Comparing lighting quality evaluations of real scenes with those from high dynamic range and conventional images

We developed a web application to investigate online-based experiments for the psychovisual assessment of luminous environments. We reproduced online a laboratory experiment conducted in a virtual environment, in which the luminous environment of an office room lit by different combinations of ambient/task lighting was assessed by observers. A control panel of 30 observers performed both experiments. Statistical analyses revealed no statistically significant differences between data collected in the laboratory and online. In addition, an online-based experiment involving 1114 observers was conducted to study the uncontrolled experimental conditions that may impact results. We show that, by increasing the panel size, bias, mainly related to the perceived contrast and brightness of the display and the brightness of the surrounding area is removed. Findings suggest that 100 observers are enough to remove bias.

Section: Introductionmentioning

confidence: 98%

Validation of an online protocol for assessing the luminous environment

Villa

Labayrade

2012

“…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.…”

Section: Existing Experimental Studiesmentioning

confidence: 99%

“…12 The former has been directly associated with perceived brightness and the latter with visual interest. 13 Some studies have found that both mean luminance and luminance variation within an office environment contribute to occupant impressions of preference, 10,11,14 whereas others have discovered that the distribution of luminance values across an occupant’s field-of-view 15,16 as well as the strength of variation are factors of preference. 9,17…”

Section: Introductionmentioning

confidence: 99%

“…Studies that have assessed the perceptual impacts of contrast on daylit space have relied primarily on subjective surveys to explore the relationship between simple photometric measurements and perceived impressions of interior space. [8][9][10][11] Two factors that are widely accepted to impact the fieldof-view in daylit architecture are average luminance and luminance variation. 12 The former has been directly associated with perceived brightness and the latter with visual interest.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contrast measures for predicting perceptual effects of daylight in architectural renderings

Rockcastle

Ámundadóttir

Andersen

2016

Daylit architecture is perceived as a dynamic luminous composition, yet most existing performance metrics were designed to evaluate natural illumination for its ability to adequately illuminate a twodimensional task surface and avoid glare-based discomfort. It may be argued that task-driven approaches based on surface illumination and glare ignore the likelihood that contrast can provide positive impacts on our visual perception of space. Advances in these metrics to accommodate climate-based sky conditions and occupant behavior have improved our ability to evaluate task illumination and glare, yet the same attention has not been paid to evaluating positive perceptual responses to daylight. Existing studies have attempted to link subjective ratings of composition to simple global contrast metrics without reaching consensus. More advanced metrics have been developed in computational graphics and vision fields, but have not been applied to studies in qualitative lighting research. This paper introduces the results from an online experiment where subject ratings of daylight composition are compared to quantitative contrast measures across a series of renderings. This paper will identify which measures correlate to subjects' ratings of visual interest, and introduces a modified contrast algorithm, which can be used as a novel prediction model for visual interest in daylit renderings.

“…Images, such as photographs or simulations, have been proven to be useful for lighting evaluation and control. [12][13][14][15][16] Camera imaging has also been utilized for context-aware applications, 10,11,13 such as using images and quantitative features for daylight controls as well as tuning light levels based on user perception.…”

Section: Introductionmentioning

confidence: 99%

Image-based perceptual analysis of lit environments

Zhao

Reinhart

Paradiso

2018

Modern lighting systems typically provide a number of control parameters, e.g. colour and intensity, which allows nearly infinite possible configurations in one single room. The growing complexity of control makes it increasingly challenging for the user to configure the system and fully utilize the additional degrees of freedom. In contrast, an intuitive control interface takes into account the perceptual meaning of the lighting configurations. Unfortunately, collecting user ratings to construct perceptual models for lit spaces is cumbersome. To address this problem, we introduce an image-based mapping method that can rapidly evaluate the perceptual impression of lighting scenes using photographs or renderings. We discuss potential applications, guidelines and limitations of this method. In summary, we were able to closely approximate ratings-based mapping (normalized dissimilarity value <0.04). Among three dimensionality reduction methods, principal component analysis achieved the lowest dissimilarity and required the least images with a resolution as low as six by six pixels. Furthermore, simulations revealed that one perceptual model might suffice for the same type of offices. Offices of different types, on the other hand, require new mapping.