Colorimetric Characterization of a
Computer-Controlled Liquid
Crystal Display
Ellen A. Day, Lawrence Taplin, Roy S. Berns*
Munsell Color Science Laboratory, Center for Imaging Science, Rochester Institute of Technology, 54 Lomb Memorial
Drive, Rochester, NY 14634-5604
Received 1 March 2003; revised 26 January 2004; accepted 17 February 2004
Abstract: A new method was used to characterize computercontrolled liquid crystal displays (LCDs). The characterization, which was performed to enable colorimetric image
display, included channel independence, spatial independence, screen uniformity, and colorimetry. The colorimetric
model consisted of three one-dimensional look-up tables
(LUTs) describing each channel’s optoelectronic transfer
function and a 3 ⫻ 4 matrix transformation that included
black-level flare. The matrix coefficients were estimated
statistically by minimizing the average CIEDE2000 color
difference for a data set sampling the display’s colorimetric
gamut. The LUTs were recreated dynamically throughout
the optimization of the matrix coefficients. The characterization was implemented with three different instruments to
evaluate the robustness of the method with respect to measurement uncertainty. The average performance ranged between 0.1 and 0.4 ⌬E00 and was well correlated with
instrument precision. The optimization approach improved
performance by a factor of two compared with direct measurements. Despite differences in instrument design, the
chromaticities of each primary following optimization and
black-level flare compensation were very similar. This excellent performance was a result of the display’s optoelectronic properties well matching the model assumptions. The
technique was also used to characterize three additional
LCD displays ranging in their matching of the model assumptions. In this case, performance worsened. For one
display, more complex models would be required for colorimetric characterization. Finally, a colorimetric characterization based on measurements at the center of the display and perpendicular to the face was used to predict
measurements at the edges and at different angles. The
*Correspondence to: Roy S. Berns, Munsell Color Science Laboratory,
Center for Imaging Science, Rochester Institute of Technology, 54 Lomb
Memorial Drive, Rochester, NY 14634-5604 (e-mail: berns@cis.rit.edu)
© 2004 Wiley Periodicals, Inc.
Volume 29, Number 5, October 2004
results indicated that characterizations would be required
at multiple positions and angles in order to achieve sufficient accuracy. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29,
365–373, 2004; Published online in Wiley InterScience (www.interscience.
wiley.com). DOI 10.1002/col.20046
Key words: LCD colorimetry; display colorimetry; display
profiles; color management
INTRODUCTION
Flat-panel liquid-crystal displays (LCDs) are gaining recognition as being superior to cathode-ray-tube displays
(CRTs) in terms of luminance, contrast ratio, sharpness, and
spatial uniformity and have begun to replace CRT monitors
for display-based visual experiments. It is critical that these
psychophysical images can be transformed to colorimetric
definitions accurately. Careful colorimetric characterization
of a display, whether it is a CRT display or an LCD, will
ensure the greatest degree of accurate representation.
A significant amount of research has been carried out in
modeling the colorimetry of computer-controlled CRT displays and in evaluating characteristics such as channel independence, spatial independence, and spatial uniformity, summarized by Katoh, Deguchi, and Berns,1–2 and Berns and
Katoh.3 Research exploring the colorimetric characterization
of computer-controlled LCDs is ongoing. In 1998, Fairchild
and Wyble, recognizing the fundamental differences between
liquid-crystal and cathode-ray-tube technologies, developed a
highly successful approach to LCD colorimetry.4 The theoretical underpinnings of their research are summarized below.
The relationship between the signal used to drive a display and the radiant output produced by that channel is
termed the optoelectronic transfer function, OETF.5 This is
usually a nonlinear relationship for computer-controlled devices. To characterize such a device, this relationship must
be modeled. For example, CRT monitors are usually
365
characterized using the gain-offset-gamma or gain-offsetgamma-offset models, often referred to as the GOG or
GOGO models, respectively.1,3 Unfortunately, these models
cannot be used to characterize an LCD accurately. Essentially, a CRT OETF has a physics basis, whereas an LCD
OETF depends on the specific cell construction, the operating mode, and usual remapping via a voltage ladder or
look-up table to compensate for a suboptimal relationship
between voltage and perceived lightness or to mimic CRTs.
As a consequence, analytical functions such as the GOG,
GOGO, or other functions, typically sigmoidal,6 will only
coincidentally describe an LCD OETF well. The solution is
to build one-dimensional look-up tables (LUTs), generally
formed by subsampled measurements and linear or nonlinear interpolation, described by Eq. (1)
R ⫽ LUT共dr 兲
G ⫽ LUT共dg 兲
(1)
B ⫽ LUT共db 兲
0 ⱕ R,G,B ⱕ 1 ,
where d defines digital counts and R, G, and B are radiometric scalars for the red, green, and blue channels, respectively. By definition, these scalars are constrained between
zero and unity. That is, L␭,r ⫽ RL␭,rmax, where L␭ is spectral
radiance and the subscript “rmax” specifies the maximum
output for the red channel. Similar expressions can be
written for the green and blue channels.
Flare can be especially detrimental to colorimetric accuracy if not taken into account. Three main types of flare
have been described for computer-controlled CRT displays.
These are external flare, which comes from reflections on
the monitor from ambient room light; internal flare, which
comes from internal scattering; and a second type of internal
flare, which comes from output from other channels at the
same pixel location, a result of improper display setup.
Katoh et al.1,2 (see also Berns and Katoh3) derived how the
two types of internal flare cascade into a single flare term,
corresponding to the display’s black level. For LCDs, there
is often significant radiant output at the black level caused
by the liquid crystal having a minimum transmittance factor
well above zero. This flare also cascades into the single flare
term. Thus, Eq. (2) is used to describe the relationship
between the radiometric scalars and CIE tristimulus values:
冋 册
冋
X
Y
Z
⫽
EXPERIMENTAL PROCEDURES
Equipment 1
X r,max ⫺ Xk,min Xg,max ⫺ Xk,min Xb,max ⫺ Xk,min Xk,min
Yr,max ⫺ Yk,min Yg,max ⫺ Yk,min Yb,max ⫺ Yk,min Yk,min
Zr,max ⫺ Zk,min Zg,max ⫺ Zk,min Zb,max ⫺ Zk,min Zk,min
⫻
366
where the “max” subscript defines each channel’s maximum
output and subscript “kmin” defines the black-level radiant
output. Eqs. (1) and (2) were first used by Fairchild and
Wyble,4 and later by Gibson and Fairchild,7 characterize
computer-controlled LCDs to colorimetrically.
Implicit in the use of Eq. (2) are the assumptions of
channel scalability and independence.8 That is, the spectral
radiance spectra of each channel following black-level compensation are scalable and drive signals in a given channel
do not affect the spectral radiance of other channels. It is
known that the spectral transmittance of liquid crystals vary
as a function of voltage.9 Their peak wavelength shifts
toward shorter wavelengths with decreasing transmittance.
Depending on the spectral properties of the display light
source, these wavelength shifts result in spectra that are not
scalable. Furthermore, for some LCDs, there can be a significant lack of independence, due to inherent optical and
electronic properties. More details about both assumptions
can be found in Silverstein,10 Marcu and Chen,11 and Yoshida and Yamamoto.12 If the assumptions of channel scalability and independence are not met, more complex approaches are required (e.g., see references 6, 12, 13, and 14).
In our experience, colorimetric performance has been
constrained by measurement uncertainty rather than model
complexity. In particular, uncertainty in black-level radiant
output has led to reduced performance. Accordingly, a
method was developed to statistically estimate the blacklevel flare, important if the measuring device has poor
low-light sensitivity.15 In the current experiments, we extended the reference 15 approach and estimated all of the
matrix coefficients simultaneously using nonlinear optimization, changing the LUTs dynamically with each iteration
of the optimization. The objective function minimized the
mean CIEDE2000 color difference between measured and
estimated colors sampling the display’s color gamut. This
optimized matrix accounted for the limitations in channel
scalability and independence, to some extent. It also reduced the need for very precise measurements of the blacklevel flare.
The purpose of this article is to describe methodologies
and performance of a typical LCD colorimetric characterization for use in psychophysics and colorimetric image
display.
冤 冥
R
G
B
1
,
册
(2)
A 22⬙ flat-panel Apple Cinema liquid crystal display controlled by a G4 Power Mac was characterized. This monitor
has a 160° viewing angle and an antiglare hard-coat screen
treatment. It is a thin film transistor (TFT) active-matrix
liquid crystal display with 1920 ⫻ 1200 pixel resolution.
The gamma was set to “uncorrected gamma (native)” in the
ColorSync Profile of the monitor. The white point was set to
“no white point correction (native),” and the brightness was
set to the maximum setting. A white stimulus on this monCOLOR research and application
TABLE I. CIEDE2000 mean color difference to the
mean (MCDM) across backgrounds and stimuli for
eight stimuli.
Digital counts
CIEDE2000 MCDM
Color
Red
Green
Blue
Across
backgrounds
Across
stimuli
Black
Gray
White
Dark red
Red
Dark green
Green
Dark blue
Blue
0
128
255
128
255
0
0
0
0
0
128
255
0
0
128
255
0
0
0
128
255
0
0
0
0
128
255
0.06
0.30
0.06
0.12
0.04
0.18
0.03
0.07
0.01
0.06
0.06
0.21
0.06
0.09
0.09
0.09
0.07
0.15
itor (dr ⫽ dg ⫽ db ⫽ 255) with this setup had a luminance
of 111 cd/m2.
Three instruments were used in the characterization to
check the robustness of the method: an LMT C1210 Colormeter, recording tristimulus values in units of lumens per
square meter; a PhotoResearch Spectrascan 650 spectroradiometer, recording spectral radiance, chromaticities, and
luminance (Y) in units of candelas per square meter; and a
Minolta CA-100 Color Analyzer, recording chromaticities
and luminance (Y) in units of candelas per square meter. We
expected each instrument to result in different colorimetric
characterizations: The LMT and Minolta colorimeters have
different filter fits to color-matching functions. The three
instruments have different collection geometries, measurement apertures, polarization sensitivities, and dynamic
ranges. All equipment was warmed up and calibrated as
necessary. Five measurements of each displayed color were
averaged.
Software
The colorimetric characterization was performed through
the MATLAB16 software environment. MATLAB was used
to display test colors and interact with the measurement
instruments. We also use MATLAB for psychophysics and
colorimetric-image display; it is critical to display the stimuli for colorimetric characterization within the identical
software environment. Other software may rerender the
display profile.
Lighting
Measurements are taken using the identical lighting conditions as those to be used for the psychophysical experiment,
in this case, a completely darkened room. Any flare resulting from the display itself was corrected for in the model.
The spectral power distribution of the white point of the
display was used for all CIELAB calculations, along with
the CIE1931 2° standard observer.
Volume 29, Number 5, October 2004
Test Image
A 400 ⫻ 400 pixel square stimulus was displayed in the
center of the LCD. The remainder of the display was set to
black (dr ⫽ dg ⫽ db ⫽ 0) during the colorimetric characterization and to other defined colors during the preliminary
experiments.
Preliminary Experiments
A set of simple experiments was first performed using the
LMT illuminance colorimeter to evaluate spatial independence, screen uniformity, and additivity, in similar fashion
to analyses we perform when evaluating CRT displays for
use in visual experiments.17 Our philosophy has been to
seek out displays that have acceptable optoelectronic properties rather than model and account for their deficiencies.
On an ideal monitor the background color will not affect
the foreground color. For this monitor, spatial independence
was evaluated with nine colors measured on nine backgrounds for a total of 81 measurements. Table I lists the
digital counts used to create the colors and backgrounds.
CIELAB values were calculated for each color combination
using the white measurement as the reference white. The
average CIELAB value for each color was calculated over
all the backgrounds and CIEDE2000 color differences18
were calculated between each measurement’s CIELAB coordinates and the color’s average value. The average of
these CIEDE2000 values for the nine colors on a given
background is called the mean color difference from the
mean (MCDM)19 across stimuli. The average CIEDE2000
value for each color over all nine backgrounds is called the
MCDM across backgrounds. The calculated MCDMs are
shown in Table I. The overall MCDM was 0.10⌬E00. Based
on these results, this monitor had excellent spatial independence.
Uniformity across the horizontal center of the screen was
also evaluated. Gray, red, green, and blue colors were
measured on the left, center, and right portions of the screen
and compared. Table II shows the color differences among
these colors as a function of position; all of these color
differences are small. The average MCDM for these measurements was excellent at 0.18⌬E00.
The tristimulus values of the red, green, and blue channels at their highest output should add to equal the tristimulus values of the display white; this assumption is called
additivity. However, this is rarely achieved exactly. For this
display, the measured white’s tristimulus values were lower
than the sum of measured peak red, green, and blue by 1%.
This display exhibited excellent additivity.
TABLE II. CIEDE2000 color differences between different locations on the display.
Left and middle
Middle and right
Left and right
Gray
Red
Green
Blue
0.1
0.2
0.3
0.1
0.3
0.3
0.2
0.2
0.3
0.4
0.3
0.7
367
mization, the coefficients of the 3 ⫻ 4 matrix were adjusted
until the average CIEDE2000 color difference between
measured and estimated tristimulus values for all three data
sets was minimized. After each adjustment to the 3 ⫻ 4
matrix the look-up tables were recalculated. The optimization included a constraint that bounded the radiometric
scalars between zero and unity. Figure 1 shows a flowchart
of the measurement, calculation, and optimization process.
CIELAB values were calculated from both the measured
and predicted tristimulus values. Color differences between
the measured and estimated values were calculated using
CIEDE2000. This entire procedure was repeated for each of
the three instruments.
FIG. 1.
Experiment flowchart.
RESULTS AND DISCUSSION
Procedure
Three data sets of RGB values were created. The first was
11-step ramps, equally spaced in digital counts from 0 to
255 for the red, green, and blue channels individually, as
well as in combination to create neutrals. The second set
was a regular sampling of the RGB color gamut made up of
a 5 ⫻ 5 ⫻ 5 grid (125 colors). The third set, also a regular
grid, was created to more carefully check the model performance for dark colors and was made up of a 5 ⫻ 5 ⫻ 5 of
digital counts from 0 to 25. All of these colors were displayed on the monitor and their colorimetric values measured using each of the three instruments. Each recorded
measurement was an average of five successive measurements. The remainder of the display was set to black.
The measured tristimulus values were normalized so that
the Y value at white digital counts (dr ⫽ dg ⫽ db ⫽ 255) was
equal to unity (i.e., luminance factor). This normalization
was desirable because the LMT is an illuminance colorimeter. For vision research, these tristimulus values can be
converted to units of luminance by multiplying by the
luminance of the display’s white point (e.g., 111 cd/m2).
The measured tristimulus values of each primary ramp
were transformed to radiometric scalars by inverting Eq.
(2), shown below in Eq. (3.) Three one-dimensional LUTs
of radiometric scalars corresponding to 256 digital counts
were created for each primary ramp from the 11-step measurements using piecewise cubic-spline interpolation.
冋 册
冋
R
G
B
⫽
X r,max ⫺ Xk,min Xg,max ⫺ Xk,min Xb,max ⫺ Xk,min
Yr,max ⫺ Yk,min Yg,max ⫺ Yk,min Yb,max ⫺ Yk,min
Zr,max ⫺ Zk,min Zg,max ⫺ Zk,min Zb,max ⫺ Zk,min
冋
X ⫺ X k,min
Y ⫺ Yk,min
Z ⫺ Zk,min
册
册
⫺1
.
(3)
The 3 ⫻ 4 transformation matrix was initially defined using
direct tristimulus measurements of the black level and each
channel’s maximum radiant output. Using nonlinear opti368
The red, green, and blue ramp spectral radiance data were
each analyzed using principal component analysis. One
eigenvector (dimension) accounted for 99.999%, 99.999%,
and 99.997% of the total variance for the red, green, and
blue ramp spectra, respectively. Although liquid crystal
spectral transmittances vary with voltage, for this particular
display, this transmittance dependency had a small effect on
reducing each primary’s scalability. The relative spectral
radiance of the first eigenvector for each ramp, normalized
by peak height to unity, is plotted in Fig. 2. It was expected
that the red and green channels would have excellent scalability given the narrow bandwidth of their spectra. We
would have expected that for the blue channel, the total
variance explained by one dimension would have been
lower given its wide-band radiant output.
To visualize channel scalability, the estimated black level
tristimulus values were subtracted from the measured tristimulus values of each ramp. The tristimulus values were
FIG. 2. Relative spectral radiance (normalized by peak
height) of each channel based on principal component
analysis.
COLOR research and application
FIG. 3. Chromaticities of each 11-step primary ramp following optimized flare correction.
FIG. 5. Radiometric scalars, R, G, and B, (data points), and
interpolated values (lines) following optimization.
then transformed to chromaticities and plotted in Fig. 3. If
each channel has scalable spectra, its ramp chromaticities
will plot as a single point. The red primary ramp chromaticities have the least variability, whereas the blue primary
ramp chromaticities have the largest variability. The direction of the variability is consistent with changes in liquidcrystal spectral transmittance as a function of voltage. The
amount of variability is correlated with the spectral properties of each channel. As a comparison, the chromaticities of
each channel are plotted without subtracting the black level,
shown in Fig. 4. This is equivalent to using a 3 ⫻ 3
transformation to relate scalars and tristimulus values, as
commonly used in the colorimetric characterization of CRT
displays. Clearly, this approach would lead to very poor
performance because the assumption of scalability would be
violated dramatically.
At first glance, it might appear that the principal component
analyses and chromaticity plots are inconsistent. Although over
99.99% of the total spectral variance is described by a single
dimension, the chromaticities are not invariant. In our experience, extremely small variation in spectra results in clear
differences in colorimetry; we have often observed limited
correlation between statistical analyses of spectra and colorimetry.20 Furthermore, principal component analysis behaves as a
spectral noise filter, removing bias and measurement noise.
The chromaticities are still subject to measurement uncertainty.
The chromaticities most different from the average corresponded to the lowest digital signals. In addition, the black
level tristimulus values were derived to minimize error
throughout the display’s color gamut. Had the black level been
optimized for only these primary ramps, better chromaticity
invariance would have resulted.15
The final primary-ramp look-up tables are plotted in Fig.
5. The shape of the OETF is the same as for a CRT,
indicating that the display manufacturer has designed this
LCD to mimic CRTs used in computer display.
The 3 ⫻ 4 transformation matrices (rounded to two
places past the decimal), based on direct measurements and
optimization, are shown in Eqs. (4) and (5), respectively, for
the LMT instrument. The greatest change occurred for the
blue channel, confirming that this technique is compensating for the lack of scalability of this channel. (The differences analyzed via their chromaticities and luminance factor
are described below.)
冋 册
X
Y
Z
⫽
measured
冋
51.09 30.12 12.05 0.41
27.74 61.52 11.63 0.43
0.83 6.22 58.41 0.33
册冤 冥
R
G
B
1
(4)
冋 册
X
Y
Z
FIG. 4. Chromaticities of each 11-step primary ramp based
on direct measurements and without flare compensation.
Volume 29, Number 5, October 2004
⫽
optimized
冋
51.11 30.17 12.09 0.41
27.74 61.59 11.57 0.43
0.86 6.26 58.44 0.33
册冤 冥
R
G
B
1
.
(5)
369
TABLE III. CIEDE2000 color differences between the
measured and estimated stimuli using the optimization technique for the LMT illuminance colorimeter.
Average
RGB ramp and gray-scale data
Full color-gamut data
Dark color-gamut data
0.1
0.1
0.1
Standard
Deviation
0.1
0.1
0.1
Maximum
0.4
0.4
0.3
The color differences comparing measured and forwardmodel predicted data are shown in Table III. For comparison, Table IV shows the color differences resulting when an
optimization of the 3 ⫻ 4 matrix and updated LUTs are not
used. The optimization improved performance by a factor of
2. Both sets of color differences are quite low, indicating
that this display has excellent optoelectronic properties and
is well suited for psychophysics and colorimetric-image
display.
The characterization was repeated using both the PhotoResearch PR-650 spectroradiometer and the Minolta color
analyzer. The results of these characterizations are shown in
Tables V and VI. The spectroradiometer had worse performance than the illuminance colorimeter by a factor of 2. The
color analyzer had worse performance than the illuminance
colorimeter by a factor of 4. However, the worst case
scenario (average accuracy of 0.4⌬E00) is still quite good.
All of these colorimetric characterizations would be acceptable for use in psychophysics and colorimetric image display, whether large colored areas or complex images were
displayed. The performance ranking is consistent with the
measurement precision of each instrument.
The measured and optimized chromaticity coordinates for
the three instruments based on the Eq. (2)-type matrix
transformations are shown in Table VII. The black-level
flare was subtracted from each primary’s tristimulus values
before chromaticities were calculated. The white was based
on a summation of these primary tristimulus values. The
black-level was calculated from the last column of the
transformation matrix. Also shown is the luminance factor
(Y) for each stimulus. The differences in colorimetry between measured and optimized values for the primaries
were quite small. This is further evidence that for this
display, limitations caused by changes in liquid-crystal
transmittance as a function of drive level were nearly negligible.
A comparison of the differences in absolute accuracy in
TABLE IV. CIEDE2000 color differences between the
measured and estimated stimuli using direct measurements for the LMT illuminance colorimeter.
RGB ramp and gray-scale data
Full color-gamut data
Dark color-gamut data
370
TABLE V. CIEDE2000 color differences between the
measured and estimated stimuli using the optimization technique for the PhotoResearch PR-650 spectroradiometer.
Average
Standard
Deviation
Maximum
0.2
0.3
0.2
0.2
0.2
0.1
0.8
0.8
0.5
RGB ramp and gray-scale data
Full color-gamut data
Dark color-gamut data
Average
Standard
Deviation
Maximum
0.2
0.2
0.2
0.1
0.1
0.1
0.8
0.4
0.7
measuring the colorimetric properties of this display is
given in Table VIII. The PhotoResearch spectroradiometer
and the LMT illuminance colorimeter or Minolta color
analyzer had similar chromaticity measurements for red,
blue, and white. The green-channel chromaticities were
dissimilar. The Minolta and LMT had similar measurements. The luminance factor measurements were all similar.
It is unknown whether the green-channel difference between the spectroradiometer and the colorimeters was due
to wavelength error in the spectroradiometer or filter fit for
the colorimeters. Given that the peak radiances of the green
and red primaries are very close to the peak tristimulus
values of y␭ and x␭, respectively, we expected, in general,
reasonable similarity between all the instruments. Overall,
the similarity was quite good, though unexpected because
the two colorimeters have different spectral sensitivities and
different spectral fit to color-matching functions, each instrument has a different dynamic range, each instrument has
a different measurement aperture, and each instrument has
different polarization sensitivity.
CONCLUSIONS
Having an accurate transformation between digital signals
and colorimetry (or spectral radiance) for computer-controlled displays is critical for visual psychophysics and color
reproduction. For at least 10 years, we have had good
success with CRT displays. For the last 5 years, we have
been even more successful with LCDs. The LCD approach,
as described in this publication, requires characterizing the
OETF via three one-dimensional look-up tables and each
channel’s maximum output and black-level flare via a 3 ⫻
4 matrix. When these parameters are first directly measured
and then empirically optimized, the performance can be
excellent. Perhaps the key to our success is our philosophy
to use only displays with good optoelectronic properties
TABLE VI. CIEDE2000 color differences between the
measured and estimated stimuli using the optimization technique for the Minolta TV color analyzer.
RGB ramp and gray-scale data
Full color-gamut data
Dark color-gamut data
Average
Standard
Deviation
Maximum
0.4
0.3
0.4
0.3
0.2
0.2
1.6
1.1
0.9
COLOR research and application
TABLE VII. Measured and optimized chromaticity coordinates for all three instruments based on Eq. (2)-type
matrix transformations.
LMT
Measured
Optimized
Difference
PhotoResearch
Measured
Optimized
Difference
Minolta
Measured
Optimized
Difference
x
y
Y
x
y
Y
⌬x
⌬y
⌬Y
x
y
Y
x
y
Y
⌬x
⌬y
⌬Y
x
y
Y
x
y
Y
⌬x
⌬y
⌬Y
Red
Green
Blue
White
Black
0.6458
0.3479
0.2730
0.6454
0.3478
0.2732
⫺0.0004
⫺0.0001
0.0002
Red
0.6458
0.3466
0.2686
0.6459
0.3462
0.2682
0.0001
⫺0.0004
⫺0.0005
Red
0.6529
0.3416
0.2666
0.6522
0.3423
0.2694
⫺0.0007
0.0007
0.0029
0.3074
0.6317
0.6108
0.3073
0.6315
0.6117
⫺0.0001
⫺0.0002
0.0009
Green
0.3182
0.6208
0.6104
0.3184
0.6207
0.6107
0.0002
⫺0.0001
0.0003
Green
0.3054
0.6335
0.6199
0.3033
0.6351
0.6178
⫺0.0021
0.0016
⫺0.0021
0.1439
0.1384
0.1120
0.1444
0.1377
0.1114
0.0005
⫺0.0007
⫺0.0005
Blue
0.1468
0.1468
0.1186
0.1468
0.1468
0.1187
0.0000
0.0000
0.0001
Blue
0.1447
0.1363
0.1078
0.1446
0.1360
0.1073
⫺0.0001
⫺0.0003
⫺0.0005
0.3594
0.3889
0.9957
0.3594
0.3886
0.9964
0.0000
⫺0.0003
0.0007
White
0.3632
0.3895
0.9971
0.3632
0.3886
0.9976
0.0000
⫺0.0009
0.0004
White
0.3619
0.3899
0.9953
0.3619
0.3902
0.9945
0.0000
0.0003
⫺0.0008
0.3469
0.3686
0.0043
0.3498
0.3682
0.0043
0.0029
⫺0.0004
⫺0.0001
Black
0.3245
0.3674
0.0029
0.3256
0.3672
0.0028
0.0011
⫺0.0002
⫺0.0001
Black
0.3524
0.3670
0.0047
0.3514
0.3682
0.0048
⫺0.0010
0.0012
0.0000
such as spatial independence and additivity. Accordingly,
we perform preliminary testing on candidate displays as
described in the text.
However, we have encountered some limitations using
this display in visual experiments. The characterization described using the LMT colorimeter was used to render
spectrally or colorimetrically defined images.21 Paired-comparison visual experiments were performed where observers
evaluated a pair of images, displayed side by side (hence the
spatial preliminary analysis), and compared with actual
objects viewed in a light booth.22,23 For some observers, the
viewing angle for the leftmost images were too extreme,
resulting in color changes caused by viewing angle rather
than by the image-capture technique. These observers
seemed to orient between the display and light booth and
rotated their head rather than moved it to be perpendicular
to both the display and light booth. This contributed to
greater observer uncertainty than we usually encounter. For
these observers, the colorimetric characterization based on
measurements perpendicular to the faceplate were not indicative of the light the observers saw. In future experiments
of this type, we will have the image pairs viewed successively in a single spatial location rather than simultaneously
side by side. Angle of view dependencies, of course, is a
known limitation of LCDs; however, with each new generation of these displays, this limitation becomes less problematic.
To start to investigate the issue of viewing geometry and
ambient lighting conditions we conducted an additional
experiment. The appendix contains the results of using the
PR-650 spectroradiometer to measure three additional displays for several viewing angles and ambient lighting conditions.
Another area of concern is the color appearance of these
TABLE VIII. Differences in chromaticity and luminance factor between each listed instrument for the optimized
data listed in Table VII.
Instrument pair
PhotoResearch - LMT
Minolta - LMT
Minolta - PhotoResearch
⌬x
⌬y
⌬Y
⌬x
⌬y
⌬Y
⌬x
⌬y
⌬Y
Volume 29, Number 5, October 2004
Red
Green
Blue
White
Black
0.0005
⫺0.0016
⫺0.0050
0.0068
⫺0.0055
⫺0.0038
0.0063
⫺0.0039
0.0012
0.0111
⫺0.0108
⫺0.0011
⫺0.0040
0.0036
0.0060
⫺0.0151
0.0144
0.0071
0.0024
0.0091
0.0073
0.0002
⫺0.0017
⫺0.0041
⫺0.0022
⫺0.0108
⫺0.0114
0.0038
0.0000
0.0012
0.0025
0.0016
⫺0.0019
⫺0.0013
0.0016
⫺0.0031
⫺0.0242
⫺0.0010
⫺0.0015
0.0016
0.0000
0.0005
0.0258
0.0010
0.0020
371
TABLE IX. Description of additional LCD monitors
tested.
LCD
Manufacturer
Model
Diagonal/Size
(inches)
1
2
3
Samsung
Apple
Sony
SyncMaster 210T
ibook G3 Laptop
SDM-N50 Desktop
21
12
15
displays in typical viewing environments. Although there is
ample information about surround effects and partial adaptation issues,24 we suspect that the higher luminances generated by LCDs may require different approaches (likely
simpler) to those we have used for CRTs.
Also of concern is the errors introduced when a model is
implemented in the workflow for colorimetric visual display, whether for psychophysics or ICC-type color management. Quite often the CRT display is an output device for
specific colorimetric coordinates. The forward model and its
evaluation are testing the inverse relationship. Although not
described in this publication, we perform additional analyses to validate the quality of the inverse model, in essence,
specifying tristimulus values, calculating their digital
counts, displaying and measuring these colors, and comparing the measured and specified colorimetric coordinates.25 This
approach has been used to evaluate color-gamut mapping.26
Computer-controlled LCDs are an integral part of our
research programs at the Munsell Color Science Laboratory.
By preselecting candidate displays, using the approach described in this publication to define the display’s colorimetric properties, and careful attention to viewing geometry and
ambient conditions, these devices are excellent to represent
object colors. We are particularly enthusiastic about the
latest displays that have very high resolution and maximum
luminance while exhibiting minimal horizontal angular dependencies.
As a final comment, we are also using this optimization
technique for CRT colorimetry. For CRT setups where it is
desirable to have minimal radiant emission at the black
level, very few instruments have sufficient dynamic range to
directly measure the black level. This approach is very
effective in estimating the black-level radiant output.
APPENDIX
The colorimetric characterization technique described in
this publication was repeated using three additional LCD
displays. To test the color variation that would occur from
a user viewing the full width of a display from a fixed
location, measurements were taken from the center of the
display at the incident angle that would occur if the viewer
looked at the edges from a viewing distance of 0.5 m. Of the
instruments used in the previous experiment, only the PhotoResearch Spectrascan 650 spectroradiometer allowed for
accurate angular control and, therefore, was used for all
measurements.
The three LCD displays were selected based solely on
their availability within our laboratory and are listed in
Table IX. Measurements were made of 11-step RGB ramps
and the 5 ⫻ 5 ⫻ 5 RGB grid sampling of the entire color
gamut. The stimulus was a 400 ⫻ 400 pixel square area
surrounded by black. The results for the forward-model
estimations of each experimental setup are listed in Table X.
The perpendicular measurement angle corresponds to the
measurements made of the Apple Cinema Display, described above. The model accuracy for all three displays
was considerably worse than those achieved for the Cinema
display. None of these displays would be used for psychophysics research, particularly when compared with the accuracies we have achieved with other displays. For colormanagement purposes, LCD 1 could be used, having
average performance less than 1⌬E00. LCD 3 should not be
used unless higher accuracy could be achieved. The assumptions required to use a 3 ⫻ 4 matrix [Eq. (2)] effectively were not met, in particular, channel independence and
additivity. Perhaps the more complex approaches described
in references 12–14 would result in sufficient accuracy.
LCD 2 had intermediate performance.
TABLE X. CIEDE2000 color differences between the measured and estimated stimuli using the optimization
technique for additional displays, measurement geometries and lighting conditions.
LCD
1
Measurement
angle
Room
lighting
Perpendicular
Right 23°
Up 18°
Down 18°
Perpendicular
2
Right 14°
Perpendicular
3
Right 17°
372
5 ⫻ 5 ⫻ 5 RGB grid
RGB ramps
Mean
Max
␴
Mean
Max
␴
On
0.70
0.63
0.81
0.76
1.70
1.25
1.65
1.67
0.43
0.33
0.46
0.38
1.02
0.79
1.18
0.91
2.25
1.82
2.63
2.17
0.41
0.32
0.47
0.41
On
Off
On
Off
1.13
1.31
1.21
1.33
3.13
3.48
3.20
3.35
0.69
0.83
0.74
0.76
1.66
1.72
1.70
1.72
3.71
3.66
3.44
3.58
0.74
0.73
0.69
0.73
On
Off
On
Off
1.84
1.94
1.75
1.89
4.84
5.03
4.66
4.98
1.42
1.44
1.36
1.51
3.17
3.26
3.13
3.24
7.18
7.40
7.00
7.20
1.58
1.65
1.58
1.64
COLOR research and application
TABLE XI. CIEDE2000 color differences between the measured and estimated stimuli using the optimization
technique based on perpendicular characterization.
LCD
1
Measurement
Angle
Room
Lighting
Perpendicular
Right 23°
Up 18°
Down 18°
On
Mean
Max
␴
Mean
Max
␴
0.70
5.41
1.31
2.36
1.70
13.67
4.60
6.89
0.43
3.35
0.97
1.63
1.02
5.33
1.97
2.98
2.25
11.87
4.23
6.17
0.41
2.60
0.84
1.32
LCDs 2 and 3 were evaluated with the room lighting both
turned on and off with very similar results, verifying that the
various sources of flare could be cascaded into a single
black-level term, as derived for CRT displays.2,3 In many
viewing environments, ambient illumination is present; the
technique described in this publication is an effective
method to account for its affect on colorimetry.
To test how an observer’s viewing geometry affects the
accuracy of the model estimates, the predictions for the perpendicular measurements of LCD 1 were compared with the
measurements at the other viewing angles. The results are
listed in Table XI. These angles correspond to the viewer
looking straight at the display (perpendicular to the display)
and at the right, top, and bottom edges. The horizontal variability was quite large resulting in average color differences
above 5. The vertical variability was smaller, ranging between
1.21 and 2.98 average ⌬E00. These results indicate that the
LCD should not be used for psychophysical experiments
where the center of the screen will be directly compared with
the edges. However the model would be sufficient to predict
the display output for two positions relative to a fixed viewer
if they were characterized separately. That is, for images displayed side by side using large-diagonal-size displays, typical
of paired-comparison psychophysics, two colorimetric characterizations should be performed.
ACKNOWLEDGMENTS
This research was supported by the National Gallery of Art,
Washington DC, the Museum of Modern Art, New York,
and the Andrew W. Mellon Foundation. This research is
part of a large research effort in spectral imaging, archiving,
and reproduction, documented at www.Art-SI.org (ArtSpectral-Imaging). The authors also acknowledge the assistance of Mahdi Nezamabadi and Mahnaz Mohammadi in
performing the measurements described in the Appendix.
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