overall diffuseness scores. The x-axis represents the different fixture layouts and the bars represent the presentation. On the y-axis the average diffuseness scores including error bars (2SE) are given. The main effect of presentation is investigated in more detail using a Tukey post-hoc test which shows that the scores of the simulation (display and photograph) are significantly different from the real life experiment. No significant difference was found between display and photograph. For all presentations (room, display, and photo), the overall diffuseness of the lighting decreases with the number of fixtures where six savios are perceived as most diffuse and six spots are perceived as least diffuse. The diffuseness scores of the real life experiment (room) saturate for the spotlighting around a score of 3, while the scores for the simulation experiment drop further towards 1. Although the diffuseness results for the different presentations are quite similar (except for the spotlighting) a small but constant offset is observed in the absolute values. This is probably due to the rendering method. Analyses of the remaining diffuseness questions shows that the overall diffuseness has the same trend as the perceived uniformity of the room. The contrast has also the same trend but is negatively correlated. High diffuseness and high uniformity is perceived as low contrast. There was not a clear trend of shadow visibility. These results were found for all presentations (room, display, photo). Table 2 below depicts the pearson correlations of the 4 questions over all presentations. A pearson correlation is defined as the strength and direction of a relationship between two random variables. The closer the coefficient is to either -1 or 1, the stronger the correlation between the variables. If the variables are independent then the correlation is 0.

Table 2. Pearson correlations of the 4 questions (overall diffuseness, contrast, uniformity, and shadow visibility) The results show an excellent correlation between overall diffuseness and overall uniformity (pearson correlation of 0.86). Overall diffuseness correlates negatively good with perceived contrast (-0.78). Overall diffuseness has a weak negative correlation with perceived shadow visibility. 3.5 Atmosphere perception 3.5.1 Cosiness In general, the ANOVA analyses on the cosiness scores showed significant main effects of presentation, fixtures, color temperature, and intensity (all p<0.001). After splitting up the analyses in diffuse fixtures and spot lighting fixtures, only a significant main effect of color temperature and presentation (p<0.001) was found for the diffuse fixtures and a significant main effect of intensity and presentation (p<0.001) was found for the spot lighting fixtures. The results of the cosiness scores for the diffuse fixtures (averaged over intensity and diffuse fixtures) and spot lighting fixtures (averaged over spot lighting fixtures) are depicted in the next figure. Figure 6. Overall cosiness scores. The left panel shows the results for the main effects of the diffuse fixtures and the right panel shows the results of the spot fixtures The left plot in Figure 6 shows the significant effect of color temperature for the diffuse fixtures for all presentations. The cosiness of a warm room (2700K) is rated higher than the cosiness of a cooler room (6500K) for both the real life experiment (room) as the simulations (display, photo). The right picture in Figure 6 shows the significant effect of

intensity for the spot lighting fixtures for all presentations. Spot lighting at lower intensity is rated more cosy for both the real life experiment and the simulations. Furthermore, the different presentations differ slightly on the absolute cosiness values but all presentations show the same trend in the effects of color temperature and intensity on cosiness. 3.5.2 Liveliness The ANOVA analyses on the liveliness scores showed a significant main effect of presentation, fixtures, and intensity (p<0.001). After splitting up the analyses in diffuse fixtures and spot lighting fixtures, only a significant main effect of intensity and presentation (p<0.001) was found for both diffuse and spot lighting fixtures. The results of the liveliness scores for the diffuse fixtures (averaged over color temperature and diffuse fixtures) and spot lighting fixtures (averaged over spot lighting fixtures) are depicted in Figure 7. Figure 7. Overall liveliness scores. The left panel shows the results of the main effects for the diffuse fixtures and the right panel shows the results of the spot fixtures The main effect of presentation is investigated in more detail using a Tukey post-hoc test which shows that the scores of the simulation (display and photograph) are significantly different from the real life experiment. No significant difference was found between display and photograph. The left panel in Figure 7 shows the significant effect of intensity for the diffuse fixtures for all presentations. The liveliness of brighter room is rated higher than the liveliness of a dimmer room for both the real life experiment (room) and the simulations (display, photo). The right panel in Figure 7 depicts the significant effect of intensity for the spot lighting fixtures, but this effect is only found in the real life experiment (room) and not in the simulations. Spot lighting in the real life experiment at a lower intensity is rated as less lively for the real life experiment. Furthermore, the liveliness scores are significantly higher for spotlighting compared to diffuse lighting. The different presentations differ a bit on the absolute liveliness scores but all presentations show the same trend in the effects of intensity for the diffuse fixtures. The same trend for all presentations is not visible for the spot lighting. The simulations (display and photo) show no effect of intensity whereas the real life experiment (room) shows a big effect. 3.5.3 Tenseness The analyses on the tenseness scores showed significant main effects of presentation, fixtures, color temperature, and intensity (p<0.001). After splitting up the analyses in diffuse fixtures and spot lighting fixtures, only a significant main effect of color temperature and presentation (p<0.001) was found for the diffuse fixtures and a significant main effect of intensity was found for the spot lighting fixtures. The results of the tenseness scores for the diffuse fixtures (averaged over intensity and diffuse fixtures) and spot lighting fixtures (averaged over spot lighting fixtures) are depicted in Figure 8.

Figure 8. Overall tenseness scores. The left panel shows the results of the main effects for the diffuse fixtures and the right panel shows the results of the spot fixtures. The main effect of presentation is investigated in more detail using a Tukey post-hoc test which shows that the scores of the simulation (display and photograph) are significantly different from the real life experiment. No significant difference was found between display and photograph. The left plot in Figure 8 shows the significant effect of color temperature for the diffuse fixtures for all presentations. The tenseness of warm lighting (2700K) is rated significantly lower than the tenseness of cooler lighting (6500K) for both the real life experiment (room) and the simulations (display and photo). The right plot in Figure 8 shows the significant effect of intensity for the spot lighting fixtures for the simulations (display and photo) but not for the real life experiment (room). Spot lighting at lower intensity is rated as less tense for the simulations. Furthermore, the different presentations differ a bit on the absolute cosiness values but all presentations show the same trend in the effects of color temperature. The trends for spotlighting are different in the real life experiment (room) compared to the simulation experiment (display and photo). 3.5.4 Detachment The ANOVA analyses on the detachment scores showed significant main effects of presentation, fixtures, color temperature, and intensity (p<0.001). After splitting up the analyses in diffuse fixtures and spot lighting fixtures, significant main effects of intensity, color temperature, and presentation (p<0.001) were found for the diffuse fixtures and no main effects on detachment were found for the spot lighting fixtures. The results of the detachment scores for the diffuse fixtures are depicted in Figure 9.

Figure 9. Overall detachment scores. The left and right panel show the main effects for the diffuse fixtures The main effect of presentation is investigated in more detail using a Tukey post-hoc test which shows that the scores of the photograph experiment are significantly different from the display and room experiment. No significant difference was found between display and room. The left plot in Figure 9 shows the significant effect of color temperature for the diffuse fixtures for all presentations. The detachment of a cooler room (6500K) is rated higher than the detachment of a warmer room (2700K) for both the real life experiment (room) as the simulations (display, photo). The right panel in Figure 9 depicts the significant effect of intensity for diffuse fixtures for all presentations. A room with diffuse fixtures at low intensity is less detached than a room at higher intensity. No significant effect was found between full and half intensity. The different presentations differ a bit on the absolute detachment scores but all presentations show the same trend in the effects of color temperature and intensity for the diffuse fixtures. 4. MAIN CONCLUSIONS The main conclusions for diffuseness and atmosphere perception will focus on the comparison between the results obtained in the real life experiment (room) and the simulated experiments (display and photo). 4.1 Diffuseness perception The average diffuseness scores of the diffuse fixtures follow exactly the same trend for the real life experiment and the simulation experiments. A significant main effect of presentation was found between simulation experiments and the real-life experiment; no difference was found between the display experiment, and photographic experiment. A possible explanation can be found in the human visual system. Its adaption is stronger in the real-life setup. The biggest difference observed is in the spot lighting fixtures, where the diffuseness scores in the real life experiment are similar to the situation with 2 savios while the scores in the simulation experiment go down further. Although the same trend is visible, a small but constant offset is observed in the absolute diffuseness values. This is probably due to the chosen rendering method. The results regarding the correlation between the different diffuseness questions were similar for the real life experiment and the simulation experiments. Overall diffuseness correlates high with overall uniformity and correlates highly negative with perceived contrast. There was a weak correlation with shadow visibility, but this is probably due to the fact that the room was almost empty and there were few shadows. 4.2 Atmosphere perception The main conclusion for all four atmosphere words (cosiness, liveliness, tenseness, detachment) is that all main effects and trends (over color temperature, fixtures, intensities) can be found in all means of presentations (room, display, and photo). Almost all trends are in the same direction for both the real life experiment as well as the simulation experiments. One exception was found for the liveliness scores for the spot lighting fixtures. The main effect of intensity was only

found in the real life experiment and not in the simulations. The absolute scores of the atmosphere words differ slightly for the different presentations. A possible explanation is that the state of chromatic adaptation and perceived brightness are different for the real life experiment compared to the simulations. Another explanation could be that being physically present in the room makes a difference in the absolute atmosphere perception. More research is needed to find a better explanation. 4.3 Overall conclusion. Although absolute values differ between real life experiments, and simulations, relative values follow the same trends. This implies that it is possible to use renderings on a screen or printout for the evaluation of atmosphere characteristics. With a more detailed simulation, better capabilities of the screen in use (such as HDR screens), more careful calibration, and more attention to the adaptation of the visual system, a better match in absolute numbers should be possible. In combination with fast hardware and rendering software, real-time perception experiments with a large predictive value seem feasible. This will greatly speed up design and evaluation of future luminaire designs. REFERENCES [1] Boyce, P. R., [Human Factors in Lighting], London: Taylor & Francis (2003). [2] Baron, R., Sea, M. S., & Daniels, S. G., Effects of Indoor Lighting (Illuminance and Spectral Distribution) on the Performance of Cognitive Tasks and Interpersonal Behaviors: The Potential Mediating Role of Positive Affect Motivation and Emotion, 16, (1992). [3] Veitch, J. A., Psychological processes influencing lighting quality Journal of the Illuminating Engineering Society, 30 (1), 124-140, (2001). [4] Vogels, I. M. L. C., [Atmosphere metrics], In J. H. Westerink, M. Ouwerkerk, T. J. Overbeek, W. F. Pasveer, B. F. de Ruyter Probing experience: From assessment of user emotions and behaviour to development of products, 25-41. Dordrecht: Springer, (2008). [5] Vogels, I. M. L. C., De Vries, M., & Van Erp, T., Effect of Coloured Light on Atmosphere Perception Technical Report. Eindhoven: Philips Research. (2008). [6] Vogels, I. M. L. C., & Bronckers, X. J., Effect of context and light characteristics on the perceived atmosphere of a space Eindhoven: Philips Research. (2009). [7] Seuntiens, P.J.H. and Vogels, I.M.L.C., Atmosphere creation: the relation between atmosphere and light characteristics Proceedings from the 6th Conference on Design & Emotion, Hong Kong, (2008) [8] Seuntiens, P.J.H., [Internal report Philips Research], (2008). [9] Yoshida et. al., Analysis of Reproducing Real-World Appearance on Displays of Varying Dynamic Range Eurographics, volume 25 (2006). [10] Kuang, J., Evaluating HDR rendering algorithms ACM Transactions on Applied Perception, 4(2) (2007). [11] Yoshida et. al., Perceptual evaluation of tone mapping operators with real-world scenes Proc SPIE, 5666, 192-203 (2005). [12] Ulbricht, C., Wilkie, A., Purgathofer, W., Verification of Physically Based Rendering Algorithms Eurographics, volume 25 (2006). [13] Drago, F. et. al., Adaptive Logarithmic Mapping For Displaying High Contrast Scenes Eurographics, volume 22 issue 3 (2003).