Evaluating Visual Environment Using Wide-Angle High Dynamic
Range Images
Denis Fan
Institute of Energy and Sustainable Development, De Montfort University,
The Gateway, Leicester, LE1 9BH, UK
[email protected]
ABSTRACT
A good daylight design is aimed to provide fully efficient light for efficient visual performance, and
to ensure a comfortable and pleasing environment appropriate to its purpose. However, the
comfort aspect of daylight design is also closely related to the problem of glare. The aim of this
paper is to describe a new method to evaluate visual environment, i.e. discomfort glare using
High Dynamic Range (HDR) images captured in real-life office environment. HDR imaging is a
set of techniques that allows a greater dynamic range of luminance between light and dark areas
of a scene than normal digital imaging techniques. The approach can accurately represent the
wide range of intensity levels found in real scenes ranging from direct sunlight to shadows, and
the image is usually referred to as a radiance map. Since every pixel represents a measurement
value, the system introduces possibilities for fast measurement with high resolution. Unlike
conventional technique such as spot metering that uses human eye to detect potential glare
sources, an algorithm to determine which parts of the radiance map should be treated as ‘glare
sources’ is proposed. Moreover, the detection should be effective and reliably to all possible glare
sources scenario at different scene configuration.
KEYWORDS
glare source extraction; daylight; comfort; discomfort glare; HDR imaging techniques, clustering.
INTRODUCTION
A good provision of daylight is considered to be highly desirable in terms of reducing the energy
consumption of buildings. Moreover, a good distribution of natural lighting in office buildings may
create a more pleasant working environment and improve the productivity and well being of the
occupants [1,2]. Daylight however can cause visual discomfort by inducing glare and veiling
reflections, causing occupants irritated and annoyed [3].
Glare is defined as the difficultly of seeing in the presence of bright light, e.g. direct or reflected
daylight. It is caused by a significant ratio of luminance between the task, which the subject is
looking at, and the glare source. Factors such as the solid angle between the subject’s field of
view and the glare source, and eye adaptation such as vertical illuminance have significant
impacts on the experience of glare.
Glare can be generally divided into two types, discomfort glare and disability glare. Discomfort
glare results in an instinctive desire to look away from a bright light source, e.g. direct sunlight, or
difficulty in seeing a task. Disability glare reduces a subject’s ability to perceive the visual
information needed for a particular activity. It is usually caused by light scattered within the eye,
and is often a problem in office buildings with large glazing areas. Installing blinds can help to
reduce glare but often results in the loss of daylight benefit as they may remain closed long after
the glare condition has disappeared.
Several equations and indices have been proposed as a means of quantifying the glare
experienced by occupants in daylit environments [4,5,6]. However they have been largely proved
to be inadequate for accurately determining discomfort glare from daylight, mainly because these
indices were derived from experiments that used artificial light sources [7]. Moreover, study
participants reported their subjective perception of glare while looking directly at the light source.
It is generally accepted that the glare sensation experienced in real-world daylit environments is
likely to differ significantly from those laboratory conditions [8,9]. In a recent glare study physical
conditions and user perception were monitored in a daylit laboratory environment and a promising
new index, the Daylight Glare Probability (DGP), was developed [10]. The authors of DGP
acknowledge that further validation of the new index with data from other environments is
required. Another visual comfort field study [11] has also been carried out to investigate whether
people’s perception of visual comfort is different outside laboratory conditions.
Luminance is a photometric measure of the luminous intensity per unit area of light travelling in a
given direction. It is often used to distinguish emission or reflection from surfaces by indicating
how much luminous power will be perceived from a subject’s eye looking at a surface from a
particular angle of view. Luminance is thus an indicator of how bright the surface will appear. It is
also a key factor to calibrate when one is interested to quantify glare by means of glare indices.
Spot meter is the conventional approach to measure luminance values of a scene. To take a
measurement across a small region of the scene, user require to hold the meter directly pointing
to a region for several seconds before the measurement can be recorded manually. The
measurements are usually within 5-10% error margin. However, since the method requires
frequent user interference when recording luminance values, taking up measurements across a
large scene can proved to be tedious and time consuming. Another approach is to employ High
Dynamic Range (HDR) imaging techniques to capture luminance variation across the scene in
terms of an image. The image can form as a luminance map, where individual pixels represent as
the luminance measure of the real scene [12]. The advantage of this method is that a distribution
of luminance variation can be captured within a single luminance map, which makes it more
efficient than using spot metering. Moreover, the captured luminance values were reported to
achieve within 10% error margin when comparing with physical measurements.
With the increasing use of HDR images for daylighting and visual discomfort analysis, a way of
identifying potential glare sources from these images becomes vital. Moreover, representation of
different glare sources such as their geometry, solid angles and vertical illuminance can be
extracted into meaningful information and correlated with qualitative data [11,13]. This paper
presents a simple, but effective algorithm to extract glare sources from HDR images in order to
quantify glare perception quantitatively.
CALCULATING GLARE INDICES
Generally speaking, different glare indices have four physical quantities in common:
 Les sf 
G   g

Lb f ( ) 
where e, f and g are weighting exponents. f() is a complex function of the displacement angle,
which can be expressed using the Guth's position index [14]. Ls is the luminance of the glare
source. The value of G gets high
 when the glare source is bright. s is the solid angle subtended
by this source. The solid angle corresponding to 1 pixel can be considered constant, and a pixel
is considered approximately to 1.365 x 10-6 steradian on a semi-hemispherical image [15].
The position index is defined as the discomfort glare experienced by the observer's line of sight
with respect to the angular displacement (azimuth and elevation) of the source. We employed the
analytical solution suggested by Einhorn [16] to calculate the position index for the glare indices
calculation. Moreover, the study from Iwata and Tokura [14] has found the sensitivity of the glare
caused by a source located below the line of vision tended to be greater than the sensitivity of the
glare caused by a source that is above this line of vision. As a result, the computation of position
index is split up into two separate calculations for area that is above and below the observer's line
of sight. The first equation is the analytical description for the position index located above the
line of vision:
where  is the angle from vertical of plane containing source and line of sight, and  is the angle
between line of sight and line from observer to source. The second equation is for the position
index located below the line of vision
where R  H  Y . D is the distance between eye to plane of source in view direction, H is
the vertical distance between source and view direction, and Y is the horizontal distance between
source and view direction.
2
2
 The BGI glare formula was developed by Petherbridge and Hopkinson [17], and the perception of
glare rating was divided into four different continuous categories named: just noticeable, just
acceptable, just uncomfortable and just intolerable. The equation has the form of
where Psi is the Guth's position index of source si and n is the number of glare sources. The
Guth's position index expresses the change in discomfort glare experienced relative to the
azimuth and elevation of the source and position the observer's line of sight. However, BGI is
limited to small sources and does not predict glare from larger sources accurately. Furthermore,
 the BGI also does not take into account the effect of adaptation when the ratio between the glare
sources luminance and the background luminance is low.
Different to the BGI glare formula, the Cornell glare formula (Daylight Glare Index, DGI) provides
an alternative way of calculating the sensation of discomfort glare experienced by an observer
with the focus of adapting more to predict glare from large sources. The equation is expressed as
where  si is the solid angle in steradian subtended by the glare source modified by the position
of the source with respect to the field of view and the Guth's position index. That means

Furthermore according to the validation studies for this formula, It has shown there is a greater
tolerance of mild degree of glare from large sources than from a comparable artificial lighting
simulation. The CIE formula (CGI) was then developed to correct the mathematical inconsistency
from the BGI formula for multiple glare sources as a unified glare assessment method [18,19].
where E v d is the direct vertical illuminance at the eye due to all the glare sources, and E v i is the


indirect vertical illuminance at the eye level. Furthermore,
across the HDR image, such that E v  (E v d  E v i ) :
the total vertical illuminance


where L(i, j ) is the luminance,
E v is
(i, j ) is the solid angle and is the orientation (i, j ) at pixel (i,j).
Moreover, the cosine of the orientation at pixel (i,j) is simply the dot product between the direction
dir( i, j ) at pixel (i,j) and the view direction dircam of the camera, i.e. cos((i, j ) )  [dir(i, j )  dircam ] .
 The CIE has also proposed

 incorporates the Guth's
a unified glare rating system (UGR), which

position index and the combine aspects of CGI and BGI to evaluate glare perceptions for an
artificial lighting system. To read
 the solid angle of glare sources are
 the rating accurately,
restricted between 3 x 10-4 and 3 x 10-1.
Studies have shown glare formulae described above have a very low correlation when compare
with user perception data in daylight environment. Recently, Daylight Glare Probability (DGP) was
proposed a new glare prediction model [10]. The formula is a modification of the CIE model,
which proved to high correlation between its predicted probability and the user perception data.
The formula has highlighted heavily to the use of vertical illuminance Ev and is expressed as:
The glare indices can be computed after glare sources have been identified from the HDR
images. The next section will give more details of how this extraction process is carried out.
EXTRACTING GLARE SOURCE
The principles of three existing methods for detecting glare sources automatically are:



Calculate the average luminance of the whole image, and classify pixels with luminance
values that are x-times higher than the average luminance value.
Take a fixed value as the threshold, and classify pixels with luminance values that are
higher than this fixed value.
Calculate the average luminance of a given zone, and classify pixels with luminance values
that are higher than this average luminance value.
The approach proposed to extract glare sources from the HDR images in this paper is similar to
the second one described above. Fixed values representing the luminance threshold are taken
from the command line, and use this to carry out a simple thresholding. A second threshold value,
in terms of pixel's solid angle, can also be used to further enhance the first thresholding. Pixels
that are above this threshold value are considered as glare source. Similarly, pixels that are
below this threshold value are considered as normal. Individual pixels that are considered as
glare source are then join up with other glare source pixels that are within their neighborhood
vicinity into different groups, i.e. different glare sources.
Figure 1(a), (c) and (e) shows the results of extracting glare sources by means of HDR images
from three different work desk layouts. The results are encouraging, but the extraction did not
perform well when in situation where individual small glare sources that are closed by did not
grouped as one glare source. For example, the small glare sources were all come from the same
window’s patch, which naturally we will consider this as a single glare source (see dashed circle
in the figure). To overcome this problem, a clustering approach, based on the assessment of the
Euclidean distance between any two glare sources, is applied to merge small glare sources that
are closed by into one glare source. Figure 1(b), (d) and (f) shows the improved results after
applying the clustering. Once glare sources have been extracted using the proposed extraction
algorithm, glare source characteristics such as their centroid, average luminance, average solid
angle and position index are calculated in order to use them to compute different glare indices,
e.g. Daylight Glare index (DGI), Unified Glare Rating (UGR), CIE Glare Index (CGI), Visual
Comfort Percentage (VCP) and Daylight Glare Probability (DGP).
CONCLUSION
The use of HDR imaging techniques for physical measurements has enabled us to qualify
quantitative visual comfort data at relatively high frequency, which has not been possible using
the traditional spot measurement techniques. This paper presented a simple, yet fast and efficient
glare source extraction algorithm using adaptive thresholding and clustering methods to evaluate
visual environment. Furthermore, the quantitative representation from these images can be
incorporated with qualitative visual comfort data that we have collected in [11] to validate existing
glare indices and to potentially develop a new glare metric for visual comfort perception in daylit
environments.
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(a) Work desk 1 before clustering
(c) Work desk 2 before clustering
(e) Work desk 3 before clustering
(b) Work desk 1 after clustering
(d) Work desk 2 after clustering
(f) Work desk 3 after clustering
Figure 1. Glare source extraction results for three different work desk layouts.