KR20180073296A - Display device and method for driving the same - Google Patents

Abstract

According to an embodiment of the present invention, provided is a display device which comprises: a color vision abnormality inspection unit which provides inspection image data for inspecting the color vision characteristics of a user and generates abnormality measurement data corresponding to the color vision characteristics of the user according to a user input corresponding to the inspection image data; a data modulation unit which modulates RGB data of a color image on the basis of the abnormality measurement data; and a pixel driving unit which controls brightness of each of a plurality of pixels of a display panel on the basis of the modulated RGB data. The display device can provide an image customized for each person having color weakness by modulating an image with an algorithm selected according to the color vision characteristics of each person having color weakness and the image characteristics of a color image.

Description

DISPLAY APPARATUS AND METHOD FOR DRIVING THE SAME

The present invention relates to an apparatus for displaying a color image and a driving method thereof.

The flat display device is applied to various electronic devices such as a TV, a mobile phone, a notebook, and a tablet. To this end, research has been continued to develop a thinner, lighter, and lower power consumption display device.

Typical examples of the flat panel display include a liquid crystal display (LCD), a plasma display panel (PDP), a field emission display (FED), an electroluminescent display An electroluminescence display device (ELD), an electro-wetting display device (EWD), and an organic light emitting display device (OLED).

On the other hand, when the display device displays a color image, the color weakness may be difficult to identify the image data as compared with a normal person. That is, the color weakness of the color weakness is lower than that of a normal person due to abnormalities in congenital malfunction, acquired cell damage, visual path abnormality, etc., so that the color image by the display device can be perceived differently from the normal person.

Accordingly, the display device needs to provide a function of displaying a color image for color blind people. In addition, since color areas differing in color hue are different for each color weakener, researches on a display device capable of displaying a color image for each color weakness have been continued.

The present invention provides a display device capable of displaying a color image for each color weakness and a driving method thereof.

The objects of the present invention are not limited to the above-mentioned objects, and other objects and advantages of the present invention which are not mentioned can be understood by the following description and more clearly understood by the embodiments of the present invention. It will also be readily apparent that the objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.

One example of the present invention provides inspection image data for inspecting a user's color vision abnormality characteristic and generates a color correction image corresponding to the color vision abnormality data corresponding to the user's input corresponding to the inspection image data, A data modulator for modulating the RGB data on the basis of the abnormal measurement data, and a data modulator for modulating the RGB data based on the modulated RGB data, And a pixel driver for controlling the brightness of each of the pixels.

Wherein the data modulator selects any one of algorithms based on the RGB data and the abnormal measurement data among a plurality of different algorithms for supplementing the user's hue abnormality characteristic, The RGB data is modulated.

Another exemplary embodiment of the present invention provides inspection image data for checking a user's color vision abnormality characteristic and generates abnormal measurement data corresponding to the user's color vision abnormality characteristic according to a user's input corresponding to the inspection image data Selecting one of a plurality of different algorithms for supplementing the user's hue abnormality characteristic based on the abnormal measurement data and the RGB data when RGB data corresponding to a predetermined color image is inputted Modulating the RGB data based on the selected one of the algorithms and the abnormal measurement data, and controlling the luminance of each pixel based on the modulated RGB data. do.

Modulating the RGB data includes calculating a reference value hue distribution spectrum corresponding to the RGB data on the basis of the normal value measurement data corresponding to the user whose hue is in a normal state, Calculating a plurality of temporal modulated data corresponding to each of the plurality of temporary modulated data and the abnormal measurement data, calculating a plurality of modal color distribution spectrums corresponding to each of the plurality of temporal modulated data and the abnormal measurement data, The method comprising the steps of: detecting a similar fuzzy action color distribution spectrum having a smallest difference from the distribution spectrum; selecting any one of the plurality of algorithms corresponding to the similar fuzzy color distribution spectrum; And modulating the RGB data.

The display device according to an embodiment of the present invention modifies the image with an algorithm selected according to the color blindness characteristic of each color weaker and the image characteristic of the color image, can do. That is, the display quality for each color weakness can be improved.

1 is a block diagram showing a display device according to an embodiment of the present invention.
2 is a flowchart illustrating a method of driving a display device according to an embodiment of the present invention.
3 is a flowchart showing a step of modulating the RGB data of FIG.
Figures 4A-4D illustrate examples of differences between a reference value hue distribution spectrum and a temporally modulated hue distribution spectrum and a reference hue distribution spectrum of red, green, and blue drugs.
FIG. 5 is a view showing an example of a color image of an original color image according to a Daltonization Algorithm, and an abnormal measurement data of a modified color image and a green color image, according to an abnormal identification prediction pattern corresponding to a predetermined original color image, Which is an example of a modulation identification prediction pattern corresponding to the modulation ID.
FIG. 6 is a diagram showing an anomaly prediction pattern corresponding to a predetermined original color image, an original color image and an abnormal measurement data of a red medicine, a modulation pattern of an original color image according to Kotera's Method Algorism, an abnormal measurement of a modulation pattern and a red medicine FIG. 8 is a diagram showing an example of a modulation identification prediction pattern corresponding to data; FIG.
7 is an illustration of a simulation screen according to an embodiment of the present invention.
8 is a flowchart showing a step of generating the abnormal measurement data of FIG.
9 is an example of the inspection image data in the first inspection mode.
10 is an example of the inspection image data in the second inspection mode.
11 is an example of the inspection image data in the third inspection mode.
12 is an example of pattern modulation data according to an embodiment of the present invention.

Hereinafter, a display device and a driving method thereof according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

First, a display device according to an embodiment of the present invention will be described with reference to FIG.

1 is a block diagram showing a display device according to an embodiment of the present invention.

1, a display apparatus 100 according to an exemplary embodiment of the present invention includes a color tone abnormality checking unit 110 for checking color tone abnormality characteristics of each user, RGB data corresponding to a predetermined color image, A data modulator 130 for modulating RGB data corresponding to each user's hue haplotypic characteristic and a data modulator 130 for modulating RGB data corresponding to each of the plurality of pixels P of the display panel 150 based on the modulated RGB data, A pixel driver 140 for controlling luminance, and a display panel 150. [

The display panel 150 includes a plurality of pixels P arranged in a matrix, and each pixel corresponds to a pixel region defined by a gate line GL and a data line DL crossing each other. Although not specifically shown in FIG. 1, each pixel P includes a switching element connected to the gate line GL and the data line DL, and can be independently driven and controlled by the switching element. Since the data signal corresponding to each pixel can be supplied through the switching element, the luminance of each pixel P can be independently controlled.

The hue-hue inspection unit 110 provides inspection image data for checking a user's hue abnormality characteristic, and generates abnormal measurement data corresponding to a user's hue abnormality characteristic according to the input of the user corresponding to the inspection image data.

The inspection image data provided by the color tone abnormality inspection unit 110 corresponds to any one of two or more inspection modes having different degrees of accuracy.

That is, the color vision abnormality inspection unit 110 can provide inspection image data for advancing a relatively accurate and complex inspection mode, and can provide inspection image data for proceeding with a relatively simple and simple inspection mode have. Alternatively, the color tone abnormality checking unit 110 may provide the inspection image data of an easy and simple inspection mode, and then provide the inspection image data of the inspection mode which is gradually difficult according to the result. In this case, the user's hue abnormality characteristic can be more precisely inspected, while the inconvenience of the user due to the inspection is reduced.

The color tone abnormality checking unit 110 will be described in detail below with reference to FIGS. 8 to 12. FIG.

The image data input unit 120 receives RGB data corresponding to each pixel P of the color image.

The data modulation unit 130 modulates the RGB data based on the abnormal measurement data by the color tone abnormality checking unit 110.

Specifically, the data modulator 130 selects any one of a plurality of different algorithms for supplementing the user's hue abnormality characteristic, and modulates the RGB data based on the selected algorithm and the abnormal measurement data.

For example, when the data modulator 130 selects one of the algorithms, the data modulator 130 holds the normal value measurement data corresponding to the user whose color hue is in a normal state, and corresponds to the normal measurement data and the RGB data A reference color distribution spectrum is calculated. The data modulator 130 calculates a plurality of temporary modulated data corresponding to each of a plurality of different algorithms and RGB data and generates a plurality of modulated color distribution spectrum corresponding to each of the plurality of temporally modulated data and the ideal measured data . Then, the data modulator 130 detects a similar-variational-action color distribution spectrum having the smallest difference from the reference value hue distribution spectrum among the plurality of variance-based color distribution spectra, Select any one of the algorithms.

Then, the data modulator 130 modulates the RGB data according to any one of the selected algorithms.

Illustratively, the plurality of algorithms for compensating for the color tone anomaly may include Daltonization, Iterative Daltonization, Kotera's Method, Self-Organizing Process, Color Contrast Enhancement, and Segment-Based Naturalness-Preserving.

The Daltonization algorithm is a method in which an area that is not identified according to the hue hue characteristic of the original image is corrected to a correction matrix and added to the original image.

The Iterative Daltonization algorithm is a method of correcting an area that is not identified according to a hue hue characteristic of an original image by using a correction matrix, and repeating correction until the color blind person becomes a different color from an identifiable hue.

Kotera's Method algorithm is a modeling method that calculates spectral curves of normal and color blind individuals according to the Least Square method and minimizes the difference between normal and color blindness based on the difference.

The self-organizing process algorithm is a method in which identifiable colors of color weakeners are provided as codebooks and corrected based on the mapping of codebooks.

The Color Contrast Enhancement algorithm is a method of performing correction by considering both color and contrast.

The Segment-Based Naturalness-Preserving algorithm is a method of performing K-means clustering after mapping RGB coordinates to LUV coordinates, and mapping by orthogonal mapping to the Confusion line.

By these algorithms, RGB data are modulated differently from each other. That is, since the color blindness characteristic of each color weakness differs in the degree of color which is difficult to distinguish, and algorithms have different characteristics of correcting each color, an algorithm suitable for the color blindness characteristic and the image characteristic of each color weakness needs to be selected.

However, existing display devices have a problem in providing a color image specialized for color blindness characteristic of each color weakness by providing only a function of modulating RGB data by a single unified algorithm.

Meanwhile, the display device 100 according to an exemplary embodiment of the present invention includes a data modulator 130 that selects an algorithm based on image characteristics of a color image and color blindness characteristics of each color weakness among a plurality of different algorithms. Thus, there is an advantage that a modulated image can be provided with an algorithm best suited to image characteristics of each color image and color blindness characteristics of each color weakness.

The data modulator 130 may provide a simulation for various images according to a user's request, and may change or select one of the algorithms according to a user's input based on the simulation. Alternatively, the data modulator 130 may select an algorithm corresponding to each image every time a color image is input.

The pixel driver 140 controls the brightness of each pixel P based on the modulated RGB data.

1, the pixel driver 140 includes a gate driver (not shown) for driving the gate line GL, a data driver (not shown) for driving the data line DL, a gate driver and a data driver And a timing controller (not shown) for controlling the driving timing.

The gate driver sequentially supplies a gate voltage to the gate line GL during a period for displaying one frame (hereinafter, referred to as a 'frame period') to drive the gate line GL.

The data driver supplies respective data voltages to the data lines DL during one horizontal period in which gate voltages are supplied to the gate lines GL to drive the data lines DL.

The timing controller controls the operation of each of the data driver and the gate driver based on timing signals such as a horizontal synchronizing signal Hsync, a vertical synchronizing signal Vsync, a data enable signal DE and a dot clock DCLK supplied from the system .

As described above, the display apparatus 100 according to the embodiment of the present invention can select the algorithm corresponding to the result of measuring the color blindness characteristic of the color weakness and the image characteristic of the color image to be displayed, There is an advantage that a personalized color image can be provided. Thus, the display quality for each color weakness can be improved.

Next, a method of driving a display device according to an embodiment of the present invention will be described with reference to FIGS. 2 to 7. FIG.

2 is a flowchart illustrating a method of driving a display device according to an embodiment of the present invention. 3 is a flowchart showing a step of modulating the RGB data of FIG. Figures 4A-4D illustrate examples of differences between a reference value hue distribution spectrum and a temporally modulated hue distribution spectrum and a reference hue distribution spectrum of red, green, and blue drugs. FIG. 5 is a view showing an example of a color image of an original color image according to a Daltonization Algorithm, and an abnormal measurement data of a modified color image and a green color image, according to an abnormal identification prediction pattern corresponding to a predetermined original color image, Which is an example of a modulation identification prediction pattern corresponding to the modulation ID. FIG. 6 is a diagram showing an anomaly prediction pattern corresponding to a predetermined original color image, an original color image and an abnormal measurement data of a red medicine, a modulation pattern of an original color image according to Kotera's Method Algorism, an abnormal measurement of a modulation pattern and a red medicine FIG. 8 is a diagram showing an example of a modulation identification prediction pattern corresponding to data; FIG. 7 is an illustration of a simulation screen according to an embodiment of the present invention.

As shown in FIG. 2, a method of driving a display device according to an embodiment of the present invention includes steps of generating abnormal measurement data (S10), inputting RGB data (S10), selecting A step S30 of modulating the RGB data based on one algorithm and the abnormality measurement data, and a step S40 of controlling the luminance of each pixel based on the modulated RGB data.

First, the display device 100 is driven in the inspection mode. In this inspection mode, the color vision abnormality checking unit 110 provides inspection image data for checking the user's color vision abnormality characteristics. Then, the abnormal measurement data corresponding to the user's hue abnormality characteristic is generated according to the input of the user with respect to the inspection image data. (S10)

Subsequently, the display apparatus 100 is driven in the display mode. In this display mode, the image data input unit 120 receives RGB data corresponding to a predetermined color image. (S20)

The data modulator 130 selects any one of a plurality of algorithms for supplementing the user's hue abnormality characteristic, and modulates RGB data based on any one selected algorithm and abnormality measurement data. (S30)

More specifically, as shown in FIG. 3, the step S30 of modulating the RGB data includes a step S31 of calculating a reference value hue distribution spectrum corresponding to a hue of a steady state, a step of modifying a plurality of temporary modulations A step S32 of calculating a plurality of modality color distribution spectra corresponding to each of the plurality of temporally modulated data and the ideal measurement data, (Step S34) of detecting a similar-color-difference color-distribution spectrum having the smallest difference between the plurality of algorithms and a step (S35) of selecting any one of algorithms corresponding to the similar-variation color-distribution spectrum among the plurality of algorithms. Then, the data modulator 130 modulates the RGB data based on any one of the algorithms selected in this way.

4A, the data modulator 130 generates a reference value corresponding to an image characteristic of a predetermined color image, based on normal vision measurement data corresponding to a user whose color hue is in a normal state, The color distribution spectrum (fundamental C ^* _LMS ) is calculated. (S31)

As shown in FIG. 4B, in the case of a user exhibiting a chromaticity error with respect to red light, the color distribution spectrum (fundamental C ^* _protan for protan) corresponding to the abnormal measurement data has no identification characteristic in a relatively low wavelength region corresponding to red It is relatively low. Therefore, the difference (lost spectra C ^* _protan ) between the color distribution spectrum (fundamental C ^* _protan for protan) and the reference value color distribution spectrum (fundamental C ^* _LMS ) of the red light is concentrated in a relatively low wavelength region corresponding to red .

As shown in FIG. 4C, in the case of a user exhibiting a chromaticity abnormality with respect to green light, the chromatic distribution spectrum (fundamental C ^* _deutan ) corresponding to the abnormal measurement data has no identification characteristic or is relatively low in the wavelength range corresponding to green. Therefore, the difference (lost spectra C ^* _deutan ) between the color distribution spectrum (fundamental C ^* _deutan ) and the reference value color distribution spectrum (fundamental C ^* _LMS ) of the green color _weakeners is a wavelength range corresponding to green and a wavelength range corresponding to red .

As shown in FIG. 4D, in the case of a user exhibiting a chromaticity error with respect to blue light, the chromatic distribution spectrum (fundamental C ^* _tritan ) corresponding to the abnormal measurement data has no identification characteristic or is relatively low in a relatively high wavelength region corresponding to blue . Therefore, it is _{confirmed that} the difference (lost spectra C ^* _tritan ) between the color distribution spectrum (fundamental C ^* _tritan ) and the reference value color distribution spectrum (fundamental C ^* _LMS ) of the blue light is concentrated in a relatively high wavelength region corresponding to blue .

Thus, the color distribution spectrum is derived differently for each of the color blindness abnormalities of the color weakeners.

As mentioned above, a plurality of different algorithms for modifying a specific color to a different color to complement the color tone abnormality characteristic can be specified for each color tone abnormality characteristic according to each calculation process.

5 is a diagram showing an example of a Daltoniation algorithm.

The original image may include yellow (C1), green (C2), green (C3), blue (C4), violet (C5), and red purple (C6), as shown in Fig. 5 (a). At this time, in the case of the red color weak red color, as shown in Fig. 5 (b), colors including red and green, i.e., green (C2), green color (C3), and red purple Differently from yellow or blue.

When the original image of FIG. 5 (a) is modulated according to the Daltoniation algorithm, as each color is modulated into a shorter wavelength region, green (C2), green (C3) , Blue (C4), and purple (C5) are modulated close to blue. As a result, as shown in Fig. 5 (d), an erroneous discrimination between the C1 block and the C2 block in the same color and the C4 block and the C6 block in the same color may occur.

6 is a diagram illustrating an example of a Kotera's method algorithm.

As shown in FIG. 6 (a), the original image may be an image in which red and green are mainly distributed. At this time, in the case of a color weakener of red green light, red and green are identified as yellow or blue as shown in Fig. 6 (b).

When the original image of FIG. 6 (b) is modulated according to the Kotera's method algorithm, the red color is modulated to the yellow color as shown in FIG. 6 (c). Thus, as shown in Fig. 6 (d), the color weakness of the red green light can identify the image in yellow instead of red.

5 and FIG. 6, it can be seen that the Kotera's method algorithm can provide an image suitable for the red-green color weaker than the Datoniation algorithm.

Thus, since it is necessary to select an algorithm suitable for the color blindness characteristic of each color weakness, the display device 100 can provide a simulation screen for selecting an algorithm suitable for the color blindness characteristic of each color weakness.

That is, as shown in Fig. 7, the simulation screen may include a tool for selecting any one of a plurality of algorithms, a tool for displaying ideal measurement data, and a tool for providing display examples of images.

At this time, the simulation screen is provided with an original image of the image, an anomaly identification prediction image of the color weakness corresponding to the original and abnormal measurement data, a modulation version corresponding to the original and algorithm, and a modulation identification prediction image corresponding to the modulation version and the abnormality measurement data . This makes it easier to identify the most appropriate algorithm for normal and color blindness, since color blindness or an administrator can intuitively identify a modulation identification prediction pattern most similar to the original.

Next, with reference to FIG. 8 to FIG. 11, a description will be made of a color tone abnormality checking unit 110 that provides two or more inspection modes.

8 is a flowchart showing a step of generating the abnormal measurement data of FIG. 9 is an example of the inspection image data in the first inspection mode. 10 is an example of the inspection image data in the second inspection mode. 11 is an example of the inspection image data in the third inspection mode.

As described above, the color vision abnormality checking unit (110 in FIG. 1) provides inspection image data corresponding to any one of two or more inspection modes having different degrees of accuracy, and according to the user's input on the inspection image data, And generates abnormal measurement data corresponding to the abnormal characteristic. (S10 in Fig. 2)

Here, the two inspection modes include a first inspection mode for providing a mission to rearrange the plurality of patterns randomly arranged and having different colors to arrange the patterns of the most similar colors in parallel with one another, And a second inspection mode for providing a mission for selecting a pattern to be identified among a plurality of patterns of different colors included in a predetermined confusion range similar to the background.

And, the two or more inspection modes may further include a third inspection mode that provides a mission of selecting a boundary between patterns that are otherwise identified while arranging patterns of mutually similar colors side by side.

Here, the inspection accuracy is high and the ease of inspection is low in the order of the third inspection mode, the second inspection mode and the first inspection mode. Accordingly, the color tone abnormality checking unit 110 may perform the second or first inspection mode sequentially to increase the inspection accuracy after performing the third inspection mode which is relatively easy to check. Thereby, the user's hue abnormality characteristic can be more precisely inspected, while the inconvenience of the user due to the inspection is reduced.

That is, as shown in FIG. 8, the step (S10 in FIG. 2) in which the hue abnormality checking unit 110 generates the abnormality measurement data provides the inspection image data in the third inspection mode, (S11) generating basic measurement data in accordance with the user's input corresponding to the data, selecting one of the first and second inspection modes based on the basic measurement data (S12) If the second inspection mode is selected within the critical range, the inspection image data of the second inspection mode is provided (S13), and the correction measurement data is generated according to the input of the user corresponding to the inspection image data of the second inspection mode (S14), and provides the inspection image data of the first inspection mode (S15) when the basic measurement data exceeds the predetermined threshold range or the first inspection mode is selected when the correction measurement data exceeds the predetermined threshold value,End at least in accordance with the input of the user corresponding to the test image data for one scan mode may include a step (S16) for generating the measurement data.

As shown in FIG. 9, the inspection image data of the first inspection mode TM1 may be an image in which patterns of different colors are randomly arranged one-dimensionally or two-dimensionally. At this time, the user can perform input in which the patterns of similar colors are arranged in the order of red, green, and blue in parallel.

10, the inspection image data of the second inspection mode TM2 is included in a predetermined confusion range (between yellow and green) similar to the background on the background having any one of the colors (yellow of FIG. 10) And a plurality of numeric patterns arranged in different colors (colors varying gradually from green to gradually yellow). At this time, the user can perform an input for selecting the identifiable patterns among the number patterns.

As shown in FIG. 11, the inspection image data of the third inspection mode TM3 may be an image in which patterns in which color and brightness gradually change. At this time, the user may perform an input (TI) to select a boundary between patterns that are differently identified. The hue abnormality check unit 110 can generate the abnormal measurement data based on the difference (?) Between the input (NI) by the normal person and the input (TI) by the user to be inspected.

In addition, the color tone abnormality checking unit 110 detects a color tone abnormality with respect to the red green light based on the first difference (? 1) between green or blue and yellow, and detects the second color difference Can be detected.

On the other hand, only modifying a part of the colors using an algorithm may not allow the user to identify the image beyond the color hue. In this case, the display apparatus 100 can perform modulation to add two or more patterns having different brightness or saturation to a wavelength region including a hue that is a target of hue more than hue.

12 is an example of pattern modulation data according to an embodiment of the present invention.

As shown in the lower part of Fig. 12, when comparing the reference value measurement data with the abnormality measurement data in the case of the color weakness with the red-green color light, it is discriminated as yellow against green. Further, the closer to the green of the blue region, the lighter blue is identified. Accordingly, in the case where the color vision abnormality of the red green light is serious color weakness, or even if it is a color blind person older enough to weaken the optic nerve, it may be difficult to discriminate even blue color adjacent to green.

Accordingly, the display device 100 according to an embodiment of the present invention can perform modulation to give different patterns to predetermined unit wavelength regions.

Illustratively, as shown in Fig. 12, the pattern modulation data is provided with a horizontal straight line pattern in the first wavelength region CA1 and a diagonal pattern from the lower left side to the upper right side in the second wavelength region CA2 , And a vertical concave-convex pattern can be imparted to the third wavelength region CA3.

In other words, as shown in the upper part of FIG. 12, in the case of displaying an image source including red, green and intermediate yellow, in the abnormal identification prediction pattern for the color weakness with the red color of the red green light, And green are all identified as yellow in the middle, so they are displayed only in the color of yellow.

In the case of a user who is hard to identify an image displayed only in the color of yellow by the depth of coloring or aging or the age of the user, the display device 100 displays, by a user's request, by a plurality of unit wavelength regions included in the wavelength region between blue and green It is possible to provide a modulated pattern imparted with different patterns. Thereby, the display quality for the color weakeners can be further improved.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Will be clear to those who have knowledge of.

100: display device 110:
120: video data input unit 130: data modulation unit
140: Pixel driver 150: Display panel

Claims (11)

A color tone abnormality check unit for providing inspection image data for checking a user's color tone abnormality characteristic and generating abnormal measurement data corresponding to the user's color tone abnormality characteristic according to a user's input corresponding to the inspection image data;
A video data input unit for inputting RGB data corresponding to a predetermined color image;
A data modulator for modulating the RGB data based on the abnormal measurement data; And
And a pixel driver for controlling the luminance of each of the plurality of pixels of the display panel based on the modulated RGB data.
The method according to claim 1,
The data modulator
Selecting any one of a plurality of different algorithms for supplementing the user's color vision anomaly characteristic based on the RGB data and the abnormal measurement data,
And modulates the RGB data based on any one of the selected algorithms and the abnormal measurement data.
The method according to claim 1,
The data modulator
Holding normal value measurement data corresponding to a user whose hue is in a normal state,
Calculates a reference value hue distribution spectrum corresponding to the normal measurement data and the RGB data,
A plurality of different temporal modulation data corresponding to each of the plurality of different algorithms and the RGB data for compensating for the user's color vision anomaly characteristic,
Calculating a plurality of temporal modulated color data spectra corresponding to each of the plurality of temporally modulated data and the abnormal measurement data,
Detecting a color similarity variation color spectrum having the smallest difference from the reference color distribution spectrum among the plurality of color variation detection spectra,
And selects any one of the plurality of algorithms corresponding to the similar color variation color distribution spectrum.
The method according to claim 1,
Wherein the color vision abnormality checking unit provides inspection image data corresponding to any one of two or more different inspection modes,
The two or more test modes
A first inspection mode for providing a mission to rearrange a plurality of randomly arranged and differently colored patterns so that the patterns of the most similar colors are arranged in parallel with each other; And
And a second inspection mode arranged on the background of any one of the colors and providing a mission for selecting a pattern identified among a plurality of patterns included in a predetermined confusion range similar to the background and having different colors, .
5. The method of claim 4,
The two or more test modes
Further comprising a third inspection mode for providing a mission of selecting a boundary between patterns that are otherwise identified while arranging patterns of mutually similar colors in a side-by-side manner.
6. The method of claim 5,
The hue abnormality checking unit
Providing inspection image data of any one of the first inspection mode and the second inspection mode on the basis of the abnormal measurement data by the third inspection mode,
And provides the inspection image data of the first inspection mode based on the abnormal measurement data by the second inspection mode.
Providing test image data for examining a user's color vision abnormality characteristic and generating abnormal measurement data corresponding to the user's color vision abnormality characteristic according to a user's input corresponding to the test image data;
When the RGB data corresponding to a predetermined color image is input, selects any one of algorithms based on the abnormal measurement data and the RGB data among a plurality of different algorithms for supplementing the user's hue abnormality characteristic, Modulating the RGB data based on any one of the algorithm and the abnormal measurement data; And
And controlling the luminance of each pixel based on the modulated RGB data.
8. The method of claim 7,
The step of modulating the RGB data
Calculating a reference value hue distribution spectrum corresponding to the RGB data based on normal value measurement data corresponding to a user whose hue is in a normal state;
Calculating each of the plurality of algorithms and a plurality of temporary modulation data corresponding to the RGB data;
Calculating a plurality of modality color distribution spectrums corresponding to each of the plurality of temporally modulated data and the abnormal measurement data;
Detecting a color similarity variation color spectrum having the smallest difference from the reference color distribution spectrum among the plurality of color variation detection spectra;
Selecting any one of the plurality of algorithms corresponding to the similar fuzzy color distribution spectrum; And
And modulating the RGB data based on the selected algorithm.
8. The method of claim 7,
Wherein, in the step of generating the abnormal measurement data, the inspection image data corresponds to any one of two or more different inspection modes,
The two or more test modes
A first inspection mode for providing a mission to rearrange a plurality of randomly arranged and differently colored patterns so that the patterns of the most similar colors are arranged in parallel with each other; And
And a second inspection mode arranged on the background of any one of the colors and providing a mission for selecting a pattern identified among a plurality of patterns included in a predetermined confusion range similar to the background and having different colors, .
10. The method of claim 9,
The two or more test modes
Further comprising a third inspection mode for providing a mission to select a boundary between patterns that are otherwise identified while arranging patterns of mutually similar colors in a side-by-side manner.
11. The method of claim 10,
The step of generating the abnormal measurement data
Generating basic measurement data according to a user's input corresponding to the inspection image data in the third inspection mode;
Providing inspection image data of the second inspection mode based on the basic abnormal measurement data;
Generating correction measurement data according to a user input corresponding to the provided inspection image data in the second inspection mode;
Providing inspection image data of the first inspection mode based on the correction abnormality measurement data; And
And generating the abnormal measurement data according to a user's input corresponding to the inspection image data in the first inspection mode.

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