ISSN 2278-3091
Volume 9, No.4, July – August 2020

Anastasios Rigos et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 5015 – 5025

International Journal of Advanced Trends in Computer Science and Engineering
Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse119942020.pdf
https://doi.org/10.30534/ijatcse/2020/119942020

A Systematic Methodology to Modify Color Images for
Dichromatic Human Color Vision and its Application in
Art Paintings
Anastasios Rigos1, Stamatis Chatzistamatis2, George E. Tsekouras3*
Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
1a.rigos@aegean.gr, 2stami@aegean.gr 3gtsek@ct.aegean.gr*
corresponding author
are subjected to various challenges in their everyday life, e.g.
road signs and traffic light recognition, object recognition inimages, consumption of colored multimedia content, etc.
Since the 8% of men and 0.8% of women are color-blind, it isclearly understood that any computational framework thatassists the color blindness would be of great importance.



ABSTRACTThis paper introduces a systematic methodology to perform
image recoloring for color-blind people that suffer fromdichromacy, i.e. protanopia, deuteranopia, and tritanopia.
The method is applied in digitized art paintings to alleviate
accessibility problems related to art content consumption by
the color-blind people, which is a very important issue. The
methodology involves the RGB, the LMS, and the CIELabcolor spaces and comprises several steps implemented insequence. To reduce the computational complexity, the colorsof the original image are clustered. Each cluster center is acolor. By using a standard technique, the cluster centers aretransformed to simulate the effects of protanopia,
deuteranopia, and tritanopia. To this end, a specialized
objective function is minimized to recolor only the cluster
centers that are significantly different from the respective
simulated ones, because only these colors are confused by thecolor-blind. Finally, all colors of the original image belonging
to the clusters associated with centers that have beenrecolored are appropriately recolored, also. The effectivenessof the proposed method is quantified in terms of comparative
analysis over several experimental cases.

1.00

Spectral Sensitivity

L-cones
0.80

M-cones
S-cones

0.60
0.40
0.20
0.00
390

465

540

615

690

765

nm

Figure 1: The cones’ spectral sensitivities as functions of the
wavelength (in nm) [1].

Depending on the cone types that have been affected, there
exist three general classes of CVDs namely, monochromacy(known as achromatopsia), dichromacy, and anomalous
trichromacy [1, 3]. Monochromacy (i.e. total color blindness)is the most severe CVD concerning the absence of two or
three types of cones, and it is the rarest one. Dichromacy
concerns the absence of one type of cones and includes three
categories of CVD: (a) the protanopia caused by the absenceof the L-cones, (b) the deuteranopia caused by the absence ofM-cones, and the tritanopia where the S-cones are missing.
Anomalous trichromacy includes three categories namely,
protanomaly, deuteranomaly and tritanomaly caused bymalfunctioning of the L, M, and S cones, respectively.
Color image analysis has been effectively employed inassisting visually impaired people [6]. In particular, the main
strategies to alleviate the CVD effects concern the recoloringof the original image under certain requirements such asimage naturalness and color contrast enhancement [3]. Image
naturalness focuses on minimizing the perceptual difference
between the original and the recolored image. On the other
hand, enhancing the color contrast would be very convenientin supporting the object recognition performed by the color
blind.

Key words: Color-blind, dichromacy, image recoloring,objective function, digitized art paintings.
1. INTRODUCTION
The human eye perceives the color using specialized
photoreceptor cells, called “cones”, contained in the eye’s
retina. There are three types of cones namely, the L-cones, theM-cones, and the S-cones. These cone types are sensitive todifferent, but overlapping, regions of the visible spectrum [1].
As an approximation of their stimulation, the L-cones
correspond to red color, the M-cones to green color, and theS-cones to blue color [2, 3, 4]. Figure 1 depicts the spectral
sensitivity distributions for each cone type as reported in [1].
Dysfunction or absence of two or more types of cones
results in color vision deficiency (CVD), also known as color
blindness (CB) [1, 3, 5]. People suffering from strong CVDs

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Huang et al [4] used the CIELab color space and developed
a rotation mechanism to transform the original color in therecolored one. To accomplish this, they designed an objectivefunction, which quantified the naturalness and the contrast
requirements. In this direction, Jeong et al [5] substantially
improved the previous recoloring process by performing atranslation of the colors not perceived correctly by thecolor-blind in distinguishable RGB color regions. Hassan andParamersan [7] used the XYZ color space to carry out image
enhancement in three steps namely, normalization, angularcolor rotation, and color un-normalization. In [8], an
optimization algorithm was proposed that was able topreserve the distances between the original colors and thecorresponding colors as seen by the color blind. Huang et al[9] performed the color enhancement by extracting key colors
and determining an optimal mapping to maintain the contrast
between pairs of those colors. Tsekouras et al [10] used
evolutionary computation to elaborate on the set of the
distinct image colors, which were modified by a daltonization
approach to generate the recolored image. In [11], a semantic
segmentation procedure was developed to identify semantic
image information. Then, the recoloring process was based
exclusively on that information. Lin et al [12] obtained therecolored image by developing a robust color separation
algorithm to perform eigenvector processing in an opponent
color space. Finally, Wong and Bishop [13] proposed anadaptive technique, which was based on non-linear hue
remapping able to preserve the image aesthetics.
Although too much effort has been put in developing
natural image recoloring [4, 5, 7, 8, 9, 12, 13], there are
relatively few methods that concern art images [10, 11]. Dueto the complexity of such kind of images, maintaining a color
natural appearance of the recolored image is a very
challenging problem [11]. However, the accessibility ofcolor-blind people in cultural content, such as art paintings,
has been acknowledged as an important demand by
worldwide organizations dealing with the CVDs and culturalorganizations such as museums [14, 15, 16]. Manycolor-blind people are at a disadvantage when choosing tostudy or enjoy art paintings because they can only discern a
confusing set of objects and colors.
In this paper we propose a systematic methodology to
produce improved recolored images that maintain thenaturalness and contrast enhancement requirements for thetest case of digitized art paintings. In a nutshell, colors thatare not perceived correctly by the color-blind are detected andelaborated. Then, a specialized objective function isminimized to modify the above-mentioned colors so that they
can be correctly perceived, and the objects contained in theimage can be easily recognized.
The rest of the paper is organized as follows. Section 2
analytically describes the proposed methodology. In Section
3, the simulation experiments are presented and discussed.
Finally, the paper concludes in Section 4.

2. THE PROPOSED RECOLORING METHOD
The proposed methodology refers to all cases ofdichromacy CVD, i.e. protanopia, deuteranopia, andtritanopia. It involves three color spaces namely, the RGB, theCIELab,
and
the
LMS.
In
what
follows,
X RGB , X Lab , and X LMS denote the RGB, the CIELab, and the
LMS color spaces. The mapping from X RGB to X Lab is

f : X RGB  X Lab , while the mapping from X RGB to X LMS isg : X RGB  X LMS . To carry out the above mappings, thecolors are appropriately gamma-corrected, the XYZ color
space is used as an intermediate space, and the wholeapproach is based on the CIE Standard Illuminant D65. Moreinformation about the above mappings can be found in [17,18, 19, 20].
Brettel et al [2] developed an algorithmic framework tosimulate the dichromatic human vision. They dealt with all
dichromacy cases, i.e. protanopia, deuteranopia, andtritanopia, and showed that a dichromat can see only asubspace of the X RGB . Herein, that subspace is denoted as
X RGB , D  X RGB , where the subscript D indicates thedichromacy and therefore, it interchangeably refers toprotanopia, deuteranopia, and tritanopia. To determine
X RGB , D , they performed specialized transformations thatinvolved the XYZ and LMS color spaces, and came up with amatrix-based mapping denoted as h : X RGB  X RGB , D . Thus,
given a color c  X RGB , the dichromat perceives it asc D  X RGB , D , with cD  h(c ) . For a detailed description of

the above-mentioned color simulation procedure, more
information can be found in [2].
The proposed image recoloring methodology consists ofseveral steps applied in sequence. These steps are analyticallydescribed in the following paragraphs.
First, we perform cluster analysis on the colors of theoriginal image. Clustering has been effectively used in many
applications [21, 22]. Herein, it is used to significantly reducethe color information involved in the computational
procedure and ultimately, reduce its complexity. Let us
assume that the size of the original image I is N  M . The setof colors of I is denoted as I RGB with I RGB  X RGB . Since the
CIELab color space preserves the Euclidean distances
between colors, the set I RGB is mapped in the CIELabspace: I Lab  f ( I RGB ) ,I Lab  X Lab . Then, thewell-known fuzzy c-means is applied over the set I Lab topartition it into n fuzzy clusters with centers x1 , x2 ,..., xn ,where i : xi  X Lab (note that in general xi  I Lab ).
The above-mentioned cluster centers are mapped in thespace X RGB : i  {1, 2,..., n}: yi  f 1 ( xi ) with yi  X RGB(note that in general yi  I RGB ). Given yi , its simulation to
the color perceived by a protanope, deuteranope or tritanope isdenoted as yi , D  h( yi ) . By defining a small integer

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the
next
condition  255
i  {1, 2,..., n}: yi  yi , D   .

ischecked:For protanopia, D , i ,12  D , i ,13  0 , while the rest fourparameters will be evaluated by the optimization process that
follows. For deuteranopia, D , i ,21  D , i , 23  0 , while the rest

If the above condition is true, the color yi is not muchdifferent from the yi , D , meaning that a dichromat canof the parameters will be adjusted by the optimization
procedurethat
follows.
Finally,fortritanopia
D , i ,31  D , i ,32  0 while the rest of the parameters will beperceive it correctly and to distinguish it from other colors.
Thus, this color remains intact. On the other hand, if thecondition is false, the color yi , and all colors in the I RGB thatbelong to the respective fuzzy cluster, will be recolored.
Thus, the set Y  { y1 , y2 ,..., yn } is divided into two subsets,treated similarly as in the previous cases.
To this end, the recolored color in the RGB space isobtained as v rec ,i  g 1 ( g (vrec ,i )) . In addition, its dichromacy
simulation is calculated according to the following mapping:v rec ,i , D  h( vrec ,i ) i  1, 2,..., n1 .

the subset V  {v1 , v 2 ,..., vn1 } , which includes the elements of
Y that will be recolored, and the subset U  {u1 , u2 ,..., un2 } ,The set that contains the recolored elements of V is denoted
Vrec  {v rec ,1 , v rec , 2 ,..., v rec , n1 }, while its dichromacy

which includes all the elements of Y that will remain intact.
Thus, V  U  , Y  V  U and n1  n2  n. The simulated
sets V and U as perceived by the dichromats are
VD  {v1, D , v 2, D ,..., vn1 , D } and U D  {u1, D , u2, D ,..., un2 , D }

as

with

mapped into the X Lab space and the following sets arederived:VD  X RGB , D ,U D  X RGB , D ,v i , D  h (v i ) ,simulation as Vrec , D  {v rec ,1, D , v rec , 2, D ,..., v rec , n1 , D } .
The elements of the sets V , Vrec , Vrec , D , U , and U D areandu j , D  h( u j ) .

The objective is to calculate a recoloring set
Vrec  vrec ,1 , vrec,2 ..., vrec, n1 οf the set V . Since the X LMSspace is directly related to the behavior of the cones, theimpact of their absence or dysfunctionality is well quantified
in that space. Therefore, the set V is mapped in X LMS as:g (V )  {g (v1 ), g (v2 ),..., g (vn1 )}withg (vi )  X LMSi  {1, 2,..., n1} . The same is done for VD , which gives theg (VD )  {g (v1, D ), g (v2, D ),..., g ( vn1 , D )}withf (V )  { f (v1 ), f (v2 ),..., f ( vn1 )}(4)f (Vrec )  { f (v rec ,1 ), f (v rec ,2 ),..., f (v rec , n1 )}(5)f (Vrec , D )  { f (v rec ,1, D ), f (vrec , 2, D ),..., f ( vrec , n1 , D )}(6)

f (U )  { f (u1 ), f (u2 ),..., f ( un2 )}(7)f (U D )  { f (u1, D ), f (u2, D ),..., f (un2 , D )}(8)

The main objective of the proposed method is to optimize
the parameters of the matrices  D ,i (with i  1, 2,..., n1 ) so thatsetg (vi , D )  X LMS i  {1, 2,..., n1} .

the recoloring process in eq. (1) will satisfy the requirements ofnaturalness and contrast enhancement. To accomplish thistask, we follow an approach similar to the one developed by
Chatzistamatis et al in [10].
Considering the sets f (V ) and f (U ) , the distances

In the LMS space, the error between the color g (vi ) andits simulation g (vi , D ) is: ei  g (vi )  g (vi, D ) , and therecoloring of g (vi ) is determined by a procedure similar tothe one developed in [10],between their elements f (vi )  f (u j ) ( 1  i  n1 ; 1  j  n2 )g (v rec ,i )  g ( vi )   D ,i ei

with

 1
D ,i,12 D ,i  D ,i ,211
 D ,i,31 D ,i,32D ,i ,13 D ,i,23 
1 

(1)

concern the color differences as perceived by a normal viewer.
After the recoloring process the respective differences asperceived by a dichromat viewer concern the sets f (Vrec , D )(2)and f (U D ) where the distances between their elements aref  vrec ,i, D   f  u j , D  . The objective is to keep the above
distances as similar as possible, so that the dichromat will beable to perceive the differences between colors in a similar way
as the normal color vision viewer. Thus, summing over all
pairs,

where i  1, 2,..., n1 . For protanopia, the implementation of(1) along with the respective matrix in (2) leads to a reductionof the L in favor of M and S, obtaining less saturated
red/oranges and more saturated greens, increasing the contrast
and therefore, decreasing color confusion of a protanope
viewer. In the case of deuteranopia, the above process leads to
the reduction of green in favor of red and blue colors obtaining
similar results. Finally, the above mechanism appears to have asimilar effect for the case of tritanopia. In all cases, the color
confusion is alleviated.

S1 

1 n1 n2
 f (vi )  f (u j )  f  vrec,i ,D   f  u j ,D 
n1 n2 i 1 j 1

(9)

Following the same approach for the sets f (V ) andf (Vrec , D ) we arrive at

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n1

S2 

fraction of the parameter values copied from one generation to
the next.
The optimization obtains the parameters of the matrices
 D ,i (i  1, 2,..., n1 ) that minimize the function J. Each matrix

n1

1
 f (vi )  f (v j )  f  vrec,i , D   f  vrec, j , D  (10)n12 i 1 j 1corresponds to one color of the set V.
Recalling that the elements of V were recolored, andIt can be easily seen that the minimization of J1 and J 2
obtains a recoloring image, the dichromatic perception ofwhich retains the contrast of the original one.
To satisfy naturalness, the objective function is
1 n1
S3   f (vi )  f (vrec,i )(11)n1 i 1vi  V corresponds to the cluster center f (vi ) in the CIELabspace, we must accordingly recolor all colors of the original
image that belong to the fuzzy cluster the center of which isthe color f (vi ) . We denote the subset of colors of I RGB thatEq. (11) can be easily interpreted since we wish themodified colors to be close to the original ones.
To this end, the overall objective function reads as
J  S1  S2   S3
(12)belong to that cluster as Ci (with Ci  I RGB ). Note thati  1, 2,..., n1 , and it refers to the color elements of the set V
because only these colors were recolored by the optimization

where  is a regularization parameter that takes positive
values and is used to obtain a counterbalance between the
distinct parts of the objective function.
To summarize, the main objective of the proposed
methodology is to minimize the function J with respect to theelements of the matrices  D ,i (with i  1, 2,..., n1 ) .

process. Let assume that c  Ci is the color of the pixelpkt (1  k  N ; 1  t  M ) of the original image I. Its
simulated vector is c, D  h(c ) . Both the above colors are
mapped in the X LMS space to get the vectors g (c ) andSpecifically, for the case of protanopia the parameters areD , i ,21 , D , i, 23 , D , i ,31 , D , i ,32 ,fordeuteranopia

D , i ,12 ,D , i ,13 ,D , i ,31 , D , i ,32 ,

and

for

g (c , D ) . Then, depending on the dichromacy CVD and using

tritanopia
the matrix  D,i that corresponds to vi (i.e. to Ci ) , we applyD , i ,12 , D , i ,13 , D , i , 21 , D , i , 23 . Thus, for each of the above
cases there are n1 matrices, and for each matrix 4 parameters.

eq. (1) to obtain the recolored color in the LMS space, which

Therefore, in all dichromatic cases there exist 4 n1
parameters to be optimized. To perform the optimization, we
use the differential evolution (DE) algorithm [23]. The DE
comprises three evolving learning phases: the mutation,
crossover and selection. Two parameters must be defined
namely, the FR   0, 1 that controls the rate at which theis denoted as g (crec,  ). Thus, the recoloring of c in the RGB

population evolves, and the CR   0, 1

set U remain intact.

space is obtained as: crec ,   g 1 ( g (crec ,  )) .

Finally, thepixels of the original image that correspond to colors
belonging to fuzzy clusters associated with the elements of thethat controls the5018

Anastasios Rigos et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 5015 – 5025

Figure 2: Testing paintings1: (a) Painting 1 (by Paul Gauguin), (b) Painting 2 (by Vincent van Gogh), (c) Painting 3 (by Paul Signac), (d) Painting
4 (by Terence Clarke), (e) Painting 5 (by Ljubomir Aleksandrovic), and (f) Painting 6 (by Achilleas Aivazoglou).

Figure 3: Dichromacy simulation paintings using the method of Brettel et al [2]: (a) Tritanopia simulation of Painting 1, (b) Deuteranopia
simulation of Painting 2, (c) Tritanopia simulation of Painting 3, (d) Deuteranopia simulation of Painting 4, (e) Protanopia simulation of Painting
5, and (f) Protanopia simulation of Painting 6.

1

The paintings were taken from the Web Gallery of Art in https://www.wga.hu/ and the Pallet Art in https://paletaart2.wordpress.com/

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1000

3. SIMULATION EXPERIMENTS

(a)

900

In this section the proposed algorithm is evaluated for thetest cases of six digitized art paintings depicted in Figure 2.
The respective simulated paintings using the method of
Brettel et al [2] for different dichromacy cases are depicted in
Figure 3.
The proposed algorithmic framework is compared to two
methods that also deal with protanopia, deuteranopia, andtritanopia. The first method was developed by Huang et al in[9], while the second one was introduced by Wong and Bishop
in [13].
To conduct the experiments we set   21 , which implies
that all colors belonging to a sphere with radius 21 areconsidered similar to the color located at the center of thatsphere. The number of clusters was preselected as n=24 for all
the experiments. Regarding eq. (12), we set   0.1 . The
domain of values for the optimizing parameters of thematrices in eq. (2) was the interval [0, 1]. The above values
were selected by trial-and-error. For the differential
evolution: FR  0.8 , CR  0.9 , the population size was equal
to 20, and the maximum number of iterations t max  200 .
For the other two methods, the parameter setting was thesame as reported in the respective references.
Figures 4-6 illustrate the behavior of the objective function
J during the implementation of the differential evolution
algorithm for all dichromacy cases and all paintings. Basedon these figures, it can be easily concluded that theconvergence of the objective function is smooth and fast.

Protanopia

800

Deuteranopia

700

Tritanopia

J 600
500
400
300
200
100
0
0

20

40

60

80

100 120 140 160 180 200

Iteration
900

(b)

800

Protanopia
Deuteranopia

700

Tritanopia

600
500

J

400
300
200
100
0
0

20

40

60

80

100 120 140 160 180 200

Iteration

Figure 5: The behavior of J as a function of the iteration number
considering the three cases of dichromacy for: (a) Painting 3, and(b) Painting 4.
1200

(a)

Protanopia

1000

Deuteranopia

800

Tritanopia

J

1800

(a)

Protanopia

600

1600
Deuteranopia

1400

400

Tritanopia

J 1200

200

1000
0

800

0

20

40

60

1400

400
200
0

20

40

60

80

100 120 140 160 180 200

Iteration
900

(b)

800

J

Protanopia

100 120 140 160 180 200

Protanopia

1000

Deuteranopia

800

Tritanopia

400

Tritanopia

600

(b)

1200

600

Deuteranopia

700

J

80

Iteration

600

500

200

400

0
0

300

20

40

60

80

100 120 140 160 180 200

Iteration

200

Figure 6: The behavior of J as a function of the iteration number
considering the three cases of dichromacy for: (a) Painting 5, and(b) Painting 6.

100
0
0

20

40

60

80

100 120 140 160 180 200

Iteration

Note that, in general, the smaller convergent values of J
arise in the case of tritanopia, while for protanopia anddeuteranopia are, pretty much, of equivalent size. Thisobservation can be justified by the fact that the blue color is

Figure 4: The behavior of J as a function of the iteration number
considering the three cases of dichromacy for: (a) Painting 1, and(b) Painting 2.

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not the dominant color in the paintings of Figure 2. Thus, thecardinality of the set V is small resulting in small values of theindividual parts of the function J, as reported in eqs (9)-(11).
The next experiment concerns the naturalness evaluationof the recolored image. For an image of size N  M thenaturalness is quantified by the index studied in [11], which isa modified version of the index developed in [4],Enat 

1 N M
 pkt  prec,kt
N M k 1 t 1

vision viewer. However, there are some results (i.e. painting 2
in the protanopia experiment, painting 6 in the deuteranopia
experiment, and painting 6 in the tritanopia experiment)
where the proposed framework was outperformed by themethod of Wong and Bishop [13]. Remarkably, even in theseexperimental simulations, it exhibited a behavior that is closeenough to the respective behavior of the method in [13]. As
far as the method of Huang et al [9] is concerned, its behavior
is inferior in all experiments.
To continue the experimental evaluation of the algorithm,
the feature similarity index (FSIMc) developed in [24] isstudied. As the value of the index FSIMc increases thechrominance information of the recolored image is closer tothe chrominance information of the original image.

(13)

where pkt is a pixel of the original image. Smaller values of
the above index result in better performance as far as thenaturalness of the recolored image is concerned.

Table 4: FSIMc index for the case of protanopia
Table 1: Naturalness index for the case of protanopia
Painting
Painting 1
Painting 2
Painting 3
Painting 4
Painting 5
Painting 6

Huang etal[9]19.12412.43929.24515.06025.48835.033

Wong andBishop[13]22.5339.038
31.69124.43620.09824.833

Painting

Proposed

Painting 1
Painting 2
Painting 3
Painting 4
Painting 5
Painting 6

7.698
12.74524.0133.964
15.585
8.636

Painting 1
Painting 2
Painting 3
Painting 4
Painting 5
Painting 6

Huang et
al
[9]
9.938
6.511
47.346
11.24117.31032.455Wong andBishop[13]22.5339.038
31.69124.43620.09822.086

Propose
d
6.381
4.417
16.3373.404
9.588
24.833

Painting

Table 3: Naturalness index for the case of tritanopia
Painting
Painting 1
Painting 2
Painting 3
Painting 4
Painting 5
Painting 6

Huang etal[9]78.73121.42329.87430.92026.43138.195Wong andBishop[13]
23.811
11.63024.66935.96434.099
7.342

Wong andBishop[13]0.94390.98300.94220.96530.97000.9347

Proposed0.99110.9876
0.98910.99030.99150.9883

Tables 4-6 illustrate the FSIMc values obtained by the threemethods for all dichromacy cases and all paintings. As
indicated by the above tables, apart from some experimental
cases (i.e. painting 2 in the protanopia experiment, paintings 2
and 6 in the deuteranopia experiment, and paintings 3 and 6 in
the tritanopia experiment), the proposed algorithm achieves
the best results in most of the simulations, which directly
implies that the recolored paintings look similar to the original
ones.
Table 5: FSIMc index for the case of deuteranopia

Table 2: Naturalness index for the case of deuteranopia
Painting

Huang etal
[9]0.98730.99180.96700.9816
0.97790.9644

Painting 1
Painting 2
Painting 3
Painting 4
Painting 5
Painting 6

Proposed
18.7956.986
23.545
6.345
10.63315.307Huang etal
[9]0.99270.98300.9466
0.9916
0.98800.9722

Wong andBishop[13]0.94390.9976
0.94220.96530.97000.9347

Proposed0.99350.9926
0.9926
0.9954
0.99200.9589

Table 6: FSIMc index for the case of tritanopia
Painting

Tables 1-3 depict the values of the naturalness index in eq.
(13) obtained by the three methods for protanopia,
deuteranopia and tritanopia and all paintings. The best values
for each simulation are presented in bold fonts.
The results reported in those tables indicate that in most of
the experimental simulations, the proposed method obtains
the best values for the naturalness index, meaning that the
produced recolored images maintain high quality in terms ofthe natural appearance when they are seen by a normal color

Painting 1
Painting 2
Painting 3
Painting 4
Painting 5
Painting 6

Huang etal
[9]0.93280.93130.98030.97430.96520.9690Wong andBishop[13]
0.94610.9386
0.95350.88950.92280.9953

Proposed0.95000.97310.91440.98380.97470.9300The next experiment concerns the visual comparison of thethree algorithms. Figures 7-10 report some of the results.
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Figure 7: Results on Painting 5 for the case of Protanopia: (a) recolored (method of Wong et al [13]), (b) recolored as seen by a protanope (methodof Wong et al [13]), (c) recolored (proposed method), (d) recolored as seen by a protanope (proposed method); Results on Painting 6 for the case ofProtanopia: (e) recolored (method of Huang et al [9]), (f) recolored as seen by a protanope (method of Huang et al [9]), (g) recolored (proposed
method), (h) recolored as seen by a protanope (proposed method).

Figure 8: Results on Painting 2 for the case of Deuteranopia: (a) recolored (method of Huang et al [9]), (b) recolored as seen by a deuteranope(method of Huang et al [9]), (c) recolored (proposed method), (d) recolored as seen by a deuteranope (proposed method); Results on Painting 4 forthe case of Deuteranopia: (e) recolored (method of Wong et al [13]), (f) recolored as seen by a deuteranope (method of Wong et al [13]), (g) recolored(proposed method), (h) recolored as seen by a deuteranope (proposed method).

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Anastasios Rigos et al., International Journal of Advanced Trends in Computer Science and Engineering, 9(4), July – August 2020, 5015 – 5025

Figure 9: Results on Painting 1 for the case of Tritanopia: (a) recolored (method of Wong et al [13]), (b) recolored as seen by a tritanope (methodof Wong et al [13]), (c) recolored (proposed method), (d) recolored as seen by a tritanope (proposed method).

Figure 10: Results on Painting 3 for the case of Tritanopia: (a) recolored (method of Huang et al [9]), (b) recolored as seen by a tritanope (methodof Huang et al [9]), (c) recolored (proposed method), (d) recolored as seen by a tritanope (proposed method).

Specifically, Figure 7 depicts some results for protanopia,
where the proposed method is compared to the method
developed in [13] for the painting 5, and the method in [9] forthe painting 6. Figure 8 illustrates some results obtained for
deuteranopia, where our algorithm is compared to the method
in [9] for the painting 2, and the method in [13] for the painting
4. Figure 9 presents the results for painting 1, where theproposed methodology is compared to the method of Wong etal [13] considering the tritanopia deficiency. Finally, Figure 10
reports the respective results for the painting 3 considering thecase of tritanopia, where the proposed method is compared tothe method of Huang et al [9].

In view of the respective images in Figures 2 and 3, it can beeasily seen that the recolored images, obtained by the proposed
method in Figures 7-10, look more similar to the original
images than the recolored images from the other two
recoloring methods. These results are quite convincing and aresupported by the numerical results reported in Tables (1)-(6).
The main outcomes of the above experiments indicate thatthe naturalness index and the FSIMc index are effectively
optimized by the proposed approach. In addition, the contrastof the original image is sufficiently retained by the recolored
image, while the method can naturally modify the originalcolors enhancing the perception of color-blind viewers.
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4. J.-B. Huang, Y.-C. Tseng, S.-I. Wu, S.-J. Wang,“Information preserving color transformation forprotanopia and deuteranopia”, IEEE Signal Processing
Letters, Vol. 14 (10), pp. 711-714, 2007.
5. J.-Y. Jeong, H.-J. Kim, T.-S. Wang, Y.-J. Yoon, S.-J. Ko,“An efficient re-coloring method with information
preserving for the color-blind”, IEEE Transactions on
Consumer Electronics, Vol. 57 (4), pp. 1953-1960, 2011.
6. R. Sayal, C. Subbalakhmi, H. S. Saini, “Mobile App
accessibility for visually impaired”, International
Journal of Advanced Trends in Computer Science andEngineering, Vol. 9(1), pp. 182-185, 2020.
https://doi.org/10.30534/ijatcse/2020/27912020
7. M. F. Hassan, R. Paramesran, “Naturalness preserving
image recoloring method for people with red–green
deficiency”, Signal Processing: Image Communication
Vol. 57, pp. 126–133, 2017.
8. L. Jefferson, R. Harvey, “Accommodating color blind
computer users”, In the Proceedings of the 8thinternational ACM SIGACCESS conference on
Computers and accessibility (Assets '06), pp. 40-47,
2006.
9. J.-B. Huang, C.-S. Chen, T.-S. Jen, S.-J. Wang, “Image
recolorization for the color blind”, In the Proceedingsof the 2009 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2009), pp.
1161-1164, 2009.
10. G. E. Tsekouras, S. Chatzistamatis, C. N.
Anagnostopoulos, D. Makris, “Color adaptation forprotanopia using differential evolution-based fuzzyclustering: A case study in digitized paintings”, In the
Poceedings of the 2018 IEEE Conference on Evolving
and Adaptive Intelligent Systems (EAIS 2018), 2018.
11. S. Chatzistamatis, A. Rigos, G. E. Tsekouras, “Image
Recoloring of Art Paintings for the Color Blind
Guided by Semantic Segmentation”, In theProceedings of the 21st International Conference on
Engineering Applications of Neural Networks (EANN
2020), pp. 261-273, 2020.
https://doi.org/10.1007/978-3-030-48791-1_20
12. H.-Y. Lin, L.-Q. Chen, M.-L. Wang, “Improving
discrimination in color vision deficiency by image
re-coloring”
Sensors,
19,
2250,
2019;
doi:10.3390/s19102250.
13. A. Wong, W. Bishop, “Perceptually-adaptive color
enhancement of still images for individuals withdichromacy”, In the Proceedings of the 21st Canadian
Conference on Electrical and Computer Engineering,2008.
14. R. Johnston-Feller, “Color science in the examination
museum objects”, Getty Conservation Institute, Los
Angeles, 2001.
15. G. W. Ho, “Color, vision, and art: teaching, learning,and making art with color blind awareness”, MsC
Thesis, University of Florida, 2014.

4. CONCLUSIONSIn this paper an effective algorithmic framework was
proposed to perform recoloring of art paintings. The goal wasto enhance the color perception of people suffering fromdichromacy. All cases of dichromacy, i.e. protanopia,
deuteranopia, and tritanopia, were studied. The methodology
involved three standard color spaces namely, the RGB, theCIELab, and the LMS space. To reduce the computationaltime, the colors of the original image were clustered. The
resulting cluster centers (which are colors) were simulated bya standard model to approximate the dichromacy color vision
deficiency. Then, colors that are confused by the color-blind
were embedded in an objective function, the minimization ofwhich estimated the parameters that decide the recoloring
strategy.
The method was tested in several experimental cases. The
main findings of the experiments can be enumerated asfollows: (a) the naturalness of the recolored image ispreserved when it is compared to the original image,indicating that the recolored image is naturally perceived by anormal color vision viewer; (b) the contrast is sufficiently
retained by the recolored image; (c) the colors in the recolored
image can easily be distinguished by the color-blind.
Future efforts could extend the present algorithm bydeveloping more sophisticated learning approaches andeffective color transformations to improve the color
perception by viewers that suffer from dichromacy andanomalous trichromacy.
ACKNOWLEDGEMENT
This research is co-financed by Greece and the European
Union (European Social Fund-ESF) through the Operational
Programme “Human Resources Development, Education andLifelong Learning 2014-2020” in the context of the project“Color perception enhancement in digitized art paintings forpeople with Color Vision Deficiency” (MIS 5047115).

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