U.S. patent application number 10/512730 was filed with the patent office on 2005-05-19 for method and system for transforming adaptively visual contents according to terminal user's color vision characteristics.
Invention is credited to Hong, Jin-Woo, Kim, Cheon-Seog, Kim, Jae-Joon, Kim, Jin-Woong, Nam, Je-ho, Ro, Yong-Man, Song, Jae-Ii, Yang, Seung-Ji.
Application Number | 20050105796 10/512730 |
Document ID | / |
Family ID | 29273744 |
Filed Date | 2005-05-19 |
United States Patent
Application |
20050105796 |
Kind Code |
A1 |
Hong, Jin-Woo ; et
al. |
May 19, 2005 |
Method and system for transforming adaptively visual contents
according to terminal user's color vision characteristics
Abstract
Disclosed are a method and a system that adaptively transform
visual contents inputted from a network, in accordance with the
visual characteristics of a terminal user. A visual characteristics
descriptor that describes the information of the user visual
characteristics in a predetermined format is proposed. The
descriptor includes the information of the color vision deficiency
type and the color vision deficiency degree. The color vision
deficiency may be described in numerical degree or textual degree.
The invention adaptively transforms visual contents differently in
accordance with the color vision deficiency type.
Inventors: |
Hong, Jin-Woo; (Daejon-si,
KR) ; Yang, Seung-Ji; (Kangwon-do, KR) ; Song,
Jae-Ii; (Gangsuh-gu, KR) ; Ro, Yong-Man;
(Yuseong-gu, KR) ; Nam, Je-ho; (Seoul, KR)
; Kim, Jin-Woong; (Yusung-gu, KR) ; Kim,
Jae-Joon; (Seo-gu, KR) ; Kim, Cheon-Seog;
(Seo-gu, KR) |
Correspondence
Address: |
Mavis S Gallenson
Ladas & Parry
Suite 2100
5670 Wilshire Boulevard
Los Angeles
CA
90036-5679
US
|
Family ID: |
29273744 |
Appl. No.: |
10/512730 |
Filed: |
October 26, 2004 |
PCT Filed: |
April 14, 2003 |
PCT NO: |
PCT/KR03/00750 |
Current U.S.
Class: |
382/162 ;
345/581; 348/E9.051; 382/114 |
Current CPC
Class: |
G09G 5/02 20130101; G06F
3/14 20130101; G06T 11/001 20130101; G09G 2320/0666 20130101; G09G
2340/14 20130101; H04N 9/73 20130101 |
Class at
Publication: |
382/162 ;
382/114; 345/581 |
International
Class: |
G06K 009/00; G09G
005/00 |
Claims
What is claimed is:
1. A method for adaptively transforming visual contents to be
suitable for color vision characteristics of a user, the method
comprising the steps of: receiving information on color vision
characteristics of a user; and transforming adaptively the visual
contents in accordance with the information on color vision
characteristics. wherein the information on color vision
characteristics includes descriptions as to color vision deficiency
type and color vision deficiency degree of the user.
2. The method according to claim 1, further comprising step of:
receiving information on a user environment, wherein the adaptive
transforming is executed in accordance with the information on
color vision characteristics and the user environment.
3. The method according to claim 2, wherein the user environment is
described with the illumination of the surrounding of the user.
4. The method according to claim 1, wherein the color vision
deficiency degree is described numerically or texturally, and the
color vision deficiency degree is described with a numerical value
in the range of 0.0 to 1.0 when numerically described, and wherein,
in the event the user is a dichromat, the color vision deficiency
degree is described as 1.0.
5. The method according to claim 1, wherein the adaptive
transforming is executed by distinguishing between a dichromat and
an anomalous trichromat according to the color vision deficiency
degree, and approaching the dichromat and the anomalous trichromat
differently.
6. The method according to claim 5, wherein the adaptive
transforming for a dichromat is executed by the steps of:
differentiating a deficiency region which is difficult for the user
to detect, from the visual contents according to the color vision
deficiency type; and adjusting at least one of hue, saturation and
intensity of pixels in the deficiency region.
7. The method according to claim 6, wherein the differentiating of
the deficiency region is executed by transforming the visual
contents from RGB color space to CMYK color space, and
discriminating pixels in the deficiency region by using the values
of cyan, magenta, and yellow in accordance with the color vision
deficiency type.
8. The method according to claim 6, wherein the differentiating of
the deficiency region is executed by transforming the visual
contents from RGB color space to LMS color space, transforming the
transformed visual contents with LMS response function of the user,
which is determined with the color vision deficiency type and the
color vision deficiency degree, and measuring the degree of
decrease of the respective LMS values.
9. The method according to claim 6, wherein the adjusting is
executed by changing the hue and the saturation of the pixels in
the deficiency region.
10. The method according to claim 5, wherein the adaptive
transforming for an anomalous trichromat is executed by the steps
of: transforming the visual contents from RGB color space to LMS
color space; transforming the visual contents in LMS color space
with an LMS response function of the user, which is determined with
the color vision deficiency type and the color vision deficiency
degree; and transforming again the transformed visual contents from
LMS color space to RGB color space.
11. A method for adaptively transforming visual contents to be
suitable for the color vision characteristics of a user of an image
display device, the method comprising the steps of: receiving
information on the color vision characteristics of the user;
receiving visual contents; transforming adaptively the visual
contents in accordance with the information on the color vision
characteristics; and displaying the transformed visual contents
through the image display device.
12. The method according to claim 11, wherein the information on
the color vision characteristics contains descriptions as to the
color vision deficiency type and the color vision deficiency degree
of the user.
13. The method according to claim 12, wherein the color vision
deficiency degree is described by a normalized numerical value from
0.0 to 1.0, and in the event the user is a dichromat, the color
vision deficiency degree is described as 1.0.
14. The method according to claim 12, wherein the numerical
description of the color vision deficiency degree is determined in
accordance with the shift or the intensity decrease of a response
function of the user's cone cells.
15. The method according to claim 12, wherein the numerical
description of the color vision deficiency degree is determined by
using the total error score obtained from the Farnsworth-Munsell
hue test for the user.
16. The method according to claim 12, wherein the numerical
description for the color vision deficiency degree is determined by
using the area of the red/green ratio section in a mixture field
that is recognized by the user as identical to a test field after
anomaloscope testing for the user.
17. The method according to claim 14, wherein the numerical
description for the color vision deficiency degree is determined by
the following equation 23 { w z .times. ( max ) + w l .times. ( max
) } / ( w z max + w l max ) wherein .alpha. is the shift value of
the user's cone cells, .alpha..sub.max is the maximum shift value
of the user's cone cells, .beta. is the intensity decrease value of
the user's cone cells, .beta..sub.max is the maximum intensity
decrease value of the user's cone cells, .omega., is a weighting
value for the shift value, .omega..sub.1 is the weighting value for
the intensity decrease value, .omega..sub.z.sup.max is the maximum
value of .omega..sub.z, and .omega..sub.1.sup.max is the maximum
value of .omega..sub.1.
18. The method according to claim 12, wherein the numerical
description of the color vision deficiency degree is determined by
the following equation: 24 { E - E min E max - E min , E min < E
< E max 1.0 , E th max wherein E is the total error score of the
user, E.sub.min is the minimum threshold value where the user is
determined as an anomalous trichromat, and E.sub.max is the maximum
threshold value where the user is determined as an anomalous
trichromat.
19. The method according to claim 12, wherein the numerical
description for the color vision deficiency degree is determined by
the following equation: color vision deficiency degree is
determined by the following equation: 25 { R d R th , R d R th 1.0
, R d > R th , Here, R.sub.dR-.sub.max-R.sub.min, 26 R th = { R
min normal , green color vision deficiency 73 - R max normal , red
color vision deficiency wherein R.sub.d is the range of a red/green
ratio section in a mixture field that is recognized by the user as
identical to the test field, R.sup.normal.sub.min and
R.sup.normal.sub.max are the minimum and maximum values of the
range of the red/green ratio section of a normal human, and
R.sub.th is the minimum threshold value of R.sub.d where the user
is determined as an anomalous trichromat.
20. The method according to claim 11, wherein the information on
the color vision characteristics further comprises identification
information on the user.
21. The method according to claim 11, further comprising the step
of receiving information on the user's environment, wherein the
visual contents are transformed in accordance with the information
on the color vision characteristics and the user's environment.
22. The method according to claim 21, wherein the information on
the user's environment comprises description as to the illumination
of the user's surroundings.
23. A system for adaptively transforming visual contents to be
suitable for the color vision characteristics of a user of an image
display device, the system comprising: means for receiving
information on the color vision characteristics of the user; means
for receiving visual contents; and accordance with the information
on the color vision characteristics of the user.
24. The system according to claim 23, further comprising: means for
storing the information on the color vision characteristics and
supplying the information on the color vision characteristics to
the processing section in a standardized XML specification.
25. The system according to claim 23, wherein the information on
the color vision characteristics contains descriptions as to the
color vision deficiency type and the color vision deficiency degree
of the user, and the color vision deficiency degree is described
with a normalized numerical value.
26. The system according to claim 25, wherein the numerical
description for the color vision deficiency degree is determined by
the following equation: 27 { w z .times. ( max ) + w l .times. (
max ) } / ( w z max + w l max ) wherein .alpha. is the shift value
of the user's cone cells, .alpha..sub.max is the maximum shift
value of the user's cone cells, .beta. is the intensity decrease
value of the user's cone cells, .beta..sub.max is the maximum
intensity decrease value of the user's cone cells, .omega..sub.z is
a weighting value for the shift value, .omega..sub.1 is the
weighting value for the intensity decrease value,
.omega..sub.z.sup.max is the maximum value of .omega..sub.z, and
.omega..sub.1.sup.max is the maximum value of .omega..sub.1.
27. The system according to claim 25, wherein the numerical
description of the color vision deficiency degree is determined by
the following equation: 28 { E - E min E max - E min , E min < E
< E max 1.0 , E th max wherein E is the total error score of the
user, E.sub.min is the minimum threshold value where the user is
determined as an anomalous trichromat, and E.sub.max is the maximum
threshold value where the user is determined as an anomalous
trichromat.
28. The system according to claim 25, wherein the numerical
description for the color vision deficiency degree is determined by
the following equation: 29 { R d R th , R d R th 1.0 , R d > R
th , Here, R.sub.d=R.sub.max-R.sub.min, 30 R th = { R min normal ,
green color vision deficiency 73 - R max normal , red color vision
deficiency wherein R.sub.d is the range of a red/green ratio
section in a mixture field that is recognized by the user as
identical to the test field, R.sup.normal.sub.min and
R.sup.normal.sub.max are the minimum and maximum values of the
range of the red/green ratio section of a normal human, and
R.sub.th is the minimum threshold value of R.sub.d where the user
is determined as an anomalous trichromat.
29. The system according to claim 25, wherein the processing
section executes adaptive transforming for dichromat on the
received visual contents in accordance with the color vision
deficiency type if the user is determined to be a dichromat from
the information on the color vision deficiency degree, and executes
adaptation for anomalous trichromat on the received visual contents
in accordance with the color vision deficiency type if the user is
determined to be an anomalous trichromat from the information on
the color vision deficiency degree.
30. A system according to claim 29, wherein the adaptive
transforming for dichromat is executed by differentiating a
deficiency region, which is difficult for the user to detect, from
the visual contents in accordance with the color vision deficiency
type; and transforming at least one of hue, saturation and
intensity of pixels in the deficiency region.
31. A system according to claim 30, wherein the differentiating of
the deficiency region is executed by transforming the visual
contents from RCB color space to CMYK color space, and
discriminating pixels corresponding to a predetermined region in
the CYMK color space in accordance with the color vision deficiency
type.
32. A system according to claim 30, wherein the differentiating of
the deficiency region is executed by transforming the visual
contents from RCB color space to CMYK color space, and measuring
the degree of decrease of the respective LMS values during the
process of transforming the transformed visual contents with a LMS
response function of the user, in which the response function is
determined in accordance with the color vision deficiency type and
the color vision deficiency degree.
33. A system according to claim 29, wherein the adaptive
transforming for dichromat is executed by determining the color
vision deficiency region and the color vision deficiency degree of
the user at the same time by using a CMY ration of the visual
contents.
34. A system according to claim 25, wherein the adaptive
transforming for anomalous trichromat is executed by transforming
the visual contents from RGB color space to LMS color space,
transforming the visual contents in LMS color space by using the
inverse function of an LMS response function of the user, in which
the LMS response function is determined in accordance with the
color vision deficiency type and the color vision deficiency
degree, and transforming again the transformed visual contents from
LMS color space to RGB color space.
Description
TECHNICAL FIELD
[0001] The present invention relates to a method and a system for
transforming visual contents and, in particular, to a method and a
system for adaptively transforming visual contents in accordance
with the color vision characteristics of a user.
BACKGROUND ART
[0002] The MPEG-21 is being established as the next generation
standard for a multimedia framework by MPEG (Moving Picture Expert
Group), which is a Working Group of ISO/IEC JTC 1/SC 29. The goal
of MPEG-21 is to realize a multimedia integration framework capable
of freely and easily using multimedia contents despite the
wide-range characteristics of networks, terminals and users,
existing under various environments, by unifying the standards of
the existing MPEGs or other standardization groups. The digital
item adaptation part of the MPEG-21, Part 7 relates to adaptively
transforming the multimedia contents (or digital items) in
accordance with the characteristics of networks, terminals (video
display devices) and users, the standardization of which is now in
progress.
[0003] Preceding researches for users with a color vision
deficiency are as follows: In "Computerized Simulation of Color
Appearance for Dichromats" (Journal of Optical Society of America.
A, v.14, no. 10, 1997, 2647-2655), H. Brettel studied an algorithm
for allowing common users to experience the color vision
characteristics of users with dichromacy. However, in this paper,
only an algorithm capable of simulating the color vision
characteristics of users with the color vision deficiency is
disclosed. An adaptation algorithm for obtaining information that
is impossible or difficult to obtain due to the color vision
deficiencies is not mentioned. This method requires that contents
manufacturers perform a simulation process for dichromats before
selecting the colors of the contents. An object of such a method is
to avoid a combination of colors that is difficult to be
distinguished by the dichromats, if possible, by performing a
simulation process to determine whether the dichromats can
discriminate the selected combination of the colors.
[0004] However, this method urges the contents manufacturers to use
limited number of colors, thereby restricting the creativity of the
manufacturers and possibly inducing the inconvenience and monotony
in the process of recognizing the color information for normal
users. Therefore, this method is difficult to satisfy the
requirements of various users. Accordingly, there is a need for
adaptation not in the contents manufacturing step, but in
accordance with individual vision abilities or terminal devices.
Nowadays, numerous digital multimedia contents are manufactured
even in a day. Thus, such a process performed in the contents
manufacturing step has a disadvantage in that it is impossible to
adaptively transform the already existing contents.
[0005] In order to solve these problems, improving the abilities
for recognizing the color information processing of humans with a
color vision deficiency by directly transforming the colors of
visual contents may be considered. This method has an advantage in
that it is not required to redesign a display device and it is
possible to adaptively transform all existing contents.
[0006] A method of adaptation for users with a color vision
deficiency is discussed in "Enhancing Color Representation for
Anomalous Trichromats on CRT Monitors Color" (G. Kovacs, Research
and Application, v.26 SUPP, 2001, S273-S276), in which an algorithm
is disclosed which allows the users to see like a normal user by
computing a filter to be mounted in cathode ray tube (to be
referred as "CRT") and applying the obtained filter to a RGB
spectrum response value of a corresponding CRT monitor. However,
this method applies a filter to a monitor and has a disadvantage in
that it is impossible to perform a transformation in accordance
with the contents if a plurality of digital items, i.e. a number of
images, exist in a screen. Furthermore, it is a burden to purchase
a specially manufactured CRT monitor in order to implement this
function.
[0007] In the Gazette of U.S. Pat. No. 6,362,830, an equation for
modeling a human with a color vision deficiency is vaguely derived.
However, the process for adaptively transforming visual contents in
accordance with the color vision characteristics of humans with a
color vision deficiency is very complicated. Moreover, the method
does not allow humans with a color vision deficiency to conceive
the adaptively transformed visual contents, but allows humans to
merely discriminate the visual contents. The disclosure of U.S.
Pat. No. 6,362,830 is incorporated herein by reference.
[0008] Humans recognize colors and brightness of an object by the
visual cells sensing the light reflected from the surface of the
object. The visual cells existing in the retina include rod cells
and cone cells. The visual cells are specialized cells that
function to sense light. Human eyes contain about seven million
cone cells and one hundred and thirty million rod cells. Humans
discriminate light and darkness using the rod cells and recognize
detailed appearance and colors using cone cells. As photochrome
contained in the cone cells absorb photons, color recognition of
humans is made. Normal humans have three types of cone cells, which
absorb different portions of light with a visible wavelength, in
the retina. The types are classified into L (long), M (middle) and
S (short) in accordance with the peak sensitivity of a wavelength
region absorbed by each type of cone cell. Humans recognize colors
depending on the ratio of signals which the three types of cone
cells generate in accordance with light.
[0009] Unlike the above conditions, color vision deficiency is the
state in which any of the three types of cone cells does not exist
naturally or function abnormally. If there are only two types of
cone cells, it is called a dichromacy. In addition, if the function
of the cone cells is abnormal, even though all three types exist,
it is called an anomalous trichromacy. In the world, about 8% of
males and about 0.5% of females have a color vision deficiency.
Nevertheless, no method for treating color vision deficiencies
exists at present; thus, this study has been commissioned to
research a new scheme for treating color vision deficiencies.
[0010] It is medically impossible to make humans with a color
vision deficiency see original colors. The goal of adaptation for
dichromacy is to allow humans with a color vision deficiency to
obtain information from the colors of contents at the same level of
a normal human, although they are not capable of seeing the
original colors.
DISCLOSURE OF THE INVENTION
[0011] It is an object of the present invention to provide a user
Wraith a color vision deficiency with the semantic information of
visual contents that corresponds to a normal user regardless of the
color vision deficiency type and without any separate
equipment.
[0012] It is another object of the present invention to provide a
user with a color vision deficiency with the semantic information
of visual contents that corresponds to a normal user in accordance
with the digital items.
[0013] In order to achieve the above objects, there are provided a
method and a system for adaptively transforming visual contents
inputted from a network to be suitable for the color vision
characteristics of a terminal user. At first, a color vision
characteristic descriptor is presented which describes information
on the color vision characteristics of the user in a standardized
format in which the characteristics of the network and the terminal
are not considered. The color vision characteristic descriptor in
accordance with the present invention contains information on the
color vision deficiency type and degree of the user. The color
vision deficiency degree is texturally or numerically described.
The color vision characteristic descriptor may further comprise
information indicating the user identification information or the
existence of a color vision deficiency. In addition, the color
vision characteristic descriptor may comprise user environment, in
particular, information on the illumination of the surroundings of
the user.
[0014] The present invention adaptively transforms visual contents
differently in accordance with the color vision deficiency type,
i.e. depending on whether the color vision deficiency is dichromacy
or anomalous trichromacy. At first, the present invention detects a
region difficult for the user with a dichromacy if it is determined
that the user is a dichromat from the information on the degree of
deficiency for color vision contained in the color vision
characteristic descriptor. The first method presented in accordance
with the present invention detects the region difficult for the
user with dichromacy by comparing the user limited LMS region to
the LMS region of a normal human and then calculating the region in
which the LMS value decreases. The second method presented in
accordance with the present invention may be implemented in such a
manner that the visual contents are transformed from the RGB color
space to the CMYK color space for identification of the deficiency
region and the pixels corresponding to a predetermined region in
the CMYK color space are differentiated in accordance with the
color vision deficiency type. If the deficiency region is
differentiated in this manner, the visual contents are adaptively
transformed to be suitable for the color vision characteristics of
the user by tuning at least one of hue, saturation and intensity of
the respective pixels corresponding to the deficiency region.
[0015] Meanwhile, if it is determined that the user is an anomalous
trichromat, the visual contents are transformed from the RGB color
space to the LMS color space and the substance of the visual
contents are adaptively transformed by using a cone cell response
function of the user eyes.
[0016] The present invention provides a method for adaptively
transforming a visual contents to be suitable for the color vision
characteristics of a user, the method comprising the steps of:
receiving information on the color vision characteristics of the
user; and executing the adaptation to the visual contents in
accordance with the color vision characteristics, wherein the
information on the color vision characteristics contains a
description of color vision deficiency type and degree.
[0017] In addition, the present invention provides a method for
adaptively transforming a visual contents to be suitable for the
color vision characteristics of a user of an image display device,
the method comprising the steps of: receiving information on the
color vision characteristics of the user; receiving the visual
contents; executing the adaptation on the visual contents in
accordance with the color vision characteristics; and displaying
the transformed visual contents through the image display
device.
[0018] The present invention also provides a system for adaptively
transforming visual contents to be suitable for the color vision
characteristics of a user of an image display device, the system
comprising: a means for receiving information on the color vision
characteristics of the user; a means for receiving the visual
contents; and a processing section for executing adaptation to the
inputted visual contents in accordance with the information on the
color vision characteristics.
[0019] As described above, in accordance with the present
invention, a user with a color vision deficiency is able to receive
semantic information from visual contents that are substantially
identical to that of a normal user without any separate equipment,
whereby the user with a color vision deficiency is able to freely
and conveniently use multimedia contents. In addition, the present
invention is applicable to the digital item adaptive part of MPEG-7
and MPEG-21, which are the international standards of media.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] FIG. 1 is a block diagram of an adaptation system in
accordance with an embodiment of the present invention;
[0021] FIG. 2 is a flowchart of an adaptation process according the
present invention;
[0022] FIG. 3 is a structure view of a user color vision
characteristic descriptor in accordance with an embodiment of the
present invention;
[0023] FIG. 4 is a view showing an example of enumerating the
degree of deficiency for color vision by using the results of the
Farnsworth-Munsell test;
[0024] FIG. 5 is a view showing an example for enumerating the
degree of deficiency for color vision by using the results of the
Nagel Anomaloscope test;
[0025] FIG. 6 is a detailed flowchart which shows an example of the
adaptation step of FIG. 2;
[0026] FIG. 7 is a detailed flowchart which shows an example of the
adaptation step of anomalous trichlomacy of FIG. 6;
[0027] FIG. 8 is a view showing the spectral sensitivity of LMS
cone cells of a normal human;
[0028] FIG. 9 is a view showing a RGB emission curve of a CRT
monitor wraith P22 phosphor.
[0029] FIG. 10 is a view showing the stimuli in the LMS color
space;
[0030] FIG. 11 is a view showing the spectral sensitivity of
protanomaly in which the peak sensitivity of L cone cells moves
about 10 nm;
[0031] FIG. 12 is a detailed flowchart of an example of the
dichromacy adaptation process of FIG. 6;
[0032] FIG. 13 is a view showing color spaces recognized by a human
with a deficiency of dichromacy;
[0033] FIG. 14 is a detailed flowchart of an example of a method of
discriminating the deficiency region in FIG. 12;
[0034] FIG. 15 is a view showing hues recognized by a normal human,
a human with a deficiency of protanopy or deuteranopy, and a human
with a deficiency of tritanopy, respectively;
[0035] FIG. 16 is a view showing hues recognized by a normal human,
a human with a deficiency of protanopy or deuteranopy, and a human
with a deficiency of tritanopy for a hue angle in the range of
0.degree. to 360.degree.;
[0036] FIG. 17 is a detailed flowchart of another example of the
process for discriminating the deficiency region in FIG. 12;
[0037] FIG. 18 is a detailed flowchart of an example of the HIS
tuning method in FIG. 12; and
[0038] FIG. 19 is a view showing the distribution of magenta, cyan,
and yellow components in the color distribution.
BEST MODE FOR CARRYING OUT THE INVENTION
[0039] Hereinbelow, the present invention will be described in
detail with reference to the accompanying drawings. For the purpose
of consistency in description, like reference numerals are used to
indicate like components and signals in the drawings.
[0040] FIG. 1 is a block diagram of an adaptation system in
accordance with an embodiment of the present invention. FIG. 2 is a
flowchart of the adaptation method in accordance with the present
invention, in which the adaptation is a processing step that is
specifically executed in a processing section 102 shown in FIG. 1.
As shown in FIG. 1, an adaptation system 100 is implemented to
include a processing section 102, an input section 103, a database
104, a network interface 106, and an image display device 110. The
processing section 102 comprises a dichromacy adaptation section
110 and an anomalous trichromacy adaptation section 112.
[0041] The user inputs the user own information on color vision
characteristics and environment to the processing section 102
through an input device 103 such as a keyboard (step 202). The
processing section 102 receives the information on the color vision
characteristics through the input device 103 and stores it in the
database 104 in a predetermined format, thereby initializing the
adaptation system 100. The information prepared and stored in a
predetermined format for the color vision characteristics of the
user is called a color vision characteristic descriptor 114. The
visual contents are provided from an external network 107 to the
processing section 102 through the network interface 106 such as a
modem (step 204). The processing section 102 determines whether the
user is an anomalous trichromat of a dichromat with reference to
the color vision characteristic descriptor 114 in the database 104.
If it is determined that the user is a dichromat, the processing
section 102 drives the dichromacy adaptation section 110, so that
the provided visual contents are adaptively transformed to be
suitable for the color vision characteristics of the user by using
the information on the color vision characteristics and/or
environment contained in the color vision characteristic descriptor
114, and then the transformed visual contents are displayed through
an image display device 108 such as a liquid crystal display device
(hereinafter, referred to as "LCD") or CRT. If the user is
determined as an anomalous trichromat, the processing section 102
drives the anomalous trichlomacy adaptation section 112, so that
the provided visual contents are adaptively transformed to be
suitable for the color vision characteristics of the user and
displayed through the image display device 108 (step 206).
[0042] In the Gazette of U.S. Pat. No. 6,362,830, a matrix [A']
displaying the color vision characteristics of humans having a
color vision deficiency is dimly derived. However, there is no
correct recognition of the problem of singularity for matrix [A'].
In the case of a dichromat, an inverse transform function of matrix
[A'] does not exist due to the problem of singularity for matrix
[A']. Therefore, it was impossible to try the adaptation using the
inverse transform function of the matrix [A'] in the Gazette of
U.S. Pat. No. 6,320,830. The present invention uses a differential
approaching method by differentiating an anomalous trichromat and a
dichromat in the process of the adaptation in consideration of the
fact that the inverse transformation function of matrix [A'] exists
in the case of an anomalous trichromat.
[0043] FIG. 3 is a structure view of a user color vision
characteristic descriptor in accordance with an embodiment of the
present invention. As shown ill FIG. 2 a user color vision
characteristic descriptor 300 comprises a user characteristic
descriptor 310 and a user environment element descriptor 320. The
user characteristic descriptor 310 contains a user identification
number (hereinafter, referred to as "ID") (311) for confirming the
user, a user name 312 for confirming the user name, information on
whether to open individual information 313 for protecting
individual information. In addition, the user characteristic
descriptor 310 includes a descriptor 314 for indicating the
eyesight of the user, a color vision deficiency presence descriptor
315 for describing whether the user has a color vision deficiency,
a color vision deficiency type descriptor 316 for describing the
user color vision deficiency type, and a color vision deficiency
degree descriptor 317 for describing a degree of deficiency for the
color vision. The user environment element descriptor 320 comprises
a user surrounding illumination degree descriptor 321.
[0044] The user characteristic descriptor 310 is described in Table
1 below. The dichromat is subdivided into red color blindness
(protanopy), green color blindness (deuteranopy) and blue color
blindness (tritanopy). For protanopy or deuteranopy that is the
most common among dichromats, the middle green of the spectrum is
seen as colorless or gray, the shorter wavelength side is seen as
blue and the longer wavelength side is seen as yellow. Therefore,
the colors visible from a monitor, a television set or the like are
shown in only the two colors of blue and yellow; it is difficult to
discriminate well a signal light. Contrary to this, trianopy is
extremely rate. With trianopy, every thing is seen in the two
colors of red and green; it is unexpectedly easy to discriminate a
signal light. Meanwhile, if all three types of cone cells do not
exist, it is called achromatopsia. In such a case, the eyesight is
very poor because all colors are seen as black or gray.
[0045] The anomalous trichromat is subdivided into protanomaly,
deuteranomaly, and tritanomaly. Protanomaly or deuteranomaly are
the most common among the anomalous trichromats who can see red and
green colors of varying degrees. The degrees of protanomaly or
deuteranomaly range from severe cases, in which protanomaly or
deuteranomaly are not different from protanopy or deuteranopy, to
very mild cases, in which protanomaly or deuteranomaly are close to
normality. Like the eyesight of humans, color vision deficiencies
widely differ in degree.
1 TABLE 1 Color Vision Deficiency Information Type Color Vision
Deficiency Degree Type Medical Color Vision Textual Numerical
Terminologies Deficiency Type Degree Type Degree Type Protanomaly
Red-deficiency Mild 0.1-0.9 Protanopy Red-deficiency Severe 1.0
Deuteranomaly Green-deficiency Mild 0.1-0.9 Deuteranopy
Green-deficiency Severe 1.0 Tritanomaly Blue-deficiency Mild
0.1-0.9 Tritanopy Blue-deficiency Severe 1.0 Achromatopsia Complete
Color Blindness NA NA
[0046] For example, in the case of protanomaly, in medical
terminology, the color vision deficiency type descriptor 319
indicates red deficiency, and the color vision deficiency degree
descriptor 316 is expressed as Mild (anomalous trichromacy) in
textural description and having a value from 0 to 0.9 in numerical
description in the case of anomalous trichromacy, and is expressed
as Severe (dichromat) in textural description and having a value of
1.0 in the case of dichromat. That is, the severity of color vision
deficiency degree may be described not only by normalized numerical
values, but also by textural description. The specific necessity of
such a description method will be described later.
[0047] The present invention provides three methods for enumerating
the severity of color vision deficiency degree. The first method
for enumerating the severity of color vision deficiency degree is
to measure abnormal elements inducing anomalous trichromacy and to
directly use the measured values. One of the abnormal elements
inducing anomalous trichromacy is the case in which the response
function of the corresponding cone cells is shifted from normal
position, and the other is the case in which the intensity of the
response value of the cone cells decreases. The severity of
anomalous trichromacy is determined by compositely combining the
above two phenomena. The procedures for enumerating the above two
cases are performed as expressed by Equation 1 and Equation 2,
respectively.
[0048] If the enumerated value of the shift of cone cells among the
LMS cone cells is Z, the value of Z is expressed like Equation 1.
If the maximum shift limit numerical value of medically verified
cone cells is .alpha..sub.max nanometers(nm), and the shifted value
of cone cells of the anomalous trichromacy is .alpha. nanometers,
the value of .alpha. may be ranged from 0.0 to .alpha..sub.max
nanometers. 1 Z = a max Equation 1
[0049] Here, if the shifted value of abnormal cone cells, .alpha.,
exceeds .alpha..sub.max or if the cone cells do not exist, it is
determined as dichromacy and the value of .alpha. is made to be
equal to the value of .alpha..sub.max. Therefore, the value of Z is
always 1.0 in the case of dichromacy.
[0050] In addition, the method for considering the case in which
the intensity of response value of abnormal cone cells among the
LMS cone cells decreases is performed in Equation 2. If the maximum
threshold numerical value of decrease of medically verified cone
cells is .beta..sub.max, and the decrease value of the cone cells
of anomalous trichromacy is .beta., the value of .beta. may be
ranged from 0.0 to the value of .beta..sub.max. As a result, the
value of 1 is normalized to have a value from 0.0 to 1.0 and is
determined by Equation 2. 2 I = max Equation 2
[0051] Here, if the decreased intensity of abnormal cone cells,
.beta., exceeds .beta..sub.max or if the cone cells do not exist,
it is determined as dichromat and the value of .beta. is made to be
equal to the value of .beta..sub.max. Therefore, the value of 1 is
always 1.0 in the case of dichromacy.
[0052] As a result, the two elements for determining the severity
of color vision deficiency degree can be enumerated by using
Equation 1 and Equation 2. The color vision deficiency is induced
medically through various combinations of the two elements.
Therefore, it is possible to more correctly reflect and enumerate
the severity of color vision deficiency degree of a human with a
color vision deficiency by giving a weighted value to Z, an
enumerated value of the shifted extent of abnormal cone cells, and
I, an enumerated value of decrease degree in the response intensity
of abnormal cone cells, respectively.
[0053] Therefore, the moving phenomena of cone cells is expressed
by Z.sub.w, in Equation 3, wherein Z.sub.w is obtained by the
product of Z and W.sub.z, in which Z is the value enumerated from
the shift of abnormal cone cells expressed by Equation 1 and
W.sub.z is a weighted value. 3 Z W = w Z .times. Z = w Z .times. a
max Equation 3
[0054] In addition, the decrease in the intensity of the response
value of the cone cells is also expressed by I.sub.w in Equation 4,
wherein I.sub.w is obtained by the product of 1 and W.sub.1, in
which I is the value enumerated from the decrease degree in the
intensity of the response value of abnormal cone cells expressed by
Equation 1 and W.sub.1 is a weighted value. 4 I W = w I .times. I =
w I .times. max Equation 4
[0055] As a result, in Equation 5, the severity of color vision
deficiency is obtained by combining the two elements of given
weighted values. 5 N = Z W + I W w Z max + w I max = w Z .times. Z
+ w I .times. I w Z max + w I max = { w Z .times. ( max ) + w I
.times. ( max ) } / ( w Z max + w I max ) Equation 5
[0056] Here, the value of N is a numerical value indicating the
degree of color vision and normalized from 0.0 to 1.0. The value of
N is obtained by adding the value obtained by the product of Z and
the weighted value, W.sub.z, to the value obtained by the product
of I and the weighted value, W.sub.1, and then normalizing the
resultant value from 0.0 to 1.0, wherein Z is the value enumerated
from the shifted extent, to which abnormal cone cells have moved to
other cone cells among the LMS cone cells of a human with a color
vision deficiency, and I is the value enumerated from the decrease
degree in 5 response intensity of the abnormal cone cells.
[0057] Because the peak values of Z and I are 1.0, the
normalization is executed by dividing the above resultant value by
the value obtained by adding W.sub.z.sup.max to W.sub.1.sup.max,
wherein W.sub.z.sup.max is the peak value of the weighted value,
W.sub.z, and W.sub.1.sup.max is the peak value of the weighted
value, W.sub.1. Finally, the numerical description value of color
vision 10 deficiency degree is obtained by moving the decimal point
one place to the right and cutting away lower fractions by one
half. Consequently, as shown in Table 1. the numerical description
value of color vision deficiency degree is 1.0 in the case of
dichromat. In the case of anomalous trichromat, the numerical
description value is in the range of 0.0 to 0.9.
[0058] The second and third methods for enumerating the degree of
color vision deficiency use the results of a color vision
deficiency test unlike the first method. The methods for testing
color vision deficiency are divided into pseudoisochromatic tests,
color arrangement tests, and color light tests. The most
representative testing method among the pseudoisochromatic tests is
the Ishihara test. This method is most generally used among the
testing methods because it is very easy and rapid. However, there
is a disadvantage in that it is difficult to test the degree of
color vision deficiency in detail.
[0059] The color arrangement tests have a disadvantage in that the
time required in testing is long and the analysis of color vision
deficiency is difficult when compared to the pseudoisochromatic
tests. However, the color arrangement tests have an advantage in
that it is possible to correctly test the type and degree of color
vision deficiency when compared to the pseudoisochromatic tests.
The most representative test among the color arrangement tests is
Farnsworth-Munsell (FM) hue test. Finally, there are anomaloscope
tests that use color light. These tests are known as being the most
capable in accurately examining red-green anomalous trichromat. In
particular, these tests easily subdivide the degree of color vision
deficiency.
[0060] In accordance with the second method of the present
invention, the present invention uses the FM hue test for
enumerating the degree of color vision deficiencies. The degree in
the severity of color vision deficiency is enumerated by using the
total error score (TES) acquired after the FM hue examination. The
degrees in the severity of color vision deficiencies are enumerated
from 0.1 to 1.0 in accordance with the total error score in
Equation 6: 6 N = { E - E min E max - E min , E min < E < E
max 1.0 , E th max Equation 6
[0061] Here, E is the total error score. If the total error score
is smaller than E.sub.min, it is determined that the subject is
normal without any color vision deficiency. If the total error
score is larger than E.sub.min, it is determined that the subject
has a color vision deficiency. If the total error is larger than
E.sub.min, but smaller than E.sub.max, it is determined that the
subject has an anomalous trichromat deficiency. In anomalous
trichromat deficiencies, the numerical value N of the color vision
deficiency degree is determined by the proportion occupied by the
total error score of the subject in the entire range of the total
error score. In this case, the numerical value N of the color
vision deficiency degree has a value from 0.1 to 0.9. These
numerical values are obtained by cutting away lower fractions by
one half and moving the decimal point two places to the right. And,
in the case of dichromat deficiencies, the numerical value N of the
color vision deficiency degree is always 1.0. FIG. 4 shows an
example of the methods for enumerating color vision deficiency
degrees using the FM hue test.
[0062] In accordance with the third method, the present invention
uses an anomaloscope for enumerating the color vision deficiency
degrees. Nowadays, anomaloscopes can be used only for examining
red-green anomalous trichromacy. The present invention enumerates
the color vision deficiency degree using a Nagel anomaloscope that
is the most representative anomaloscope. The Nagel anomaloscope
consists of two parts. The first part is a test field, in which a
pure yellow color is emitted, and the second part is a mixture
field, in which a red color and a green color are jointly emitted
and produce a yellow color. The Nagel anomaloscope is provided with
two adjustment devices: The first adjustment device is used to
adjust the illumination of the test field and the second adjustment
device is used to adjust the ratio of red to green in the mixture
field. The subject should adjust the colors emitted from the test
field and the mixture field to be identical, using the two
adjustment devices while viewing the anomaloscope with both eyes.
The examiner determines the degree of severity and the type of the
color vision deficiency by analyzing the values of the two
adjustment devices adjusted by the subject. The ratio of red to
green has a value from 0 to 73. 0 indicates a pure green color and
73 indicates a pure red color. The numerical range of 1 to 72
indicates a mixed color generated by adding red to green. The
proportion occupied by red in the mixed color increases as the
value decreases while the proportion occupied by green in the mixed
color increases as the value increases. The numerical value is
usually set to 43 before initiating the test and thus a yellow
color is generated in the mixture field. If the value of the
subject ranges from 40 to 45, the subject is determined as normal.
The degree in the severity of the color vision deficiency is
enumerated from 0.1 to 1.0 in Equation 7. 7 N = { R d R th , R d R
th 1.0 , R d > R th Equation 7
[0063] Here R.sub.d=R.sub.max-R.sub.min, 8 R th = { R min normal ,
green color vision deficiency 73 - R max normal , red color vision
deficiency
[0064] In Equation 7, R.sub.d indicates the range of the red/green
ratio section in the mixture field, which is recognized as
identical to the test field of the subject. That is, R.sub.d
indicates the distance between the minimum value, R.sub.min, and
the maximum value R.sub.max, in the red/green ratio section range.
The larger the value of R.sub.d, the more severe the degree of the
color vision deficiency. A normal human has the minimum value of
R.sup.normal and the maximum value of R.sup.normal.sub.max in the
red/green ratio section range. That is, the value of R.sub.d is the
value of (R.sup.normal.sub.max-R.sup.normal.sub.m- in). As a result
of performing the anomaloscope test, if the distance value,
R.sub.d, is smaller than the limit value, R.sub.th, it is
determined that the user has the deficiency of anomalous
trichromacy,; and if R.sub.d is larger than R.sub.th, it is
determined that the user has the deficiency of dichromacy. In
accordance with the types of color vision deficiencies, the limit
value, R.sub.th, varies. In the case of green-color vision
deficiency, the limit value R.sub.th equals R.sup.normal.sub.min
and in the case of red-color vision deficiency, the limit value
R.sub.th equals (73-R.sup.normal.sub.max0. Using these numerical
values, the numerical value of the color vision deficiency degree,
N, is determined by the ratio between R.sub.th and R.sub.d in the
case of anomalous trichromacy, wherein R.sub.th is the longest
distance in the red/green ratio section range in which the color
vision deficiency is determined as dichromacy, and R.sub.d is the
distance within the red/green ratio section range of the subject.
In this case, the numerical value of the color vision deficiency
degree, N, has a value from 0.1 to 0.9. These values are obtained
by cutting away lower fractions by one half moving the decimal
point two places to the right. In the case of dichromacy, the color
vision deficiency degree, N, is always 1.0. FIG. 5 shows an example
of the method for enumerating the color vision deficiency degree
using the results of the anomaloscope test. Following Table 2 is an
example of the color vision deficiency descriptor prepared in a XML
document, in which the descriptor has the structure shown in FIG.
3.
2TABLE 2a <!-- #################################- ##########
--> <!-- Definition of VisualImpairmentType --> <!--
########################################### --> <complexType
name="VisualImpairmentType"> <sequence> <element
name="ColorVisionDeficiency" type="ColorVisionDeficiencyType"
minOccurs="0"/> </sequence> <attribute
name="ColorVisionDeficiencyOrN- ot" type="boolean"
use="required"/> </complexType>
[0065]
3 TABLE 2b <!-- #############################- ##############
--> <!-- Definition of ColorVisionDeficiency --> <!--
########################################### --> <complexType
name="ColorVisionDeficiencyType"> <sequence> <element
name="ColorVisionDefici- encyType"
type="ColorVisionDeficiencyTypeType"/> <element
name="ColorVisionDeficiencyDegree"
type="ColorVisionDeficiencyDegreeType"/> </sequence>
<attribute name="Sight" type="float" use="optional"/>
<attribute name="IlluminanceDegree" type="float" use=
"optional"/> </complexType> <simpleType
name="ColorVisionDeficiencyTy- peType"> <restriction
base="string"> <enumeration value="Red-Deficiency"/>
<enumeration value="Green-Deficiency"/> <enumeration
value="Blue-Deficiency"/> <enumeration
value="CompleteColorBlindness"/> </restriction>
</simpleType> <complexType
name="ColorVisionDeficiencyDegreeType"> <choice>
<element name="NumericDegree" type= "mpeg7:zeroToOneType"/>
<element name="TextualDegree"> <simpleType>
<restriction base="string"> <enumeration
value="Severe"/> <enumeration value="Mild"/>
</restriction> </simpleType> </element>
</choice> </complexType>
[0066] FIG. 6 is a detailed flowchart of the adaptation step (step
206) shown in FIG. 2.
[0067] As shown in FIG. 6, the color vision deficiency degree of
the user is determined from the color vision characteristic
descriptor as described above (step 402). If the user is determined
as an anomalous trichromat as the result of the determination, an
adaptation process is executed for such an anomalous trichromat
(step 404). If the user Is determined as a dichromat, a separate
adaptation process is executed for such a dichromat (step 406). If
the textural description 317 of the color vision degree descriptor
316 is "Severe" (dichromat) or a numerical description 318 is 1.0
in FIG. 3, the user is a dichromat among the color vision
deficiencies, and thus the adaptation process is performed.
Whereas, if the textural description 3 17 of the color vision
deficiency of the color vision deficiency degree descriptor 316 is
"Mild" (anomalous trichromacy) or the numerical description 318 is
0-0.9, the user is an anomalous trichromat among the color vision
deficiencies, and thus the adaptation process for the anomalous
trichromat is performed.
[0068] FIG. 7 is a detailed flowchart of an example of the
adaptation process for an anomalous trichromat (step 404). At
first, an LMS response function expressing the vision
characteristics of the user, who is an anomalous trichromat, is
obtained (step 502). The method for obtaining the LMS response
function will be specifically described below. Next, the externally
inputted visual contents are transformed from the RGB color space
to the LMS color space (step 504). Then, the inputted visual
contents are transformed using the inverse function of the user LMS
response function (step 506), and the visual contents transformed
in this manner in the LMS space is transformed to the RGB color
space again (step 508).
[0069] Next, the principle of the adaptation method of an anomalous
trichromat in accordance with the present invention is specifically
described with reference to FIGS. 8 to 11. FIG. 8 shows the
spectral sensitivity of the LMS cone cells for the visible
wavelengths of a normal human.
[0070] FIG. 9 shows the RGB emission curves of a CRT monitor with
P22 phosphor. As described above, a human discriminates colors by
visual cells in the eyes that recognize light reflected from an
object. However, when a human recognizes colors through an image
display device, unlike the case in which a human recognizes colors
by directly viewing the object, the colors are recognized
differently due to the characteristics of the image display device
and the characteristics of each individual's eyes. Therefore, in
order to allow the human to accurately grasp the finally recognized
colors, the characteristics of the spectral emission function of
the corresponding image display device should be considered. In
general, the characteristics of the spectral emission function of
an image display device can be measured by using a
spectroradiometer, in which those characteristics appear
differently in accordance with the characteristics and the types of
image display devices. In this embodiment, the characteristics of
the RGB emission function of a CRT monitor with P22 phosphor are
measured using a spectroradiometer.
[0071] FIG. 10 expresses stimuli in the LMS color space. The colors
measured with a spectroradiometer are not equal to those recognized
by a human. The former is merely a physical measurement of colors.
The colors finally recognized by a human are a result of a
composite reaction between the LMS characteristics of cone cells
and the RGB characteristics of an image display device. The colors
emitted from the image display device are transformed and
recognized in accordance with the characteristics of the three
types of cone cells. FIG. 10 expresses each of the RGB values
recognized by the three types of cone cells on LMS orthogonal
coordinate system. All colors are recognized, using an image
display device, are present in the hexahedron formed by the points
ORYGBMWC.
[0072] The LMS values (L.sub.Q, M.sub.Q, S.sub.Q) of an optional
stimulus Q can be transformed by a transformation matrix that is
obtained by integrating the LMS function of cone cells (FIG. 8) and
the RGB spectrum emission curves measured with a spectroradiometer
(FIG. 9) in accordance with each wavelength. The equation for
obtaining the LMS transformation matrix of a normal human,
T.sub.normal, is expressed in Equation 8 below. 9 [ L M S ] = T
normal [ R G B ] , T normal = [ L normal r L normal g L normal b M
normal r M normal g M normal b S normal r S normal g S normal b ] ,
where ( L r = k l E r ( ) L ( ) L g = k l E g ( ) L ( ) L b = k l E
b ( ) L ( ) M r = k m E r ( ) M ( ) M g = k m E g ( ) M ( ) M b = k
m E b ( ) M ( ) S r = k s E r ( ) S ( ) S g = k s E g ( ) S ( ) S b
= k s E b ( ) S ( ) ) . Equation 8
[0073] In Equation 8, E.sub.r(.lambda.), E.sub.g(.lambda.), and
E.sub.b(.lambda.) indicate spectrum powers emitted by an image
display device at a wavelength (.lambda.) in connection with R, G,
and B stimuli, respectively, and L(.lambda.), M(.lambda.), and
S(.lambda.) indicate spectral response values absorbed by cone
cells at the wavelength (.lambda.). The maximum emission value of
each phosphor in an image display device forms a neutral LMS
response value. Each neutral response value should have an ideal
emission function characteristic in order to form a white point. If
an image display device has such an ideal condition, the K value is
selected to satisfy .SIGMA. L=.SIGMA.M=.SIGMA.S=1.
[0074] FIG. 11 shows the spectral sensitivity of protanomaly, in
which the peak sensitivity of cone cells is shifted about 10 nm.
Unlike dichromacy, anomalous trichromacy is the state in which all
three types of cone cells exist, but they do not exert normal
function. Because the difference in accordance with the degree of
anomalous trichromacy is varied, unlike that of dichromacy, it is
very difficult to accurately express the colors recognized by an
anomalous trichromat. However, in accordance with several papers
studying eyesight, in the case of anomalous trichromacy, it is
assumed that the peak sensitivity of the LMS cone cells is shifted
by a certain wavelength. Because the L-cone cell is shifted in
protanomaly, the M-cone cells are shifted in deuteranomaly, and the
S-cone cells are shifted in tritanomaly, two types of cone cells
are more overlapped than those in a normal human. Therefore, an
anomalous trichromat lacks the capability of discriminating colors
when compared to a normal human. FIG. 11 shows the spectral
sensitivity of protanomaly, in which the peak sensitivity of the
L-cone cells is shifted about 10 nm.
[0075] Unlike the simulation of dichromacy, color simulation
recognized by an anomalous trichromacy can be directly obtained by
the transformation matrix that transforms light emitted from an
image display device into the colors recognized by defected cone
cells of an anomalous trichromat. Transformation matrixes are
obtained in accordance with the type of anomalous trichromacy;
protanomaly is given the transformation matrix T.sup.L.sub.abnormal
in Equation 9, deuteranomaly is given the transformation matrix
T.sup.M.sub.abnormal in Equation 10, and tritanomaly is given with
the transformation matrix T.sup.S.sub.abnormal in Equation 11. That
is, it is possible to obtain direct transformation matrixes by
applying an LMS response function in deformed cone cells of an
anomalous trichromat instead of an LMS response function of a
normal human to Equation 8.
[0076] However, for such an approach, the enumeration for the LMS
transformation matrix T.sub.abnormal of a human with a color vision
deficiency should precede. As apparent in FIG. 8, in order to
enumerate T.sub.abnormal, it is necessary to know the spectral
response functions L'(.lambda.), M'(.lambda.), and S'(.lambda.) in
cone cells of a human with a color vision deficiency along with the
characteristics E.sub.r(.lambda.), E.sub.g(.lambda.), and
E.sub.b(.lambda.) of the display. However, an important problem in
practice is how to obtain L'(.lambda.), M'(.lambda.), and
S'(.lambda.). Even if it becomes possible to measure those
characteristics by an expert, a problem still remains in that a
method should be devised for inputting the measured data into an
adaptation system for use in an adaptation.
[0077] As described in reference to Equation 1 through Equation 5,
the present invention proposes a method for expressing the degree
of anomalous trichromacy with simple numerical values by modeling
the mechanism of anomalous trichromacy in consideration of the
spectral transition of LMS cone cells and the variation of the
response intensity. The simplified numerical values for the degree
of anomalous trichromacy are very effectively used to approximate
the spectral response functions L'(.lambda.), M'(.lambda.), and
S'(.lambda.) of the cone cells of anomalous trichromats together
with the information on the types of anomalous trichromacy. Through
these procedures, it becomes possible to enumerate T.sub.abnormal
and thus to very easily and effectively express color vision
deficiency of an anomalous trichromat for the first time.
[0078] Here, the response functions of defected cone cells of an
anomalous trichromat include the cases in which one type of LMS
cone cells is shifted toward any other type of cone cell by several
nm to tens of nm and in which the response degree of the LMS cone
cells decreases.
[0079] The original color image information i.e. (R, G, B) is
directly transformed to (L', M', S') in the LMS space by using an
LMS transformation matrix of each anomalous trichromat, and in the
transformation procedure, protanomaly is expressed in Equation 9,
deuteranomaly is expressed in Equation 10, and tritanomaly is
expressed in Equation 11. 10 [ L ' M ' S ' ] = T abnormal L [ R G B
] , T abnormal L = [ L abnormal r L abnormal g L abnormal b M
normal r M normal g M normal b S normal r S normal g S normal b ]
Equation 9 [ L ' M ' S ' ] = T abnormal M [ R G B ] , T abnormal M
= [ L normal r L normal g L normal b M abnormal r M abnormal g M
abnormal b S normal r S normal g S normal b ] Equation 10 [ L ' M '
S ' ] = T abnormal S [ R G B ] , T abnormal S = [ L normal r L
normal g L normal b M normal r M normal g M normal b S abnormal r S
abnormal g S abnormal b ] Equation 11
[0080] A color stimulus value transformed to (L', M4', S') in the
LMS space is transformed again by an LMS inverse transformation
matrix in a normal human in Equation 12, whereby it is possible to
obtain the colors in RGB values practically recognized by an
anomalous trichromat. By this method, it is possible to simulate
the colors seen by anomalous trichromats, in Equation 12, so that
normal humans are capable of seeing the colors. At first, the
original color information, i.e. (R, G, B) is transformed to (L',
M', S') using the LMS transformation matrix of anomalous
trichromats in Equation 12(1), and then the transformed (L', M',
S') is transformed to (R.sub.simulate, G.sub.simulated,
B.sub.simulated) which is recognized by anomalous trichromats by
multiplying the transformed (L', M', S') by the LMS inverse
transformation matrix in normal humans, thereby executing the
simulation. If Equation 12(1) and Equation 12(2) are combined, it
is possible to execute color simulation for anomalous trichromats
using Equation 12(3). In general, the colors simulated for
anomalous trichromats, in Equation 12(4), are not identical to the
original colors. The more severe the degree of anomalous
trichromacy, the greater the difference between the simulated
colors and the original colors. 11 Equation 12 [ L ' M ' S ' ] = [
T abnormal ] [ R G B ] ( 1 ) [ R simulated G simulated B simulated
] = [ T normal ] - 1 [ L ' M ' S ' ] ( 2 ) [ R simulated G
simulated B simulated ] = [ T normal ] - 1 [ T abnormal ] [ R G B ]
( 3 ) [ R simulated G simulated B simulated ] [ R G B ] ( 4 )
[0081] An adaptation process for anomalous trichromats is performed
in such a manner that the color discriminating capability of
anomalous trichromats is further enhanced by emphasizing the
brightness and saturation of a color, which is difficult for an
anomalous trichromat with a given anomalous trichlomacy type to
discriminate, to be more intense than normal ones. That is, this is
a method to compensate for the decrease in the color discrimination
capability of an anomalous trichromat with a given anomalous
trichromacy type due to shifted cone cells, and is expressed in
Equation 13. Specifically, the adaptively transformed colors, i.e.
(R.sub.adapted, G.sub.adapted, B.sub.adapted) are first obtained by
multiplying the original colors (R, G, B) by the adaptation matrix
[A] in Equation 13(1). Here, the adaptation matrix [A] is applied,
so that the result of simulating the adaptively transformed colors
(R.sub.adapted, G.sub.adapted, B.sub.adapted) to the colors
(R.sub.simulated, G.sub.simulated, B.sub.simulated), which are
recognized by the anomalous trichromats, are equal to the original
colors (R, G, B) in Equation 13(2). 12 Equation 13 [ R adapted G
adapted B adapted ] = [ A ] [ R G B ] ( 1 ) [ R simulated G
simulated B simulated ] = [ T normal ] - 1 [ T abnormal ] [ R
adapted G adapted B adapted ] = [ R G B ] ( 2 )
[0082] That is, the goal of the contents adaptation for anomalous
trichromats is to adaptively transform the RGB colors of the
original contents, so that a corresponding type of anomalous
trichromat can see the contents as a normal human sees the
contents. Here, the contents adaptive matrix [A] for anomalous
trichromats can be expressed in Equation 14 below. Although the
adaptively transformed contents may be very factitious to normal
humans, anomalous trichromats can see the adaptively transformed
contents at the same or approximate level as normal humans see the
original contents.
A=[T.sub.abnormal].sup.-1.multidot.[T.sub.normal] Equation 14
[0083] FIG. 12 is a detailed flowchart of an example of the
adaptation process for dichromacy shown in FIG. 6. As shown in the
drawing, a deficiency region, which is difficult for the user to
detect, is first discriminated in accordance with the color vision
deficiency type extracted from the color vision characteristic
descriptor (step 1002). Next, at least one of hue, saturation or
intensity of the pixels corresponding to the deficiency region is
tuned (step 1004). Thereby, the visual contents are adaptively
transformed to be suitable for the color vision characteristics of
a user with the deficiency of dichromacy. The specific
transformation process is described in detail below.
[0084] FIG. 13 is a view displaying the color spaces recognized by
a human wraith the deficiency of dichromacy, in which FIG. 13a
showers that for protanopy or deuteranopy and FIG. 13b shows that
for tritanopy. To express the colors recognized by humans with
color vision deficiencies is essential for an adaptation process
for a color vision deficiency. Several papers have already verified
simulation processes of expressing colors recognized by dichromats.
Humans with the deficiency of protanlopy or deuteranopy recognize a
color of short wavelength as blue and a color of long wavelength as
yellow. Therefore, the colors for humans with the deficiency of
protanopy or deuteranopy can be expressed by two colors with
various degrees of intensity and saturation. Although it is very
seldom, humans with the deficiency of tritanopy recognize a color
of short wavelength as cyan and a color of long wavelength as red.
Therefore, the colors for humans with the deficiency of tritanopy
can also be expressed by two colors with various degrees of
intensity and saturation. These two colors will be seen as
identical colors for both humans with color vision deficiencies and
normal humans. Medically, it is possible to assume that these two
colors are blue of 475 nm and yellow of 575 nm for protanopy or
deuteranopy and cyan of 485 nm and red of 660 nm for tritanopy.
[0085] FIG. 13 expresses the colors recognized by humans with the
deficiency of dichromacy. In FIG. 13, point E (L.sub.E, M.sub.E,
S.sub.E) is the brightest metamer among the equal-energy stimuli of
a corresponding image display device. Therefore, OE indicates
neutral stimuli equally recognized by a normal human and a
dichromat. Two limited stimulus planes are formed centering on
these stimuli. In other words, these planes form two unchangeable
colors for given dichromat types. A certain color stimulus Q in the
LMS space is substituted with a color on the two planes in
accordance with the wavelength thereof. In FIG. 13, the color
stimuli of points P.sub.1 and P.sub.2 of protanopy are all
substituted with the color stimulus of point P, and the color
stimuli of points D.sub.1 and D.sub.2 of deuteranopy are all
substituted with the color stimulus of point D. Similarly, the
color stimuli of points T.sub.1 and T.sub.2 of tritanopy are all
substituted with the color stimulus of point T.
[0086] It is assumed that the color stimulus of dichromats
substituted from the certain stimulus, Q, is
Q'(L.sub.Q,M.sub.Q,S.sub.Q). And, it is assumed that the color
stimulus forming the two unchangeable color planes is
A(L.sub.A,M.sub.A,S.sub.A). The substituted Q' value is always
orthogonal to a plane formed by normal vectors. Therefore, the Q'
can be expressed in Equation 15. In addition, Equation 15 can be
expressed by the lineal equations of L.sub.Q', M.sub.Q', S.sub.Q'
values in Equation 16.
(E.times.A).multidot.Q'=0 Equation 15
aL.sub.Q'+bM.sub.Q'+cS.sub.Q'=0 Equation 16
[0087] Here,
a=M.sub.ES.sub.A-S.sub.EM.sub.A, b=S.sub.EL.sub.AL.sub.ES.sub.A,
c=L.sub.EM.sub.A-M.sub.EL.sub.A
[0088] Therefore, the transformation equations from stimulus Q to
Q' are finally expressed in Equation 17 (for protanopy), Equation
18 (deuteranopy), and Equation 19 (tritanopy). 13 [ L Q ' M Q ' S Q
' ] = [ 0 - b a - c a 0 1 0 0 0 1 ] [ L M S ] = [ L p M p S p ]
Here , A = { 575 nm , SQ / MQ < SE / ME 475 nm , otherwise
Equation 17 [ L Q ' M Q ' S Q ' ] = [ 1 0 0 - a b 0 - c b 0 0 1 ] [
L M S ] = [ L d M d S d ] Here , A = { 575 nm , SQ / LQ < SE /
LE 475 nm , otherwise Equation 18 [ L Q ' M Q ' S Q ' ] = [ 1 0 0 -
a b 0 - c b 0 0 1 ] [ L M S ] = [ L d M d S d ] Here , A = { 660 nm
, MQ / LQ < ME / LE 485 nm , otherwise Equation 19
[0089] FIG. 14 is a detailed flowchart of an example of the method
for discriminating a deficiency region in FIG. 12. As shown in FIG.
14, the visual contents are first transformed from the RGB color
space to the CMYK color space in Equation 20 (step 1202). Next, a
region to be adaptively transformed is determined (step 1204). This
is executed by discriminating the pixels corresponding to a
predetermined region of the CMYK in accordance with the color
vision deficiency type. In the case of protanopy or deuteranopy,
the deficiency region is determined in Equation 21 and in the case
of tritanopy, the deficiency is determined in Equation 22. 14 [ C M
Y ] = [ c m y ] - K Equation 20
[0090] Here, c, m, y are values obtained as the complements of R,
G, B, respectively, and are indicated as follows: 15 [ c m y ] = 1
- [ R G B ]
[0091] In addition, K indicates the minimum value in (c, m, y). The
color deficiency regions R.sub.adaptaion(x,y) for protanopy or
deuteranopy distributed in the space are detected in Equation 21.
16 R adaptation ( x , y ) = { 1 , when M ( x , y ) Th 1 0 ,
otherwise Equation 21
[0092] Here, (x, y) indicates positions of pixels in an image.
M(x,y) indicates magenta values distributed in the space. Th.sub.1
indicates the threshold of values determined as magenta. In the
case of tritanopy, the color deficiency region
R.sub.adaptation(x,y) is detected as follows: 17 R adaptation ( x ,
y ) = { 1 , when Y ( x , y ) Th 2 0 , otherwise Equation 22
[0093] Here, Y(x,y) indicates the yellow values distributed in the
space. Th.sub.2 indicates the threshold of the yellow values for
finding a blue that is the complementary of yellow using the yellow
values.
[0094] The adaptation processes for dichromats are divided into an
adaptation process for protanopy or deuteranopy and an adaptation
process for tritanopy. Humans with protanopy or deuteranopy see all
of the colors viewed through an image display device as blue or
yellow. That is, the red of long wavelength in the red color region
is seen as yellow and the red of short wavelength is seen as blue.
Similarly, the green of long wavelength in the green color region
is seen as yellow and the green of short wavelength is seen as
blue. Therefore, the goal of the adaptation of dichromat is to find
the red color and the green color regions that are
indistinguishable by humans with protanopy or deuteranopy and to
make those regions distinguishable. If only one, either red or
green is changed into a color that is distinguishable by humans
with a deficiency of protanopy or deuteranopy, the two colors are
made to be distinguishable. In general, the pixels of the visual
contents consist of three values, RGB (Red, Green, Blue), and these
values have hue, saturation, and intensity. Therefore, the inherent
color of the pixels is just hue. Even if the pixels have a same
hue, they are expressed differently by the intensity or
saturation.
[0095] In the process of contents adaptation for dichromats, the
HSI (Hue, Saturation, Intensity) color space is used in order to
tune the hues and intensities of colors. The HSI color space is
known to be useful to divide an object of an image. Therefore, the
adaptation process is performed in such a manner that the RGB
colors are transformed into the HSI color space to obtain object
information on an image, and the colors indistinguishable by the
dichromats are changed.
[0096] FIG. 15a indicates the hues (1302) of colors recognized by
normal humans. Here, .THETA. means a hue angle, and red R is
distributed to 360.degree., in the counterclockwise direction in
reference to 0.degree.. Typically, yellow (Y) is positioned at the
point of 60.degree., green (G) is positioned at the point of
120.degree., cyan is positioned at the point of 180.degree., blue
is positioned at the point of 240.degree. and magenta (M) is
positioned at the point of 300.degree..
[0097] However, unlike normal humans, dichromats recognize all
colors recognized by normal humans as two hues. FIG. 15b indicates
the hues (1304) recognized by protanopy or deuteranopy. FIG. 15c
indicates the hues (1306) recognized by tritanopy. That is,
dichromats discriminate colors based on the difference in
saturation and intensity because they are able to recognize only
two hues. As a result, dichromats have extremely poor capability
for recognizing information from colors of an image.
[0098] FIG. 16 shows a simulation of the hues recognized by
protanopy, deuteranopy and tritanopy in comparison to the hues,
from 0.degree. to 360.degree., recognized by normal humans. In FIG.
16, the horizontal axis indicates the hue angles from 0.degree. to
360.degree. and the vertical axis indicates the hue values obtained
by normalizing the hues from 0.degree. to 360.degree. to have
values in the range of 0.0 to 1.0. As shown in FIG. 16, the hues
recognized by protanopy, deuteranopy and tritanopy are divided into
two types of hues.
[0099] FIG. 17 is a detailed flowchart of another example for the
method of discriminating a deficiency region in FIG. 12. As shown
in FIG. 17, the pixels of the inputted visual contents are first
transformed from the RGB color space to the LMS color space (step
1502). Next, the LMS values are transformed to the limited LMS
space of a user with a deficiency of dichromacy (step 1504). Then,
the L-value decrease region is detected in the case of protanopy,
the M-value decrease region is detected in the case of deuteranopy,
and the S-value decrease region is detected in the case of
tritanopy (step 1506). It is also possible to detect a color
deficiency region R.sub.adaptation(x,y) by this method.
[0100] After detecting the color deficiency region
R.sub.adaptation(x,y), the color correction is performed in the
detected color deficiency region as follows. FIG. 18 is a detailed
flowchart of an example of the HSI tuning method in FIG. 12. As
shown in FIG. 18, the RGB values of the pixels corresponding to the
detected deficiency region are first transformed into HSI values in
Equation 23, and then the HSI values are corrected in Equation 24
(step 1602). Then, the corrected HSI values are transformed into
RGB values again in Equation 25 (step 1604). 18 [ R ( x , y ) G ( x
, y ) B ( x , y ) ] [ H ( x , y ) S ( x , y ) I ( x , y ) ] ,
Equation 23
[0101] Here, H, S, I values are normalized values in the range of
0.0 to 1.0. 19 [ H ' ( x , y ) S ' ( x , y ) I ' ( x , y ) ] = [ H
( x , y ) S ( x , y ) I ( x , y ) ] + R adaptation ( x , y )
.times. [ h s i ] , Equation 24
[0102] Here, h, s, i values are adaptively transformed values in
the range of 0.0 to 1.0. 20 [ H ' ( x , y ) S ' ( x , y ) I ' ( x ,
y ) ] [ R ' ( x , y ) G ' ( x , y ) B ' ( x , y ) ] Equation 25
[0103] Another method for adaptively transforming colors in
accordance with the present invention is to determine the
deficiency region and deficiency degree at the same time by using
proportions of cyan, magenta, and yellow instead of detecting the
deficiency region in Equation 21 and Equation 22. Protanopy or
deuteranopy is expressed in Equation 26 and tritanopy is expressed
in Equation 27. In this case, R.sub.adaptation(x, y) is always 1
and the deficiency region and deficiency degree are determined with
(h, s, i) at the same time.
[0104] (1) hue adaptive transformation: 21 h = { 0 , H blue region
max .times. M ( x , y ) , otherwise
[0105] (2) saturation adaptive transformation:
s=.alpha..sub.1.times.M)x,y)+.alpha..sub.2.times.C(x,y)
[0106] Here, M (x, y) indicates the magenta values distributed in
the space and C (x, 5) indicates the cyan values distributed in the
space. In Equation 26, h is the amount of change in hue for
protanopy or deuteranopy and s is the amount of change in
saturation for protanopy or deuteranopy. In the hue adaptation, if
the hues of the original pixels are included in the blue region,
the hue adaptation is not performed. The blue region is excluded
from the object for the hue adaptation because the region is
normally recognizable by protanopy or deuteranopy. (.THETA..sub.max
is the maximum value of the amount of change in hue, which means
the maximum angle that the hue angle can move. Here, .alpha..sub.1
and .alpha..sub.2 are the maximum amounts of change in saturation
using the magenta ratio and the cyan ratio and have values in the
range of 0.0 to 1.0.
[0107] In the hue and saturation adaptation for dichromats, the
magenta ratio, the cyan ratio and the yellow ratio are used in
Equation 26. The magenta, cyan and yellow ratios are values
obtained by transforming RGB values of pixels into values in the
CMYK color space and normalizing the transformed CMY values to have
values in the range of 0.0 to 1.0; and the magenta, cyan and yellow
ratios indicate the proportions of magenta, cyan and yellow
components contained in corresponding pixels, respectively.
[0108] FIG. 19a, 19b and 19c indicate a magenta ratio 1702, a cyan
ratio 1704 and a yellow ratio 1706 in color distribution,
respectively. First, the magenta ratio 1702 has the maximum value
for the product in saturation with intensity for a hue angle in the
range of 240.degree. to 360.degree.. For example, if both
saturation and intensity have the maximum values, that is, if both
the hue value and intensity value are 1.0, the magenta ratio is
1.0, that is, the product of the saturation value 1.0 multiplied by
the intensity value 1.0. In another example, if the saturation
value is 0.5 and the intensity value is 0.5, the magenta ratio is
0.25, that is, the product of the saturation value 0.5 multiplied
by the intensity value 0.5. Furthermore, the magenta ratio is
always 0 for a hue angle in the range of 60.degree. to 180.degree..
For a hue angle in the range of 0.degree. to 60.degree., the
magenta ratio linearly decreases from the maximum magenta ratio
wraith a hue angle of 0.degree. to the minimum magenta ratio with a
hue angle of 60.degree.. For a hue angle in the range of
180.degree. to 240.degree., the magenta ratio linearly increases
form the minimum magenta ratio with a hue angle of 180.degree. to
the maximum magenta ratio with an angle of 240.degree..
[0109] The cyan ratio 1704 has the maximum value of the product of
saturation multiplied by intensity for a hue angle in the range of
120.degree. to 240.degree.. In addition, the cyan ratio is always 0
for a hue angle in the range of 0.degree. to 60.degree. and for a
hue angle in the range of 300.degree. to 360.degree.. For a hue
angle in the range of 60.degree. to 120.degree., the cyan ratio
linearly increases from the minimum cyan ratio with a hue angle of
60.degree. to the maximum cyan ratio with a hue angle of
120.degree.. For a hue angle in the range of 240.degree. to
300.degree., the cyan ratio linearly decreases from the maximum
cyan ratio with a hue angle of 240.degree. to the minimum cyan
ratio with a hue angle of 300.degree..
[0110] The yellow ratio 1706 has the maximum ratio of the product
of saturation multiplied by intensity for a hue angle in the range
of 0.degree. to 120.degree.. In addition, the yellow ratio is
always 0 for a hue angle in the range of 180.degree. to
300.degree.. For a hue angle in the range of 120.degree. to
180.degree., the yellow ratio linearly decreases from the maximum
yellow ratio with a hue angle of 120.degree. to the minimum yellow
ratio with a hue angle of 180.degree.. For a hue angle in the range
of 300.degree. to 360.degree., the cyan ratio linearly increases
from the minimum yellow ratio with a hue angle of 300.degree. to
the maximum cyan ratio with a hue angle of 360.degree..
[0111] The magenta ratio is used in the process of hue adaptation
for protanopy or deuteranopy due to the following reasons. The
first reason is to exclude the yellow region normally
distinguishable by protanopy or deuteranopy from the objects of hue
adaptation. The second reason is to simultaneously adaptively
transform not only the red region indistinguishable from green, but
also the magenta region indistinguishable from blue. The third
reason is to gradually change the hue because an abrupt
transformation of the hue may deteriorate the quality of an image.
The fourth reason to use the magenta ratio in the process of
saturation adaptation for protanopy or deuteranopy is to provide a
difference in saturation as a measure for differentiating the color
changed to blue after the adaptation from the original blue. The
fifth reason to use the cyan ratio is to provide a difference in
saturation as a measure for differentiating the green region seen
as yellow to protanopy or deuteranopy, from the original yellow
region.
[0112] Unlike protanopy or deuteranopy, tritanopy has a principle
problem in that a blue (adjacent to violet) is recognized as red
and thus indistinguishable from the original red. Tritanopy
normally recognizes blue green (cyan) and red only. Therefore, if
hue angle of pixels of original image is included in the blue green
region when using a method similar to that used for protanopy or
deuteranopy, the hue adaptation is not performed. In general, the
hue angle of 165.degree. to 195.degree. is used as the blue green
angle.
[0113] (1) hue adaptive transformation: 22 h = { 0 , H cyan region
max .times. Y ' ( x , y ) , otherwise
[0114] (2) saturation adaptive transformation:
s=.beta..sub.1.times.Y'(x,y)+.beta..sub.2.times.M'(x,y)
[0115] Here, Y' (x, y) indicates the yellow component in the color
changed by H', that is, the H value of the original color plus 0.5,
and M' (x, y) indicates the magenta value in the color changed to
the HSI value. In the Equation 27, h and s are the amount of change
in hue and the amount of saturation for tritanopy, respectively.
.THETA..sub.max is the maximum value of the amount of change in
hue, which means the maximum angle that the hue angle can move. In
the process of adaptation for tritanopy, the blue ratio and the
green ratio are used; and in order to use these ratios, the yellow
ratio that is the complementary color ratio of the blue ratio, and
the magenta ratio that is the complementary ratio of the green
ratio, are used instead of the blue and green ratios. Here,
.beta..sub.1 and .beta..sub.2 are the maximum amounts of change in
saturation using the blue ratio and green ratio and have values in
the range of 0.0 to 1.0. The blue ratio is used in the process of
hue adaptation for tritanopy in order to exclude the red region
from the object to be adaptively transformed, and if possible, the
yellow ratio complementary to blue is used in order to obtain the
blue ratio.
[0116] In the process of hue adaptation for tritanopy, the green
ratio is also used beyond the blue ratio. The yellow ratio,
complementary to blue ratio, is used to obtain the blue ratio; and
the magenta ratio, complementary to green ratio, is used to obtain
the green ratio. The reason to use the blue ratio is to provide a
difference in saturation between the colors changed to red after
the adaptation and the original red, thereby differentiating these
two colors. The reason to use the green ratio is to provide a
difference in saturation in order to differentiate the green
region, seen as blue green to tritanopy, from the original blue
green region.
[0117] Table 3 below is a color table of adaptation for dichromats
in accordance with the present embodiment.
4TABLE 3 Type of Indistinguishable Recognizable Adaptively
Dichromacy Color Color Transformed Color Protanopy or Red and Green
Yellow Red .fwdarw. Magenta Deuteranopy Tritanopy Blue and Yellow
Red Blue .fwdarw. Green
[0118] The embodiments described above are not intended to limit
the scope of the present invention, but merely provided for those
who skilled in the art to readily understand and embody the present
invention. Therefore, it should be appreciated that various
modification and change can be made within the scope of the present
invention. In principle, the scope of the present invention is
determined by the accompanying claims.
INDUSTRIAL APPLICABILITY
[0119] In accordance with the present invention as described above,
a user with a color vision deficiency is able to receive semantic
information that is almost the same as that of a normal human from
visual contents without a separate apparatus. As a result, the user
with a color vision deficiency can freely and conveniently use
multimedia contents. In addition, the present invention is
applicable to the digital item adaptive parts of MPEG-7 and MPEG-21
that are international standards in media.
* * * * *