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Information Preserving Color Transformation for Protanopia and Deuteranopia


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In this letter, we proposed a new recoloring method for people with protanopic and deuteranopic color deficiencies. We present a color transformation that aims to preserve the color information in the original images while maintaining the recolored images as natural as possible. Two error functions are introduced and combined together to form an objective function using the Lagrange multiplier with a user-specified parameter lambda. This objective function is then minimized to obtain the optimal settings. Experimental results show that the proposed method can yield more comprehensible images for color-deficient viewers while maintaining the naturalness of the recolored images for standard viewers.
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Information Preserving Color Transformation
for Protanopia and Deuteranopia
Jia-Bin Huang, Yu-Cheng Tseng, Se-In Wu, and Sheng-Jyh Wang, Member, IEEE
Abstract—In this letter, we proposed a new recoloring method
for people with protanopic and deuteranopic color deficiencies. We
present a color transformation that aims to preserve the color in-
formation in the original images while maintaining the recolored
images as natural as possible. Two error functions are introduced
and combined together to form an objective function using the La-
grange multiplier with a user-specified parameter
. This objective
function is then minimized to obtain the optimal settings. Experi-
mental results show that the proposed method can yield more com-
prehensible images for color-deficient viewers while maintaining
the naturalness of the recolored images for standard viewers.
Index Terms—Color deficiency, image processing, Lagrange
multiplier, recoloring.
UE to the increasing use of colors in multimedia con-
tents to convey visual information, it becomes more impor-
tant to perceive colors for information interpretation. However,
roughly around 5%–8% of men and 0.8% of women have cer-
tain kinds of color deficiency. Unlike people with normal color
vision, people with color deficiency have difficulties discrimi-
nating certain color combinations and color differences. Hence,
multimedia contents with rich colors, which can be well dis-
criminated by people with normal color vision, may sometimes
cause misunderstanding to people with anomalous color vision.
Humans’ color vision is based on the responses to photons
in three different types of photoreceptors, which are named
“cones” and are contained in the retina of human eyes [1].
The peak sensitivities of these three distinct cones lie in the
long-Wavelength (L), middle-wavelength (M), and short-wave-
length (S) regions of the spectrum. Anomalous trichromacy is
frequently characterized by a shift of one or more cone types
so that the pigments in one type of cone are not sufficiently
distinct from the pigments in others. For example, L-Cones are
more like M-Cones in protanomaly and M-Ccones are more
like L-Cones in deuteranomaly. On the other hand, dichromats
have only two distinct pigments in the cones and entirely lack
one of the three cone types. Lack of L-cones is referred to
as protanopia, lack of M-cones is referred to as deuteranopia,
Manuscript received October 15, 2006; revised February 11, 2007. This work
was supported by the National Science Council of the Republic of China under
Grant NSC-94-2219-E-009-008. The associate editor coordinating the review
of this manuscript and approving it for publication was Dr. Konstantinos N.
The authors are with the Department of Electronics Engineering, National
Chiao Tung University, Hsin-Chu 30050, Taiwan, R.O.C. (e-mail: mysoul-
Color versions of one or more of the figures in this paper are available online
Digital Object Identifier 10.1109/LSP.2007.898333
and lack of S-cones is referred to as tritanopia. Among these
three types of dichromats, protanopia and deuteranopia have
difficulty in distinguishing red from green, while tritanopia
has difficulty in discriminating blue from yellow. So far, many
research works have been conducted on simulating color-defi-
cient vision [2]–[5]. These approaches represent color stimuli
as vectors in the three-dimensional LMS space, where three
orthogonal axes L, M, and S represent the quantum catch for
each of the three distinct cone types. Since the dichromatic
vision is the reduced form of trichromatic vision, the lack of
one cone type can be simulated by collapsing one of the three
dimensions into a constant value.
To enhance the comprehensibility of images for color-defi-
cient viewers, daltonization is proposed in [6] to recolor images
for dichromats. In [6], the authors first increase the red/green
contrast in the image and then use the red/green contrast in-
formation to adjust brightness and blue/yellow contrast. In [7],
et al. described the manipulation of webpage colors for
color-deficient viewers. They first decompose a webpage into a
hierarchy of colored regions and determine “important” pairs of
colors that are to be modified. An objective function is then de-
fined to maintain the distances of these color pairs, as well as to
minimize the extent of color remapping. This approach is fur-
ther extended to deal with full-color images in [8]. On the other
hand, Seuttgi Ymg et al. [9] proposed a method to modify colors
for dichromats and anomalous trichromats. For dichromats, a
monochromatic hue is changed into another hue with less sat-
uration, while for anomalous trichromats, the proposed method
tends to keep the original colors. In [10], Rasche et al. use a
linear transform to convert colors in the CIELAB color space
and enforce proportional color differences during the remap-
ping. Based on the same constraint for color deficiency, the au-
thors further improve the optimization process by using the ma-
jorization method [11].
Basically, all the aforementioned works may generate im-
ages that are more comprehensible to color-deficient viewers.
However, recolored images may look very unnatural to viewers
with normal vision. From an application viewpoint, images in a
public place may be simultaneously observed by normal people
and color-deficient people. For example, in a public transporta-
tion system, many advertisements and traffic maps are delivered
in colors. Without concerning the needs of deficient observers,
color-deficient people may have difficulty in understanding the
image contents. On the contrary, if only concerning the needs
of color-deficient people, then these recolored images may look
annoying to normal observers. Hence, in this letter, we aim to
develop a recoloring algorithm that can automatically construct
a transformation to maintain details for color-deficient viewers
while preserving naturalness for standard viewers.
1070-9908/$25.00 © 2007 IEEE
Fig. 1. Rotation operation in the plane.
A. Color Reproduction Method
In this letter, we focus on protanopia and deuteranopia,
which are the major types of color deciency. In order to
mimic the color perception of protanopia and deuteranopia, we
adopt Brettels algorithm [2] to simulate the perceived images.
Here, we adopt CIELAB color space as the working domain.
In both protanopia and deuteranopia, there is strong correlation
between the original colors and the simulated colors in the
values of
and , while there is a weak correlation between
the original
and the perceived . That is, the original color
information in
gets lost signicantly. To retain the infor-
mation in
, a reasonable way is to do some kind of image
warping so that the information of
is mapped onto the
axis in the CIELAB color space.
In our approach, we aim to maintain the color differences of
color pairs in the CIELAB color space while keeping the recol-
ored images as natural as possible. To keep the recolored image
natural, three premises are adopted. First, the recolored image
has the same luminance as the original image. Second, colors
with the same hue in the original image still have the same hue
after recoloring. Third, the saturation of the original colors is
not altered after recoloring. In our approach, a rotation oper-
ation is adopted in the
plane to transform the informa-
tion of
onto the axis, as illustrated in Fig. 1. Here, we
assume some color stimuli
have the same in-
cluded angle
with respect to the axis. The rotation opera-
tion maps these colors to new colors
, which lay
on another line with the included angle
. If ignoring
the nonlinear property of the iso-hue curves in the CIELAB
color space [13], this rotation process simultaneously changes
the hue of
with the same amount of hue. Hence,
the transformed colors
still share the same hue
after color transformation. Moreover, the saturation of the orig-
inal color
is also preserved.
In mathematics, this rotation operation can be formulated as
a matrix multiplication. That is, we have
and are the CIELAB values of the
recolored color and the original color, respectively.
is a
monotonically decreasing function of
. Since the color differ-
ence along the
axis can be well discriminated by protanopic
Fig. 2. (a) Function
with three parameters: , and . (b) Func-
with parameters
and for a half plane.
and deuteranopic viewers, ) decreases to zero when ap-
. In this letter, we dene to be
for the right half-plane of the
plane, where ranges from
to . Here, represents the maximal change of
the included angle and
represents the degree of the decreasing
rate. These two parameters will be specied by optimizing an
objective function based on the contents of the original color
image. For the left half-plane
,wedene the
function in a similar manner but with different and .
This is because in practice, we may want the right half and the
left half of the
plane to have different transformations, as
shown in Fig. 2(a). Moreover, since
approaches zero when
colors are close to the
axis, crossover of colors can be avoided
when crossing the b
In Fig. 2(b), we show the plot of the transformed hue
versus the original hue for the right-half plane.
If the
is positive, then the quadrant with positive
will be compressed while the quadrant with negative
will be expanded and vice versa. To avoid
colors crossover in the compressed quadrant, we require
By combining (2) and (3), we have
ranges from to , the LHS of (4) has the
lower bound
. Thus, we can obtain the constraint
. On the other hand, the constraint in (4) is not necessary
in the expanded quadrant. Hence, we introduce two parameters
and , one for each quadrant. For the compressed quadrant,
the constraint in (4) is required, while for the expanded region,
no constraint is needed for
and . In the proposed algo-
rithm, there would be six parameters in total. Their notations
and meanings are listed in Table I.
B. Optimization Using Detail and Naturalness Criteria
In this section, we introduce two criteria, one for detail pre-
serving and the other for naturalness preserving. For each color
pair in the original color domain, we rst calculate the perceived
color difference with respect to a person with normal vision.
Then, for the corresponding color pair in the transformed color
domain, we calculate the perceived color difference with re-
spect to a person with protanopic or deuteranopic deciencies.
As mentioned above, we follow Brettels algorithm [2] to simu-
late the color perception for protanopia and deuteranopia. In our
criterion, we wish these two perceived color differences to be as
similar as possible. Hence, we dene an error function to be
and range over the colors contained in the images,
is a perceptual color difference metric, is our recoloring
function, and
denotes the simulated color perception
using Brettles algorithm. By minimizing this error function, we
can preserve color details of the original image.
On the other hand, we attempt not to dramatically modify the
color perception of the color images since a severe modication
may make the recolored image extremely unnatural for normal
viewers. Hence, we dene another error function to be
ranges over all the colors in the original color image.
Minimizing this error function shortens the color distance
between the original colors and the corresponding remapped
colors. To preserve both details and naturalness, we combine
these two error functions using the Lagrange multiplier with a
user-specied parameter
. Here, we further normalize these
two error functions by their arithmetic means to achieve similar
order of magnitude. That is, the total error is written as
To minimize the objective function in (7), we roughly
and in the initialization stage
, and xed to 1. Then we use the
FletcherReeves conjugate-gradient method with the constraint
in (4) to obtain the optimal solution. By choosing different
values of
, users may adjust the tradeoff between details and
naturalness. A larger
makes the recolored image more natural
Fig. 3. (a) Original image. (b) Recolored by the Daltonization method with a
middle-level correction [6]. (c) Recolored by Rasches method [10]. (d) Recol-
ored by our proposed method with
. (e) Recolored by our proposed
method with
. (f)(j): Corresponding color images perceived by people
with deuteranopic color deciency.
for normal viewers, while a smaller
makes the recolored
image more comprehensible for color-decient viewers.
One more thing to mention is about the nonlinear property
of the iso-hue curves in the CIELAB color space [13]. That is,
two colors with the same included angle
in the plane
may not have the same value of hue. Due to this nonlinear prop-
erty, colors with the same hue in the original image may gen-
erate colors with different hues in the recolored image. To solve
this problem, we may simply apply the hue-linearization process
mentioned in [14] as a preprocessing and then apply the delin-
earization process after the recoloring algorithm.
In Fig. 3, we demonstrate some experimental results for the
“flower image. Fig. 3(a)(e) shows the images perceived by
normal viewers, while Fig. 3(f)(j) presents the images per-
ceived by viewers with deuteranopic deciency. We can ob-
serve that the color contrast between the red ower and the
green leaves is lost for people with deuteranopic deciency.
We compare our method with the Daltonization method [6] and
Rasches method [10], as shown in Fig. 3(b)(e) and (g)(j). We
may observe that even though the Daltonization method with
a middle-level correction may also preserve the naturalness of
the recolored image for normal people, the contrast between the
ower and leaves looks very poor for deuteranopic people. On
the other hand, even though Rasches method may create great
contrast for deuteranopic people, the naturalness of the recol-
ored image is extremely poor for people with normal vision. In
comparison, our method may well preserve both details and nat-
uralness at the same time.
To verify the effect of
, we also demonstrate in Fig. 3(e) that
our proposed method will produce an extremely unnatural re-
colored image if
. Furthermore, in Table II, we compare
the naturalness error and detail error among different methods,
based on (5) and (6). In our approach, the naturalness error de-
creases while detail error increases when
rises. For the Dal-
tonization method, even though its naturalness error is less than
ours, its detail error becomes extremely high. On the other hand,
even though Rasches method has a smaller detail error, its natu-
ralness error is larger. These experimental results show that both
naturalness and detail can be properly preserved by our method.
Fig. 4. (a) Original image. (b) Perceived image by protanopic viewer. (c) Per-
ceived image by deuteranopic viewer. (d) Recolored image for protanopia. (e)
Perceived image of (d) by protanopic viewers. (f) Recolored image for deuteran-
gopia. (g) Perceived image of (f) by deuteranopic viewers.
In Fig. 4, we show more examples to verify the effectiveness of
the proposed method.
We also used Thurstones Law of Comparative Judgment [12]
for subjective evaluation. In our subjective experiments, ten par-
ticipants with normal vision were involved and six represen-
tative color images were chosen, as shown in Fig. 5. All ten
participants were graduate students with some background in
video coding and image processing. Since we have difculty in
nding color-decient viewers, we adopted Brettels algorithm
[2] to mimic the perception of protanopia and deuteranopia. In
the rst experiment, each of the six images was, respectively, re-
colored by the Daltonization method, Rasches method, and our
method with
. For each image, the original image was
rst shown to the participants. Then, exhaustive paired compar-
isons were performed over the recolored images, and the partic-
ipants were asked to choose the more natural image from each
pair. This experiment is to evaluate the naturalness of the re-
colored images from the viewpoint of normal viewers. In the
second experiment, Brettels algorithm was applied over the
original images and recolored images to simulate the perceived
images for deuteranopia. Exhaustive paired comparisons were
performed again over the simulated images, and the participants
were asked to choose the more comprehensible image from each
pair. This experiment is to evaluate the comprehensibility of the
recolored images from the viewpoint of deuteranopic viewers.
The results of these two subjective experiments were analyzed
based on Thurstones Law of Comparative Judgment [12]. The
scaling of data is shown in Fig. 6. Fig. 7(a) indicates that both
our method and the Daltonization method produce more natural
images, while Fig. 7(b) indicates that our method may preserve
more details than the other two methods.
Fig. 5. Six images for the subjective evaluation.
Fig. 6. Experimental results. (a) Scales from the naturalnessexperiment. (b)
Scales from the comprehensibility experiment.
We have presented in this letter a new recoloring method for
people with protanopic or deuteranopic deciency. We propose
a color transformation that can yield more comprehensible im-
ages for protanopic or deuteranopic viewers while maintaining
the naturalness of the recolored images for standard viewers.
The same procedure can be extended to the case of tritanopia,
in which blue and yellow tones cannot be well distinguished.
The experimental results show that our proposed method per-
forms subjectively better than others, in terms of comprehensi-
bility and naturalness.
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... Chromatic difference: in [12,16,18,[24][25][26]28], chromatic difference (CD) metric in the CIE L*a*b* color space was introduced, which can be computed as: ...
... We classify the methods in [25,27,[38][39][40][41][42][43][44][45][46] into a category named "hue rotation" (HR). For these methods, rotation ΔH by an angle is applied to the hue of the original image. ...
... We classify the methods in [16-18, 22, 23, 25, 31, 32, 41, 43, 45-53] into a category named "optimization" (Opt). For these methods, except for Huang et al. [25], key colors are firstly extracted from the original image using image quantization, clustering, color sampling, and so forth. Then, objective functions are used to find optimal target colors for the extracted key colors or optimal mapping applied to the whole image. ...
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... So far, a wide range of recoloring methods have been developed based on optimization of specially designed objective functions [11][12][13][14][15][16] or regularized objective functions [17][18][19] that uniformly combine the naturalness and contrast criteria, pixel-based classification [20], spectral filtering [21], cluster analysis [22], gradient domain recoloring [23,24], confusion-line based [7,8,25], color transformation and rotation/translation [26][27][28][29], neural networks [30], image retrieval [31], and deep learning [32]. ...
... In particular, they showed that the dichromat color gamut is a plane in the three-dimensional RGB space. Having generated the dichromat simulation of an image using the above method, recoloring processes can be applied to carry out color adaptation of the image in order to enhance color appearance, color discrimination, and object recognition for the color-blind [10,13,17]. ...
... While the method manages to increase the contrast, it finally changes the colors significantly due to the rotation process and the naturalness deteriorates. In [17] the CIELab color space was used to perform image recoloring. Confusing colors were rotated in the ab plane. ...
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... Several image recoloring methods [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21] have been proposed to compensate for the loss in the ability to discriminate colors. In addition to contrast, the importance of naturalness preservation has also been raised in state-of-the-art image recoloring studies [9], [10], [11], [12], [13], [14], [15], [16], [17]. Since CVD is congenital, people with CVD are accustomed to their own perception of the colored world. ...
... For naturalness preservation, some approaches [9], [10], [11], [12], [13], [14], [15] attempted to minimize deviation from the original image. Hassan et al. [9], [10] regarded the difference between the original image and its CVD simulation as a perception error and compensated for it by increasing the blue channel according to the amount of the error. ...
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... Other authors have compared the compensated images with the state-of-the-art and checked the effectiveness in terms of contrast, clarity and computation time [36][37][38][39][40]. A couple of authors have used Thurstone's Law of Comparative Judgment for seeking subjective feedback from the colour blind on the compensated images [41][42]. In 2017, Simon-Liedtke et al. provided guidelines for the design and evaluation of algorithms using daltonization method for colour compensation [43]. ...
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Eyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male population and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advancement in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defining the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be useful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients. © 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
Colour vision deficiency is a common visual impairment that cannot be compensated for using optical lenses in traditional glasses, and currently remains untreatable. In our work, we report on research on Computational Glasses for compensating colour vision deficiency. While existing research only showed corrected images within the periphery or as an indirect aid, Computational Glasses build on modified standard optical see-through head-mounted displays and directly modulate the user’s vision, consequently adapting their perception of colours. In this work, we present an exhaustive literature review of colour vision deficiency compensation and subsequent findings; several prototypes with varying advantages—from well-controlled bench prototypes to less controlled but higher application portable prototypes; and a series of studies evaluating our approach starting with proving its efficacy, comparing to the state-of-the-art, and extending beyond static lab prototypes looking at real world applicability. Finally, we evaluated directions for future compensation methods for computational glasses.
Dichromats recognize colors using two out of three cone cells; L, M, and S. To extend the ability of dichromats to recognize the color difference, we propose a method to expand the color difference when observed by dichromats. We analyze the color between the neighboring pixels not in intensity space but chromaticity space and form a Poisson equation. In addition, we use the sigmoid function to weigh the edge of a color image. The color difference can be adequately tuned manually by the weight parameter so that the dichromats can obtain the image that they want where the visibility of the color is enhanced.
The purposes of the study were 1) to design and develop the 3D role-playing game on PC with an assistive system for green vision impaired people, 2) to assess the quality of the game created by the experts, and 3) to evaluate the satisfaction of the samples towards the game. This game was qualitatively evaluated by three experts in gaming development through a purposive sampling. Thirty participants were recruited via accidental sampling method to evaluate a satisfaction towards the game. The quality evaluation results was found to be at a satisfactory level (mean = 3.90/SD = 0.12). The participants rated their satisfaction towards the game at the very satisfactory level (mean = 3.64/SD = 0.05). It can be summarized that the 3D role-playing game on PC with an assistive system for green vision impaired people is appropriate to be used for the visually impaired. The assistive system can also be applied to further develop games for the vision-impaired people. and relaxing users, which can be compared to being on a vacation.
Gamut mapping is an important part of the color reproduction pipeline. A color's appearance depends on the gamut achievable by the reproduction device (e.g., display, printer, etc.) or the reproduction material (e.g., plastics, paints, textiles, etc.). In the surface color industry, often a single color is managed such that, if it lies outside of the reproduction gamut, it would be mapped to a visually similar color on the boundary of the reproduction gamut using a gamut mapping algorithm. The algorithm's performance mainly depends on the uniformity of the working color space and/or selection of a focal point, inside the reproduction gamut, towards which the mapping line should be directed. Hitherto, the CIE standard color difference formula CIEDE2000 is the best known perceptual color difference metric for the standard dynamic range. In this paper, a method is proposed with the aim to achieve perceptually uniform mapping of a source color to the reproduction gamut using the CIEDE2000 as reference for uniformity. The proposed method, named UNIMAP00, is independent of the uniformity of the working color space, and no focal points are needed. The current results closely agreed with the experimental findings previously reported by other researchers.
Conference Paper
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In this paper, we propose a color modification scheme for web-pages described by HTML markup language in order to realize barrier-free color vision on the internet. First, we present an abstracted image model, which describes a color image as a combination of several regions divided with color information, and define some mutual color relations between regions. Next, based on fundamental research on the anomalous color vision, we design some fitness functions to modify colors in a web-page properly and effectively. Then we solve the color modifi- cation problem, which contains complex mutual color relations, by using Genetic Algorithm. Experimental results verify that the proposed scheme can make the colors in a web-page more recognizable for anomalous vi- sion users through not only computer simulation but also psychological experiments with them.
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An effective gray-scale conversion method should preserve the detail in the original color image. The fundamental premise is that this is best achieved by a perceptual match of relative color differences between the color and the gray-scale images. Specifically, the perceived color difference between any pair of colors should be proportional to their perceived gray difference. A new approach to the gray-scale conversion problem that is built on this premise is proposed. The method automatically constructs a linear mapping that depends on the characteristics of the input image and incorporates information from all three dimensions of the color space.
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Color is a powerful medium for coding, structuring and emphasizing visual information and, as such, used in many computer applications. However, this tool is less effective, or even counterproductive, in the case of people with impaired color vision. This problem can be remedied to a reasonable extent, provided the display designer is able to anticipate the chromatic trouble spots of a particular color palette. For that purpose, a color editor was designed that allows an image to be displayed as if viewed through the eyes of a color-deficient observer. The model used for computing the color transformations, makes use of state-of-the-art knowledge concerning the polymorphism of human cone pigment and the spectral filtering of the eye lens and macular pigment. As a result, the color editor not only enables the emulation of dichromatic color vision, but also of anomalous trichromatism, the more complex, but also more frequently occurring form of deficient color vision (75% of the colorblind population). In addition to its use as a diagnostic design tool, the editor also provides the means for adjusting the color look-up table to the individual needs of a color-deficient display user.
Color images have a gamut that typically spans three dimensions. Nevertheless, several important applications, such as the creation of grayscale images for printing and the re-coloring of images for color-deficient viewers, require a reduction of gamut dimension. This paper describes a technique for preserving visual detail while reducing gamut dimension. The technique is derived by focusing on the problem of converting color images to grayscale. A straightforward extension is then provided that allows re-coloring images for color-deficient viewers. Care is taken so that the resulting images remain within the available gamut and visual artifacts are not introduced.
We propose an algorithm that transforms a digitized color image so as to simulate for normal observers the appearance of the image for people who have dichromatic forms of color blindness. The dichromat's color confusions are deduced from colorimetry, and the residual hues in the transformed image are derived from the reports of unilateral dichromats described in the literature. We represent color stimuli as vectors in a three-dimensional LMS space, and the simulation algorithm is expressed in terms of transformations of this space. The algorithm replaces each stimulus by its projection onto a reduced stimulus surface. This surface is defined by a neutral axis and by the LMS locations of those monochromatic stimuli that are perceived as the same hue by normal trichromats and a given type of dichromat. These monochromatic stimuli were a yellow of 575 nm and a blue of 475 nm for the protan and deutan simulations, and a red of 660 nm and a blue-green of 485 nm for the tritan simulation. The operation of the algorithm is demonstrated with a mosaic of square color patches. A protanope and a deuteranope accepted the match between the original and the appropriate image, confirming that the reduction is colorimetrically accurate. Although we can never be certain of another's sensations, the simulation provides a means of quantifying and illustrating the residual color information available to dichromats in any digitized image.
The normal X-chromosome-linked color vision gene array is composed of a single red pigment gene followed by one or more green pigment genes. The high degree of homology between these genes predisposed them to unequal recombination, leading to gene deletions or the formation of red-green hybrid genes that explain the majority of the common red-green color vision deficiencies. Gene expression studies suggest that only the two most proximal genes of the array are expressed in the retina. The severity of the color vision defect is roughly related to the difference in absorption maxima of the photopigments encoded by the first two genes of the array. A single amino acid polymorphism (Ser180Ala) in the red pigment accounts for the subtle difference in normal color vision and influences the severity of color vision deficiency. Blue cone monochromacy is a rare disorder that involves absence of red and green cone function. It is caused either by deletion of a critical region that regulates expression of the red/green gene array, or by mutations that inactivate the red and green pigment genes. Total color blindness is another rare disease that involves complete absence of all cone function. A number of mutations in the genes encoding the cone-specific alpha- and beta-subunits of the cation channel and the alpha-subunit of transducin have been implicated in this disorder.
Conference Paper
We propose a color modification scheme for still images in order to realize barrier-free color vision in the IT society. Based on the knowledge of Kondo's anomalous color vision model, we quantify the degree of color discrimination among colors in a given image by the anomalous vision people, and modify the pixel colors to improve the discrimination by them while keeping naturalness of the image.
Conference Paper
In this paper, we propose methods to adapt colors on the visual content for people with color vision deficiency. The proposed adaptation consists of two parts: adaptations for dichromat and anomalous trichromat. The adaptation for dichromats aims to give them better color information, while the adaptation for anomalous trichromats aims to give them original color. To verify the proposed methods, we used both quantitative and qualitative measurements. Experimental results showed that the proposed adaptation enhanced color information readability of the people with color vision deficiency.