Luminance and Color Correction for Display Stitching in Semi-Cave
Virtual Reality
Dariusz Sawicki
1 a
, Łukasz Izdebski
1
, Agnieszka Wolska
2 b
and Mariusz Wisełka
2 c
1
Warsaw University of Technology, Institute of Theory of Electrical Engineering, Measurements and Information Systems,
Warsaw, Poland
2
Central Institute for Labour Protection, National Research Institute (CIOP-PIB), Warsaw, Poland
Keywords: Image Stitching, Multimedia, Virtual Environment, Immersive VR, Cave.
Abstract: The most spectacular example of a virtual reality (VR) environment is Cave (Cave Automatic Virtual
Environment). Image stitching is an essential problem encountered in displaying images in the Cave VR. We
analyzed this problem in the Semi-Cave installation, a low-budget representative of the Cave VR. Seamless
stitching of displayed images requires two independent tasks: geometrical correction and color/luminance
correction. The aim of this work is to present the main aspects and the methods used for color/luminance
correction for seamless stitching of displayed images in the Semi-Cave installation. The proposed procedure
and the software developed for color correction of images were tested and verified. The final effect of
displaying stitched images was subjectively assessed. The impression of immersion into the Semi-Cave VR
was sensed by subjects, and in this way, the correctness of the proposed method was confirmed.
1 INTRODUCTION
The quality of the displayed images is a key issue in
Cave (Cave Automatic Virtual Environment) that
determines the correctness of immersion into the
created VR environment. Immersion into the VR is
understood as a specific concept that defines how
well the VR environment represents the real world
and how well it is perceived (Slater, 2003). The
following parameters are considered as the most
important in the immersion process: correctness of
color and image geometry in the stitched images,
correctness of the color rendering process, perception
of projection, and image resolution (Slater, 2003).
Image stitching is an essential problem
encountered in displaying images in the Cave VR
installation, as well as in many other multimedia
applications. We analyzed this problem in the Semi-
Cave, a low-budget representative of the Cave VR
installation. Image stitching in the Semi-Cave
installation requires two independent corrections:
geometric correction which is the first task and
described in the work (Sawicki et al., 2018); and
a
https://orcid.org/0000-0003-3990-0121
b
https://orcid.org/0000-0003-3912-605X
c
https://orcid.org/0000-0002-7145-6457
color/luminance correctionassuming that the
images are already geometrically corrected.
The need to correct the color of the stitched
images in the Semi-Cave installation results from the
differences in the colors of the displayed images. This
is mainly related to the differences in image
displaying by particular projectors (individual
differences and aging of the equipment). The
difference in color coordinates is a measure of the
color mismatches (Mokrzycki and Tatol, 2011).
The problem encountered in seamless stitching of
images to create a panorama is well described in the
literature (Singh and Saravanan, 2017, Pravenaa and
Menaka, 2016). Geometric correction and color
correction work together in most of the stitching
methods for creating panoramas (Pravenaa and
Menaka, 2016). Nevertheless, advanced methods of
color correction are applied in such cases (Bellavia
and Colombo, 2018). Color stitching for the Semi-
Cave VR requires not only an advanced color
correction method but also solving additional
measurement problems that we would like to
highlight here.
Sawicki, D., Izdebski, Ł., Wolska, A. and Wisełka, M.
Luminance and Color Correction for Display Stitching in Semi-Cave Virtual Reality.
DOI: 10.5220/0008168501370144
In Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications (CHIRA 2019), pages 137-144
ISBN: 978-989-758-376-6
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
137
The aim of this paper is to present the main
aspects of color/luminance correction and the
procedure used for color correction for seamless
stitching of images in the Semi-Cave installation.
2 TECHNICAL ASPECTS AND
PRELIMINARY ANALYSIS
The Semi-Cave was implemented in a rectangular
room, measuring 8.6 m × 4.3 m × 6 m. The image was
created by direct projection onto the four walls of the
room. Image creation was ensured by six projectors
(Canon, XEED WUX400ST) presented in Figure 1.
In the computer system responsible for image
control, three graphic cards (NVidia GTX 980 Strix
OC SLI) were used. Each of the graphic cards
supported two projectors. The whole system ensured
a smooth and sharp display of images at a resolution
of 1920 × 1080. The details of the installation are
described in the work (Sawicki et al. 2017).
Figure 1: Arrangement of projectors in the Semi-Cave
laboratory (Sawicki et al., 2018).
The way in which the images are displayed in
Semi-Cave strongly influences the possibilities of
geometric and color corrections. Stitching of images
should be done differently on the same wall (e.g.,
Image_1 and Image_2 in Figure 1) and on
neighboring walls, respectively (e.g., Image_2 and
Image_3 in Figure 1). In the first case, for the correct
display and ease of correction, it is convenient to use
overlapping images. For geometric correction, an
overlapping part with a width of 50 pixels is
considered. In the second case (in the corner of the
room), the images are corrected at the place of
“contact” (i.e., at the edge of the wall corner). If one
subsystem of display correction works for geometry
as well as for color, the adopted rules must be applied
in both the cases. As a consequence, the color needs
to be equalized in the overlapping part. This problem
does not occur in the case of geometry correction
the reproduction of properly corrected fragments in
the same place does not affect the geometry, but
changes the luminance or color.
On the basis of the analysis of the entire correction
process (geometric and color correction), we assumed
that the geometry correction should be performed as
the first task. This is due to the fact that it does not
require additional measurements but only the visual
assessment of corrected and displayed images.
However, color/luminance correction requires taking
measurements during correction.
3 COLOR SPACE USED IN
SEMI-CAVE
The projectors used enable working in many different
modes (Canon User’s Manual, 2013). However, the
manufacturer declares compliance with the sRGB
space only for the Photo/sRGB mode.
The sRGB color space is defined in the IEC
standard (IEC 61966-2-1:1999, 1999). Linearization
(gamma) dependencies for each coordinate (R, G, B)
are described by equations (1)(4). Due to the
additivity of the color components of radiation, the
corresponding dependence will also apply to the
blackwhite scale (BWthe scale of gray levels).
for R
L
,G
L
,B
L
≤ 0.0031308

  
  
  
(1)
for R
L
,G
L
,B
L
> 0.0031308
 

 
  

 
 

 
(2)
for R,G,B 0.04045



(3)
for R,G,B > 0.04045
 

 

 

(4)
Where R,G,B [0,1] and R
L
,G
L
,B
L
[0,1]. R,G,B are
the color coordinates that define a color in the sRGB
space, for example, for gray scale from black
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
138
(R=G=B=0) to white (R=G=B=1), while R
L
,G
L
,B
L
are
the luminance coordinates of the displayed image.
The dependence between the image luminance
and control parameters (stimulation) in monitors is
described by the power function. At the same time,
the dependence of the brightness of the perceived
image is also not linear and can be described by a
similar functionthe perceptual dependencies of the
sense of sight of humans are described by the Weber
Fechner’s law (Bruce, 2014). Hence, the
mathematical description of nonlinear dependencies
(1)(4) is an attempt to match the technological
properties of the equipment to the perceptual abilities
of the human. The assumption of an appropriate
function describing the nonlinear relations very
strongly affects the perception of differences. In the
software used for controlling graphic devices as well
as for processing images and computer graphics, the
coordinates R,G,B are most often expressed in a
binary form (2
bit_depth
-1). For example, for
bit_depth = 8 the coordinates change in the range of
[0,255]. This requires rescaling from the binary form
to the form expressed in formulas (1)(4) or vice
versa.
It is worth noting that according to the IEC
standard (IEC 61966-2-1:1999, 1999), the power in
equations (2) and (4) has a value of 2.4 instead of 2.2,
that is, the value assumed to be typical in the gamma
correction of PC hardware (Poynton, 2005, Poynton,
2012). In practice, the approximate formulas (5) and
(6) are most often used.
for R,G,B [0, 1] and R
L
,G
L
,B
L
[0, 1]






(5)



(6)
4 MEASUREMENTS AND
PERCEPTUAL
IDENTIFICATION OF COLOR
DIFFERENCES IN SEMI-CAVE
A Chroma Meter CS-200 instrument (Konica Minolta
(Chroma Meter CS-200, 2013) was used to measure
the luminance and color of the displayed images. The
instrument is adjusted to V()the relative spectral
luminous efficacy of the eye adapted to brightness.
This allows the direct measurements of luminance
and X,Y,Z color coordinates in the CIE XYZ color
space. However, it does not allow color correction for
usage in the display software. In addition, the
luminance values of the displayed images in Semi-
Cave are at a low range (below 60 cd/m
2
), which
further reduces the usefulness of this type of
measurement in color correction.
A number of experiments were carried out which
confirmed that the X,Y,Z color coordinates cannot be
determined directly in an efficient and useful way.
Ultimately, a decision was made to carry out the
measurements and color correction indirectly and
independently for each R, G, and B component. In this
case, the measured luminance value will indeed take
into account the curve V(). This is enough to
compare the image fields displayed by the
neighboring projectors, but within only one
component. The display correction will be based on
the determined difference. This will allow displaying
identical colors regardless of how the projectors
display them. On the other hand, this means treating
the entire displaying process (software, graphic cards,
projectors, wall reflectance) as a “black box.” We
know the input parameters (binary values describing
the color of an image), which are defined in the
software and are subject to color correction, and the
output parameters (information displayed on the
Semi-Cave wall), which are subject to measurement.
The aim of the correction software is to adjust the
input parameters according to the changes needed in
the output parameters and take the relevant
measurements.
5 COLOR CALIBRATION IN
SEMI-CAVE: ANALYSIS OF
POSSIBILITIES
For system calibration, a set of color images was
prepared in four groups: for each component R, G, B,
and BW (gray levels). A comparison of all the images
and their corresponding R,G,B color coordinates is
summarized in Table 1. However, unfortunately, the
changes in the color of low-luminance images cannot
be actually recognized in a printout of this table
especially in the case of binary components R (31,0,0)
and B (0,0,31)due to limited printing capabilities.
The analysis of the results of the preliminary tests
leads to quite interesting conclusions. On the one
hand, the results are in line with expectationswith
Luminance and Color Correction for Display Stitching in Semi-Cave Virtual Reality
139
known perceptual properties of the human sight. A
human cannot identify differences at a level of 1 bit
(1/256 of the displayed full scale of color) for any
component in the dark parts of an image. It is assumed
that a human can distinguish a maximum of 6090
shades of one color. In the bright parts of a picture, a
human can distinguish the shades of red color better
than the shades of blue, and in an exceptional
situation of a specific color neighborhood, a
difference at the level of 1 bit would be noticed. On
the other hand, the measurements of luminance in
many of the analyzed cases do not help in
distinguishing the images. This is due to the reflection
properties of the walls in the Semi-Cave. The use of
reflective paint for a projector wall does not enable as
high reflectance as a professional projection screen.
Table 1: The set of used images (R,G,B and BWgray)
with proper R,G,B binary coordinates.
R image
binary
coordinates
G image
binary
coordinates
BW image
binary
coordinates
0,0,0
0,0,0
0,0,0
31,0,0
0,31,0
31,31,31
63,0,0
0,63,0
63,63,63
127,0,0
0,127,0
127,127,127
191,0,0
0,191,0
191,191,191
223,0,0
0,223,0
223,223,223
255,0,0
0,255,0
255,255,255
Color interpolation at a resolution of 1 bit for each
component corresponds to the border perceptual and
measurement possibilities of identifying color
differences. The perceptual capabilities of a human
do not allow identifying differences at such a
resolution in the whole range of luminance that can
be obtained while displaying images. Nevertheless,
experiments have shown that under real conditions in
the Semi-Cave, differences at a level of 1 bit may be
noticed in specific displayed images.
Color correction means interpolation of the values
of each color coordinate in a specific area of a
displayed image. If the side edge of a displayed
rectangle is the place of matching the common color,
it can be assumed that the area on which the
interpolation will be carried out will be a rectangle.
Its height will be consistent with the height of the
displayed image, with one edge being the edge of the
match and the other edge determining the “depth of
penetration” of interpolation in the area of the
displayed image.
Two interpolation parameters need to be defined.
The first is the “depth of penetration.” After
conducting preliminary experiments, it was assumed
that the interpolation would cover one-third area of
the surface of the displayed image. This means that
each image should be divided into three equal parts
(left, central, and right). The luminance and color will
not be modified in the central part, but will be
modified in the area on the left side or right side
where interpolation is associated with the left edge or
right edge of the match, respectively.
The second parameter is the “type of changes”
carried out in the value of the interpolated parameter.
The simplest solution is to change linearly the values
of color coordinates in the stitched images. The
problem seems to be trivial: the color correction of
stitched images is done in a large space and the
differences in corrected colors are small. In such a
large space, local, high color changes may occur
depending on the content of the image. Therefore, the
differences related to color correction would not be
noticeable. However, in many cases, we can see large
surfaces with almost the same colorfor example,
the sky with a similar or the same blue color. In this
situation, because of human perception, incorrect
local color changes will be unnatural.
The simplest linear change is practically
unfavorable in every case. Taking into account the
results of studies on perception and measurement in
real conditions, we assumed that the curve of color
changes resulting from the correction should be
smooth (in the sense of continuity of the first
derivative). This is important because of lateral
inhibition (Bakshi et al., 2017, Hall, 1989), a
phenomenon that causes even the smallest local
unevenness of changes in color or luminance to be
emphasized and perceived by the sense of sight. It is
worth considering the appropriate connecting curve
used in computer graphics and animationcurves
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
140
derived from the set of easing functions (Penner,
2002, Izdebski and Sawicki, 2016). This means that
to describe the changes in the values of color
coordinates, the appropriate (smooth) function should
be used—for a “smooth start and smooth stop.” The
simplest functions that produce good results are the
polynomial functions of InOutQuad and InOutCubic
or non-polynomial InOutSin from the set of easing
functions (Penner, 2002, Izdebski and Sawicki, 2016)
Figure 2.
Figure 2: The InOutCubic easing function an example of
function with a “smooth start and smooth stop” (Izdebski
and Sawicki, 2016).
6 THE RESULTS: CORRECTION
PROCEDURE
Analysis of the results obtained from the conducted
experiments allows proposing a simple procedure for
color correction for stitching images in Semi-Cave.
Correction is carried out independently for R, G, and
B, assuming that in the case of each component, the
other two components are zero. This procedure is
based on a comparison of luminance in specific fields
of adjacent (stitched) images and is carried out in the
following three steps:
S1. Measure the luminance of neighboring (adjacent)
fields (Figure 3).
S2. If the measured luminances have the same value,
finish the correction and stop the procedure; if not,
perform operation S3.
S3. In the correction program, change the displayed
colorby inserting the value of an appropriate
correction factor in the color coordinate. Most
often, it is enough to change the value of the factor
in only one of the neighboring fields, but if the
differences are large, change the correction
factors in both the fields, and return to S1.
The measurements carried out confirmed the
possibility of using approximate formulas in versions
(5) and (6) in real conditions of Semi-Cave.
Therefore, it is possible to propose a simple method
for determining the value of correction factors in the
proposed procedure of color correction.
Figure 3: The image generated by the correction program,
for example, for color G (0,255,0). Correction will take
place along the left or right edgewhere identical images
will be displayed. The colors of the neighboring images are
compared along the joining edge in three fields: upper,
lower, and middle. The middle field corresponds to the
height of the “horizon” of the displayed image. It was
assumed that at such a height, the displayed information is
the most important.
Let L1
k
be the value of the measured luminance in
the first field (e.g., the image on the left side of the
joining edge), where k is one of the components (R,
G, or B) or corresponds to the correction of gray
levels (BWin this case, all three components are
corrected equally in the program). For example, L1
R
is the measured luminance of the red component and
corresponds to R
L
in equations (1)(6). Similarly, let
L2
k
be the value of the measured luminance in the
second field (e.g., the image on the right side of the
joining edge). Let us assume that the correction factor
W for the first field will be introduced in the color
correction program. This means that L2
k
should be the
expected luminance value after correction in the first
field. Taking into account equation (5), the value of
W can be determined on the basis of equation (7).







(7)
For small differences in luminance values (a
maximum difference of 20%30%which
practically always occurs in Semi-Cave), equation (7)
can be represented in a simple approximated form (8)
using the power series expansion.
  

 

(8)
Luminance and Color Correction for Display Stitching in Semi-Cave Virtual Reality
141
The simple correction procedure with feedback
proposed above requires consideration of specific
cases. The full procedure is as follows:
P1. Check if the display geometry is correct. If not,
perform the geometry correction. Select the
two stitched images for color correction (called
in the procedure as the left image and the right
image).
P2. Carry out the correction independently for the
components R, G and B. Repeat the procedure
independently for the appropriate components.
The order is not important.
P2.1. Select a component (R or G or B).
P2.2. Specify the level of color (binary) displayed on
the control screen. As a standard, the color at the level
of R (127,0,0) or G (0,127,0) or B (0,0,127) will be
displayed. In this way, the luminance of the displayed
information corresponds to approximately 18% of the
full range of luminance, which is good enough for
assessing the lighting in typical scenes
P2.3. Display the same control image with the
specified color and specified level on both stitched
images.
P2.4. Measure the luminance in the adjacent fields on
both sides of the joining edge (on the left and right
stitched image independently). Compare the results of
the measurement.
P2.5. Apply display correction (correction factor)
according to formula (8). If the difference in
luminance of measurements between images (left and
right) is small, apply correction only for one image
(either left or right). If the difference is greater (above
15%), apply the correction for both images.
P2.6. Repeat the luminance measurement. If the
difference is aligned, finish the correction for a given
pair of fields (or for a given component), if there are
still measurable differences, repeat operations P2.4
P2.5.
P3. Display both the images in a standard view (such
that the entire surface of the image is visible, not just
the separate squares). Rate visually the stitched
images. Evaluate the adjacent fields at different
heights of the joining edges. If still there are
perceptual or measurement differences, repeat the
whole P2 operation of correction procedure.
On the basis of the procedure proposed for color
correction, an application carrying out appropriate
tasks has been prepared. The software has been
implemented in Visual Studio using the Vulkan
libraries (Sellers and Kessenich, 2016) and OpenGL
(Sellers et al., 2015), and works on the level of shader
drivers (Bailey and Cunningham, 2011). The
application works in two modes: edit mode and work
mode. Both modes use a common code associated
with managing the display of windows in a Windows
environment.
Operations of color correction were combined
with previously developed program for geometric
correction (Sawicki et al., 2018), thereby creating one
subsystem for display correction.
7 VERIFICATION OF THE
SOLUTION
The method developed for color correction was tested
using selected color images. The tests verified the
color composition between the images, which had
different degrees of complexity and showed color
variations. The comparison was based both on
perceptual verification and on the luminance
measurements obtained for each component.
Figure 4: An example of stitching two images with a large
color mismatch between components. The images were
combined with up-down and down-up changes between R
and G: from (255,127,127) to (127,255,127). a) Stitched
images before correction. b) After correction.
Preliminary stitching tests carried out with the
images with color correction within each color
component showed positive results. The stitching
tests were further carried out with specially prepared
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
142
images that had very large color mismatches both
within one component and between two components.
An example of image correction is presented in
Figure 4. The stitching tests were carried out to
confirm the correctness of the operation of the
developed correction system. Changes in the
correction level (or changes in the values of
correction factors) are subject to linear dependencies.
The correction problem is scalable can be scaled to
the range allowed by the maximum hardware settings.
Therefore, if it is possible to achieve adequate
correction for selected (very large) color differences,
it will also be possible to achieve proper correction
for all smaller color differences
Figure 5: An example view of the presented VR
environment generated in Semi-Cave. The photo was taken
from a point consistent with the position of the observer in
VR.
To check the operation of the entire subsystem
developed for the correction of both geometry and
color in Semi-Cave, we used UNITY to create a VR
environment. The principles of perspective projection
for acquiring images were determined, taking into
account the size and shape of the laboratory. It was
assumed that the observer (projection center) is
located exactly in the center of the room. Images
created using the methods of computer graphics allow
defining elements with an accuracy of 1 pixel. In
addition, computer graphics facilitate changing the
projection conditions and display conditions. This
approach allows visual evaluation of the correctness
of the display. In our case, visual evaluation is
considered the best because it gives an opportunity to
evaluate the perception of images and immersion into
a VR environment. We carried out the evaluation with
10 participants (other than authors). The conducted
tests confirmed the correctness of the operation of the
display system. A sample picture taken in Semi-Cave,
using Sponza Palace in Dubrovnik as a model to
create a VR, is shown in Figure 5.
8 SUMMARY
Image stitching in the Semi-Cave installation is a
complex issue involving several independent
problems. The subsystem developed for the
correction of geometry and color is an individual
solution adapted to the conditions of Semi-Cave. In
addition, our software used for display driver allows
for full control of the entire process of displaying
information.
Color correction is a much more difficult task than
geometry correction. It requires obtaining a series of
measurements under strictly defined conditions. In
addition, it requires the use of a specially prepared set
of control images. Therefore, the solution developed
both indicates the specific methods for conducting
correction and helps prepare the environment to carry
out this correction.
The analysis of the process of displaying
information in real conditions of Semi-Cave showed
that the best solution would be to perform an indirect
comparison of colors displayed in the neighboring
images. For this, control images with colors
corresponding to a particular R, G, B component were
used, and the luminance measurement was carried
out. The measurement obtained helped in carrying out
the color correction in a convenient way.
The tests carried out showed that the developed
method allows achieving seamless stitching of
images in terms of their color matching. The method
can give good results even if an extreme color
mismatch existed before correction. The entire
subsystem (for the correction of geometry and color)
was also tested using the VR environment created for
Semi-Cave. It was assumed that the observer’s
location and perspective projection (in the VR) were
consistent with the shape and size of the real room.
The best test in this situation is the perceptual
assessment and the impression of immersion into the
VR. The experiments carried out confirmed the
correctness of the operation of the developed system.
It is worth comparing our method to solutions
known from the literature. The installation conditions
of Semi-Cave allowed performing the geometry
correction independently as the first step. In this way,
we could assume that the images are already
geometrically correctedthis means that it has
simplified the task in relation to advanced
contemporary methods (Bellavia and Colombo,
2018). On the other hand, the task turned out to be
much more difficult because of the need to measure
the luminance/color (and its components)a
problem that does not occur with the typical stitching
of the panorama (Bellavia and Colombo, 2018).
Luminance and Color Correction for Display Stitching in Semi-Cave Virtual Reality
143
The original method of seamless stitching of
images in the Semi-Cave installation is presented in
this paper. However, we only consider it as a
technical tool necessary to create a convincing VR
environment for conducting scientific research in the
future. We are planning to create a VR environment
which will help reproduce images of workplaces,
garden, forest, etc. This will allow examining the
impact of the visual environment (objects, colors,
contrasts, etc.) on the cognitive performance and the
well-being of the subjects.
ACKNOWLEDGEMENTS
This paper has been based on the results of a research
task carried out within the scope of the fourth stage of
the National Programme "Improvement of safety and
working conditions" partly supported in 20172019
within the scope of research and development --- by
the Ministry of Science and Higher Education /
National Centre for Research and Development. The
Central Institute for Labour Protection -- National
Research Institute is the Programme's main
coordinator.
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