# Color Image Quality in Presentation Software

Authors: Edison Valencia; María S. Millán
Journal: Advances in Optical Technologies
 (2008)

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## Abstract

The color image quality of presentation programs is evaluated and measured using S-CIELAB and CIEDE2000 color difference formulae. A color digital image in its original format is compared with the same image already imported by the program and introduced as a part of a slide. Two widely used presentation programs—Microsoft PowerPoint 2004 for Mac and Apple's Keynote 3.0.2—are evaluated in this work.


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## Body

## 1. Introduction

In the last two
decades, we have seen the development of new communication tools capable of
transmitting diverse types of information—numeric, textual, graphics,
pictures, movies—and increasing their impact. Existing and emerging technologies
have refined and integrated in computer software packages used to display
information in a variety of presentations, including education, professional,
and for general communication, normally in the form of a slide show.
Presentation programs have actually replaced older visual aid technology, such
as chalkboards, slides, and overhead transparencies, though the
“slide” analogy is still a reference to the nowadays rather obsolete
slide projector. Slides can be printed, but more usually, displayed onscreen
and navigated through at the command of the presenter.VCN ExecuVision,
created by Visual Communications Network, Inc. in 1982, was the first
presentation program for personal computers. The program had a complementary
image library that originated the concept of clip art (predesigned images). The
benefits and potential utilities of this sort of software programs were rapidly
reported by Toong and Gupta [1]. Nowadays, Microsoft PowerPoint and Apple's
Keynote are two commonly known and widely used presentation programs. Most of
current presentation programs have the ability to import graphic images and
color pictures from digital photo archives or image banks. Concerning the
quality of displayed color images, much more investigation has been reported on
the influence of the projector or other display devices [2, 3] than on the
influence of the presentation program (software). However, both influences are
important. For those users not very experienced in color science, it may be
useful to know that not only does the display device play a role, but also the
presentation software. Presentation of color images are widely used in many
fields of science and education. In some of them (e.g., presentation of
clinical photography in medical lecturing), we have noticed that a good quality
of color image display is essential.In this work, we
focus on the color image quality of the digital image imported and processed by
a presentation program to be a part of a slide. To this end, we compare a
digital image in its original format with the same image already imported by
the program and introduced as a part of a slide. The effects of the display
device, usually a video projector, are not considered here, but just the
effects of the presentation program on the color content of the spatially
variant signals. These effects can be evaluated through the measurement of the
color reproduction error existing between the original digital color image and
the digital color image contained in the slide created by the presentation program
of concern.The CIELAB system
(CIE 1978) is an important international standard for measuring color
reproduction errors between large uniform patches. However, when it is used to
determine the color difference between images on a pixel-by-pixel basis, it tends to produce larger errors at most image
points than the perceived ones. For this reason, Zhang and Wandell proposed a
spatial extension to the CIELAB color metric, known as the S-CIELAB metric [4], that
can be applied to complex stimuli such as digital images when they are viewed
at different distances. They use a series of spatial filters in the opponent
color space AC1C2, containing one luminance channel (A)
and two chrominance channels (C1, C2). The filters are
smoothing filters consisting of a linear combination of exponential function
masks that approximate the contrast sensitivity functions of the human vision
system for a given viewing distance. The filtered image is then back
transformed to the CIELAB representation. S-CIELAB allows one to measure the
perceived color differences by applying the standard CIELAB formula Delta E to
the filtered images pixel-by-pixel. S-CIELAB has been used to measure color
reproduction errors in images [4], to predict texture visibility of printed
halftone patterns [5], to evaluate the effects of image compression [4],
to segment color images [6], and to sharpen color images in combination with a
second derivative operator [7]. S-CIELAB can be implemented in both the spatial
and the frequency domains [8]. The CIEDE2000 color difference formula [9] ΔE00 combined with S-CIELAB has been compared with
other existing CIE color difference formula (ΔEab*, ΔE94*, and ΔE00) and three different viewing conditions in [8].
The authors proved that CIEDE2000 (ΔE00) tends to produce color difference images
(called error image) with the smallest mean and standard deviation when evaluating
the color difference of halftone image pairs [8]. In this work, we also use
S-CIELAB and CIEDE2000 color difference formulae to measure the color
reproduction error between the original digital color image and the digital
color image imported by a presentation program to build one of its slides. Two
presentation programs are to be analyzed in this work: Microsoft PowerPoint
2004 for Mac and Apple's Keynote 3.0.2.


## 2. Measuring Color Reproduction Errors  of Digital Images

With S-CIELAB [4], Zhang and Wandell
provided a metric to determine the perceived color differences between image
pairs. To implement S-CIELAB, a sequence of steps has to be followed. First of
all, it is necessary to transform the input images into a device-independent
color space, such as CIE 1931 XYZ. In the following, we assume that the input
images of a given pair are expressed in the standard color space sRGB [10] and
are then transformed into CIE XYZ. The second step involves a spatial filtering
of the images that is performed in an opponent color space consisting of one
luminance channel (A) and two chrominance channels (C1, C2).
The transformation and spatial filters used in S-CIELAB have been estimated
from human psychophysical measurements of color appearance [11].The opponent channelsAC1C2 are linear transform from CIE
1931 XYZ as shown by(1)[AC1C2]=[0.2970.72−0.107−0.4490.29−0.0770.086−0.590.501][XYZ].Since the threeAC1C2 channels are not completely orthogonal, some color fringes may appear
after spatially filtering the image components with different size filters in
each channel. These artifacts could be generated when rendering images.
However, they were analyzed in applications such as color image sharpening and
found of very low significance [7]. Moreover, the effects caused by the lack of
orthogonality are not relevant when calculating color differences for the
number of applications described in [4–6].Once two imagesI,I′ are transformed into the opponent color space,
they are spatially filtered using filters that approximate the contrast
sensitivity functions of the human visual system. In this work, we will carry
out this filtering via convolution in the spatial domain. In each opponent
channel, the filter is a linear combination of weighted exponential functions
and its kernel sums to one. Thus the three filters preserve the mean color
value for large uniform areas and S-CIELAB and CIELAB give similar predictions
for them. The kernel of each spatial filter Fdi is given by(2)Fdi(x,y)=∑jwijGij(x,y,dσij2), where i={0,1,2} indicates the opponent channel {A,C1,C2}, wij is the weight, and Gij is the normalized kernel of a function
described by the expression(3)G(x,y,s)=1Sexp[−(x2+y2)2s2].In (3), s is
the spread and S is a constant that normalizes the kernel of the function
so that it sums to one. In (2), the spread of the exponential functions is sij=dσij/2 and it represents the decreasing in
sensitivity that occurs in the human vision system when the viewing distance
increases. This blurring effect is represented by the product of the spread
expressed in degrees of visual angle (σij) times the number of pixels per degree of
visual angle (d) when the observer is placed at a given distance from
the monitor. Table 1 shows the values of weights wij,
which are already adjusted to sum to one [8], and the values of spreads used in
S-CIELAB [4].Table 1
Weight and spread of kernels used to build the spatial filters for image convolution in the opponent channels.
ijwσ0 (A)11.003270.050020.114420.22503−0.117697.00001(C1)10.616730.068520.383280.82602(C2)10.567890.092020.432120.6451Let us denoteIdi,Idi′ the components of the spatially filtered
images in the opponent color space that are produced from the convolution of
the spatial filters Fdi with the input image components Ii,Ii′:(4)Idi(x,y)=Fdi(x,y)∗Ii(x,y),Idi′(x,y)=Fdi(x,y)∗Ii′(x,y), where symbol * is the convolution
operation. The 2-D convolution in the spatial domain of (4) can be more
efficiently computed as two 1-D convolutions taking into account that the
kernels are separable.The filtered
components in the opponent channelsIdi,Idi′ (4) are then transformed back into CIE XYZ
space using the linear transformation(5)[XYZ]=[0.979−1.5350.4451.1890.7640.1351.2321.1632.079][AC1C2], which is the inverse of (1). The
filtered images Id(XYZ),Id(XYZ)′ are transformed into the CIELAB space using
standard equations [10], for which the tristimulus values of the
white point of the display device Xn,Yn,Zn have to be known through the device
characterization (sRGB monitors have D65 white point). Once the CIELAB
coordinates are calculated for all the pixels, color differences between
filtered images Id(CIELAB),Id(CIELAB)′ can be computed on a pixel-by-pixel basis. The
result is a color difference image where each pixel value represents the
perceived color difference at that given point. The standard CIE ΔEab* color difference equation [12] has
been traditionally used with S-CIELAB. Since CIEDE2000 (ΔE00) tends to produce color difference images (or
error image) with the smallest mean and standard deviation among a set of three
other CIE color difference formulae (ΔEab*, ΔE94*,
and ΔE00) [8], we will also use CIEDE2000 color difference
formula to compare the image pairs of this work.


## 3. Experiment and Results

Figure1 shows the test image (Printer
evaluation target for sRGB, in .TIFF format) I(x,y) of 600×430 pixels size. This image is
imported by the presentation programs Microsoft PowerPoint 2004 for Mac and
Apple's Keynote 3.0.2 to provide Ippt(x,y) and Ikey(x,y), respectively. These presentation programs are commanded to read the single
image file and give it the same configuration of size, scale, and other common
characteristics. In both cases, the image under evaluation is obtained after
activating the option “present” (display) of the software program and then it
is saved using screenshot (through the Grab version 1.3 for Mac Os X-Panther)
in the same conditions. Working in this way, we ensure that the differences
between images are only due to the characteristics of the presentation programs
under comparison.Figure 1
(Color online) original test image,
printer evaluation target for sRGB, in TIFF formatI(x,y) of 600×430 pixel size.Let us consider that
these images are to be displayed on a CRT monitor that conforms to sRGB and is
controlled by computer. The sRGB color space has been characterized by the
International Electrotechnical Commission (IEC) [10]. Thus we consider that the
original image is an sRGB color image, that is, it was created using devices
that conform to sRGB, and the monitor is sRGB compliant, and is associated with
an appropriate color profile. In consequence, the resulting processed images
will be consistent across devices and we will be able to compare them with the
original in appearance. The formulae to convert between sRGB and XYZ
tristimulus values for D65 white point are the following (also available on the
Internet [10]):(6)(XYZ)=(0.41240.35760.18050.21260.71520.07220.01930.11920.9505)⋅(RGB),(RGB)=(3.2406−1.5372−0.4986−0.96891.87580.04150.0557−0.20401.0570)⋅(XYZ).Let us consider that
the monitor is capable of displayingp pixels-per-cm (ppc) and it is viewed at L cm. The number of pixels-per-degree of visual angle is then(7)d=pLtan(π180)=0.0175pL.In our case, we
consider that the image is displayed on the monitor with 72 dpi resolution (p=28.35 ppc) and is to be observed at
two different distances L={25,50} cm
so that, according to (7), the pixels-per-degree of visual angle are d={12.4,24.8}.The CIEDE2000 color
differences in the S-CIELAB metric between the original image of the test and
the image imported by Microsoft PowerPointΔE00SCIELAB(I,Ippt) are shown in grayscale in Figure 2(a) for the viewing condition of d=12.4 pixels/degree (L=25 cm). The result corresponding to
Apple's Keynote ΔE00SCIELAB(I,Ikey) for the same viewing condition is shown in
Figure 2(b). The color
difference has been represented with the same scale in Figures 2(a) and 2(b).
Black areas indicate the minimum color difference and white areas indicate the
maximum color differences in CIEDE2000 units. For a better visualization of the
magnitude of color differences in both figures, pseudocolored versions of Figures
2(a) and 2(b) are also provided in Figures 2(c) and 2(d), respectively. Table 2
contains the statistics (mean, std) of both figures. From these results,
it can be clearly seen that the program Microsoft PowerPoint gives better
results than Apple's Keynote.Table 2
Statistics (mean, std) of the color difference distributions of Figures2 and 3.
<mean><std>CIEDE2000 unitsCIEDE2000 unitsΔE00SCIELAB(I,Ippt)d=12.40.73842.5831ΔE00SCIELAB(I,Ikey)d=12.43.90128.7121ΔE00SCIELAB(I,Ippt)d=24.80.39591.2023ΔE00SCIELAB(I,Ikey)d=24.83.58818.1024CIEDE2000 color differences in the
S-CIELAB metric between the original image of the test and the image imported
by: (a), (c) Microsoft PowerPointΔE00SCIELAB(I,Ippt), and (b), (d) Apple's
Keynote ΔE00SCIELAB(I,Ikey) for the viewing
condition of d=12.4 pixels/degree.
(a)
ΔE00SCIELAB(I,Ippt)-grayscale(b)
ΔE00SCIELAB(I,Ikey)-grayscale(c)
ΔE00SCIELAB(I,Ippt)-pseudocolor(d)
ΔE00SCIELAB(I,Ikey)-pseudocolorThe results Figure3
have been obtained for a viewing conditions of d=24.8 pixels/degree (L=50 cm). The statistics of the color
difference distributions of Figure 3 are also contained in Table 2. Again, for
this viewing condition, the better quality of Microsoft PowerPoint is obtained.
The results of Figure 3 are very close to those of Figure 2, except for some
small regions for which the blurring of a longer viewing distance also smoothes
the local color differences.(a), (c)ΔE00SCIELAB(I,Ippt),
and (b), (d) ΔE00SCIELAB(I,Ikey) for
the viewing
condition of d=24.8 pixels/degree.
(a)
ΔE00SCIELAB(I,Ippt)-grayscale(b)
ΔE00SCIELAB(I,Ikey)-grayscale(c)
ΔE00SCIELAB(I,Ippt)-pseudocolor(d)
ΔE00SCIELAB(I,Ikey)-pseudocolorThe regions of the image with largest
color errors appear enhanced and segmented in Figure4 for both presentation
programs and both viewing conditions. The pixels with color differences less
than 1% appear darkened. Microsoft
PowerPoint has few and very small areas of color difference greater than
1% segmented in Figures 4(a), 4(c). This is not the case of Apple's Keynote, for
which orange, blue, and magenta are the biggest problems as it can be seen in
Figures 4(b), 4(d).Figure 4
Test image with regions of largest
color error enhanced and segmented. Pixels with color differences less than
1% appear darkened. (a) and (c) correspond to the result obtained for
Microsoft PowerPoint in Figures2 and 3. (b) and (d) correspond to the result
obtained for Apple's Keynote in Figures 2 and 3.


## 4. Conclusions

The color image quality of the digital
image imported and processed by presentation programs can be evaluated and
measured using S-CIELAB and CIEDE2000 color difference formulae. We have
compared a digital image in its original format with the same image already
imported by the program and introduced as a part of a slide. Two widely used
presentation programs have been evaluated in this work: Microsoft PowerPoint
2004 for Mac and Apple's Keynote 3.0.2. From the results obtained in our
numerical experiment, the presentation program of Microsoft PowerPoint shows
lower color reproduction error and, therefore, better color image fidelity than
Apple's Keynote.



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