KR20130098654A - Method and apparatus for generating visual sensitivity function - Google Patents

Abstract

PURPOSE: A visibility function generating method and a device thereof generate an enhanced visibility function accurately reflecting human visual characteristics by using spectral distribution measurements of a reference field and spectral distribution measurements of a matching field. CONSTITUTION: A first color characteristic of first spectral distribution and a second color characteristic of second spectral distribution are computed by applying a first visibility function to first spectral distribution measurements and second spectral distribution measurements (230). A first color difference between the first color characteristic and the second color characteristic is calculated (235). A third color characteristic of the first spectral distribution and a fourth color characteristic of the second spectral distribution are computed by applying a second visibility function to the first spectral distribution measurements and the second spectral distribution measurements (240). A second color difference between the third color characteristic and the fourth color characteristic is calculated (245). The first visibility function is determined as a modified visibility function when the first color difference is smaller than the second color difference (280). [Reference numerals] (210) Match visual angles; (220) Calculate spectral distribution; (230) Compute color characteristics using a candidate visibility function; (235) Calculate a first color difference; (240) Compute color characteristics using a reference visibility function; (245) Calculate a second color difference; (250) Is the first color difference larger than the second color difference ?; (255) Add the candidate visibility function to a candidate visibility function group; (260) Is there any additional candidate visibility function ?; (265) Change the candidate visibility function; (270) Select an optimal visibility function from the candidate visibility function group; (280) Provide the selected optimal visibility function; (AA) Start; (BB) End

Description

METHOD AND APPARATUS FOR GENERATING VISUAL SENSITIVITY FUNCTION}

A method and apparatus for generating a visibility function, and more particularly, a method and apparatus for generating a visibility function for improving color reproduction accuracy are disclosed.

In general, color characteristics perceived by human vision for any object color can be predicted using 1) spectral distribution information (i.e. measurements) of the object color and visual gamma function. Humans expect similar color information to be reproduced by the display and perceived color information when they see any object directly.

The color characteristic that humans perceive through vision is that light reflected from an object and reaching the eye is absorbed by three long, middle, and short LMS cones in the eye's retina. Thereafter, the signal generated by the absorption is determined by transmission to the brain by the optic nerve.

The three LMS cones have different sensitivity in the visible light wavelength band. From the early to mid-1900s, human LMS cone sensitivity characteristics could not be measured directly. Therefore, LMS cone sensitivity characteristics were measured indirectly by color matching experiments, a psychological experiment method, and the Commission Internationale de l'Eclairage (CIE) determined LMS cone sensitivity characteristics measured indirectly by various research institutes. Data was collected to define the CIE 1931 visibility function (or color matching function). Color characteristic measurement equipment and color handling imaging devices can include algorithms based on the CIE 1931 visibility function that can calculate the color characteristics of light reflected from an object under any light source or light emitted from the object.

The CIE 1931 visibility function includes x-bar, y-bar and z-bar functions. The x-bar, y-bar and z-bar functions show the highest sensitivity in the long, medium and short wavelength regions, respectively.

Although the data collected by the various research institutes for the z-bar function, which is the visibility function in the short wavelength region, have large fluctuations with each other, the averaging of the data collected for the z-bar function is not sufficient. Accordingly, there has been a continuous demand for modification of the CIE 1931 visibility function.

As a result of the modifications to the CIE 1931 visibility function, the 2006 CIE was based on LMS cone signals generated using LMS cone sensitivity characteristic data measured in the visible wavelength range, rather than indirectly as in conventional color matching experiments. CIE 170: 2006 visibility function is proposed.

The color characteristics of the light reflected from the object or the light emitted from the self-emissive element are considered by considering 1) the spectral power distribution of the light reflected from the object, 2) the spectral distribution of the self-emitting light source, and 3) the CIE 1931 visibility function. Tristmulus can be expressed in the form of XYZ. In addition, the color characteristics of the light may be displayed in the form of brightness and color difference information. The brightness and chrominance information is obtained by nonlinearizing the values of the tristimulus values XYZ, and then using the color appearance model such as CIE Lab or CIECAM02. Can be generated by conversion.

The spectral distribution of a light-emitting diode (LED) light source has a narrower width and a sharper peak than the spectral distribution of an incandescent lamp light source. For both CIE 1931 visibility functions and CIE 170: 2006 visibility functions, a problem has been raised that the color characteristics of LED light sources having such spectral distribution characteristics cannot be accurately described. For example, observers perceive similarly the color characteristics of three light sources: 1) the color characteristics of incandescent lamps with filters and 2) red, green, blue (RGB) LEDs. However, the color characteristic values of each of the two spectral distributions calculated by applying the CIE 1931 or CIE 170: 2006 visibility function respectively to the spectral distribution of the incandescent lamp and the RGB LED light source actually measured show a large difference. That is, for two light sources exhibiting color characteristics that humans perceive as the same, the color characteristic values of two light sources calculated using 1) the spectral distribution of each of the two light sources and 2) the existing visibility function used as a standard. They differ greatly from one another.

One embodiment can provide an apparatus and method for generating an improved visibility function that more accurately reflects human visual perception characteristics as compared to existing visibility functions.

One embodiment can provide an apparatus and method for calculating and using color information using an improved visibility function.

In one aspect, the electronic device generates a visibility function, wherein the first color characteristic of the first spectral distribution is applied by applying a first visibility function to the measurement of the first spectral distribution and the measurement of the second spectral distribution, respectively. Calculating a second color characteristic of the second spectral distribution, calculating a first color difference between the first color characteristic and the second color characteristic, measured values of the first spectral distribution and measured values of the second spectral distribution Calculating a third color characteristic of the first spectral distribution and a fourth color characteristic of the second spectral distribution by applying a second visibility function to the second color difference between the third color characteristic and the fourth color characteristic. And calculating the first visibility function as a modified visibility function when the first color difference is smaller than the second color difference, wherein the second visibility function is short (S) cone. corn And a middle (M) cone function and a Long (L) cone function, wherein the first visibility function is a visibility function that shifts the S cone function of the second visibility function along a wavelength axis. A production method is provided.

The method of generating a visibility function may further include generating the measured value of the first spectral distribution and the measured value of the second spectral distribution by measuring the spectral distribution of the first field and the spectral distribution of the second field, respectively.

The method of generating a visibility function includes adjusting a first light emitted from a first light source illuminating the first field such that an observer recognizes that the first field and the second field visually have the same color characteristics. It may further include.

The second light shining on the second field may be emitted from an incandescent lamp and pass through a filter.

The first light source may include a light-emitting diode (LED) light source, a green LED light source, and a blue LED light source.

The adjusting of the light may control the first light by adjusting a magnitude of current applied to each of the red LED light source, the green LED light source, and the blue LED light source.

The second visibility function may be a CIE 170: 2006 visibility function.

The first visibility function may be plural, and the modified visibility function may be one or more.

Each of the S cone functions of the plurality of first visibility functions may be a function in which the S cone functions of the second visibility functions are shifted by different distances along the wavelength axis.

The method of generating a visibility function may further include selecting a visibility function that generates the smallest first color difference among the one or more modified visibility functions as an optimal visibility function and providing the optimal visibility function. .

In another aspect, the method further comprises: generating a first spectral distribution measurement by measuring a spectral distribution of the first observation site of the subject output from the monitor, wherein the spectral distribution of the second observation site of the subject output from the monitor is generated. Generating a second spectral distribution measurement by measuring, calculating a first color characteristic by applying a first visibility function to the first spectral distribution measurement, and by applying the first visibility function to the second spectral distribution measurement Calculating a second color characteristic, calculating a color difference between the first color characteristic and the second color characteristic, and generating a medical diagnosis result for the subject based on the color difference. A medical diagnostic method using a function is provided.

The medical diagnostic method using the visibility function may further include generating the first visibility function.

The generating of the first visibility function may include applying a second visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively, to determine the third color characteristic and the fourth spectral distribution of the third spectral distribution. Calculating a fourth color characteristic, calculating a second color difference between the third color characteristic and the fourth color characteristic, a third visibility function, respectively, for the measurement of the third spectral distribution and the measurement of the fourth spectral distribution Calculating a fifth color characteristic of the third spectral distribution and a sixth color characteristic of the fourth spectral distribution, calculating a third color difference between the fifth color characteristic and the sixth color characteristic, and If the second color difference is smaller than the third color difference, the method may include determining the second visibility function as the first visibility function.

The third visibility function may include a short (S) cone function, a middle (M) cone function, and a long (L) cone function.

The second visibility function may be a visibility function obtained by shifting the S cone function of the third visibility function along a wavelength axis.

In another aspect, a first visibility function is applied to a spectral distribution measuring unit that provides a measurement of a first spectral distribution and a measurement of a second spectral distribution, and a measurement of the first spectral distribution and a measurement of the second spectral distribution, respectively. By applying a first color characteristic of the first spectral distribution and a second color characteristic of the second spectral distribution, calculating a first color difference between the first color characteristic and the second color characteristic, and calculating the first spectral The third color characteristic of the first spectral distribution and the fourth color characteristic of the second spectral distribution are calculated by applying a second visibility function to the measured value of the distribution and the measured value of the second spectral distribution, respectively, and the third color characteristic And a calculation unit configured to calculate a second color difference between the fourth color characteristics and to determine the first visibility function as a modified visibility function when the first color difference is smaller than the second color difference, and wherein the second visibility is determined. box Includes a Short (S) cone function, a Middle (M) cone function, and a Long (L) cone function, wherein the first visibility function is the S cone function of the second visibility function. An electronic device is provided, which is a visibility function in which is shifted along the wavelength axis.

The spectral distribution measuring unit may generate the measured value of the first spectral distribution and the measured value of the second spectral distribution by measuring the spectral distribution of the first field and the spectral distribution of the second field, respectively.

The electronic device may further include a visual matching unit configured to adjust first light emitted from a first light source illuminating the first field so that an observer recognizes that the first field and the second field have visually identical color characteristics. It may include.

The electronic device may further include the first light source.

The first light source may include a light-emitting diode (LED) light source, a green LED light source, and a blue LED light source.

The visual matching unit may adjust the first light by adjusting a magnitude of current applied to each of the red LED light source, the green LED light source, and the blue LED light source.

The electronic device may further include a providing unit.

The first visibility function may be plural, and the modified visibility function may be one or more.

Each of the S cone functions of the plurality of first visibility functions may be a function in which the S cone functions of the second visibility functions are shifted by different distances along the wavelength axis.

The calculator may select a visibility function that generates the smallest first color difference among the one or more modified visibility functions as an optimal visibility function.

The providing unit may provide the optimum visibility function.

In another aspect, the first spectral distribution measurement is generated by measuring the spectral distribution of the first observation site of the subject to be output from the monitor, and the spectral distribution of the second observation site of the subject to be output from the monitor is generated. A first color characteristic is calculated by applying a first visibility function to the spectral distribution measurement unit and the first spectral distribution measurement by generating a second spectral distribution measurement by measuring the first spectral distribution measurement, and applying the first visibility function to the second spectral distribution measurement. And a calculation unit configured to calculate a second color characteristic by calculating, calculate a color difference between the first color characteristic and the second color characteristic, and generate a medical diagnosis result for the subject based on the color difference. A medical diagnostic device using the function is provided.

The calculator may generate a first visibility function.

The calculating unit calculates a third color characteristic of the third spectral distribution and a fourth color characteristic of the fourth spectral distribution by applying a second visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively. And calculating a second color difference between the third color characteristic and the fourth color characteristic, and applying a third visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively. Compute a fifth color characteristic and a sixth color characteristic of the fourth spectral distribution, calculate a third color difference between the fifth color characteristic and the sixth color characteristic, and wherein the second color difference is smaller than the third color difference. In this case, the first visibility function may be generated by determining the second visibility function as the first visibility function.

The third visibility function may include a short (S) cone function, a middle (M) cone function, and a long (L) cone function.

The second visibility function may be a visibility function obtained by shifting the S cone function of the third visibility function along a wavelength axis.

An apparatus and method are provided for generating an improved visibility function that more accurately reflects the visual perception characteristics of humans than conventional visibility functions.

An apparatus and method are provided for calculating and using color information using an improved visibility function.

1 is a structural diagram of an apparatus for generating visibility functions, according to an exemplary embodiment.
2 is a flowchart of a method of generating a visibility function, according to an exemplary embodiment.
3 illustrates an optimal visibility function according to one embodiment.
4 illustrates the performance of a CIE 1931 visibility function according to an example.
5 illustrates the performance of a CIE 170: 2006 visibility function according to an example.
6 illustrates the performance of an optimal visibility function according to an example.
7 is a flowchart of a method of applying a visibility function to a monitor color characteristic model, according to an exemplary embodiment.
8 is a flowchart of a medical diagnosis method using a visibility function, according to an exemplary embodiment.
9 is a flowchart illustrating a method of applying a visibility function to an apparatus for evaluating color characteristics of an LED light source, according to an exemplary embodiment.

In the following, embodiments will be described in detail with reference to the accompanying drawings. Like reference symbols in the drawings denote like elements.

1 is a structural diagram of an apparatus for generating visibility functions, according to an exemplary embodiment.

The visibility function generating device 100 may be an electronic device such as a computer or a part of the electronic device.

The visibility function generator 100 may include a calculator 160 and an output unit 170. The visibility function generator 100 may further include RGB LED light sources 110, an incandescent lamp 120, a filter 130, a visual matching unit 140, and a spectral distribution measuring unit 150.

RGB LED light sources 110 and incandescent lamps 120 emit light, respectively. The RGB LED light sources 110 may include an R LED light source, a G LED light source, and a B LED light source.

Filter 130 may have a center wavelength and width. For example, the central wavelength of the filter 130 may be 450 nm and the width may be 10 nm. The filter 130 may filter the light emitted from the light source. The light source may be one or more of the RGB LED light sources 110 and the incandescent lamp 120. For example, if the filter 130 having a center wavelength of 450 nm and a width of 10 nm is installed in front of the incandescent lamp 120, the observer can recognize the color of light having a spectral distribution of 450 ± 5 nm passing through the filter 130. Here, the RGB LED light sources 110 and the incandescent lamp 120 are exemplary, and the RGB LED light sources 110 and the incandescent lamp 120 may each be replaced with any other type of light source. For example, a lamp having a broad wavelength such as tungsten or cold-cathode fluorescent lamps (CCFL) may be used instead of the incandescent lamp 120.

The time matcher 140 may generate a reference field and a matching field. The reference field may refer to a portion (that is, a portion or an object that the light shines) in which the color characteristics of the light emitted from the incandescent lamp 120 and passed through the filter 130 appear. The reference field may be configured by applying a filter to the incandescent lamp 120. The matching field may mean a portion (that is, a portion or an object that the light shines on) in which the color characteristics of the light emitted from the RGB LED light sources 110 appear. That is, light emitted from the incandescent lamp 120 may pass through the filter 130 to reach the reference field, and light emitted from the RGB LED light sources 110 may reach the matching field.

Also, the time matching unit 140 may adjust one or more of the reference field and the matching field. The above adjustment is explained in detail below with reference to FIG. 2.

The spectral distribution measuring unit 150 may measure the spectral distribution of each of the reference field and the matching field, and generate a measurement value of the spectral distribution based on the measured spectral distribution. Here, the measured value of the spectral distribution may mean information indicating the spectral distribution. The spectral distribution measuring unit 150 may be a sensor for measuring the spectral distribution.

The calculation unit 160 may generate a new visibility function having superior characteristics compared to the existing visibility function using the spectral distribution measurement of the reference field and the spectral distribution measurement of the matching field.

The output unit 170 may provide the detected new visibility function. The output unit 170 may output information about the detected new visibility function.

A method of generating a new visibility function is described in detail below with reference to FIG. 2.

2 is a flowchart of a method of generating a visibility function, according to an exemplary embodiment.

In the following description, the first field may mean the reference field described with reference to FIG. 1. The second field may mean the matching field described with reference to FIG. 1.

The first light can be emitted from the first light source and can illuminate the first field. The first light source may be the RGB LED light sources 110 described above with reference to FIG. 1, and may include an R LED light source, a G LED light source, and a B LED light source of the RGB LED light sources 110.

The second light may be emitted from the incandescent lamp 120 and may pass through the filter 130. The second light emitted from the incandescent lamp 120 and passed through the filter 130 may illuminate the second field.

In operation 210, the visual matching unit 140 may adjust the first light so that the viewer recognizes that the first field and the second field have visually identical color characteristics to each other. In other words, the visual matching unit 140 may adjust the matching field so that the observer recognizes the color characteristic of the matching field as the same color characteristic as that of the reference field. For example, the visual matching unit 140 may adjust the first light by adjusting the magnitude of the current applied to each of the R LED light source, the G LED light source, and the B LED light source.

The second field can be adjusted by the observer's manipulation. The observer may manipulate the visual matching unit 140 so that the color characteristics of the second field appear to the same as the color characteristics of the first field. For example, the observer manipulates the visual matching unit 140 such that the color characteristics of the second field look the same as the color characteristics of the first field, so that the R LED light source, the G LED light source, and the B LED of the RGB LED light sources 110 are operated. The amount of current applied to each of the LED light sources can be adjusted.

After the above adjustment, the first field and the second field may visually exhibit the same or similar color characteristics. In other words, after the observer observes that the color characteristics of the first field and the color characteristics of the second field are visually the same or similar, step 220 may be performed.

In operation 220, the spectral distribution measuring unit 150 may generate the measured value of the first spectral distribution and the measured value of the second spectral distribution by measuring the spectral distribution of the first field and the spectral distribution of the second field, respectively. The spectral distribution measuring unit 150 may measure the spectral distribution of the first field and the spectral distribution of the second field in the visible light wavelength region, respectively. Here, the visible light wavelength region may mean a wavelength region from 380nm to 780nm.

In operation 230, the calculator 160 may calculate a first color characteristic of the measurement value of the first spectral distribution and a second color characteristic of the second spectral distribution measurement using the first visibility function. Here, the first visibility function may be a candidate of the visibility function to be newly generated by the visibility function generating device 100. The first visibility function is hereinafter referred to as a candidate visibility function. That is, the calculation unit 160 calculates the first color characteristic of the first spectral distribution and the second color characteristic of the second spectral distribution by applying the candidate visibility function to the measured value of the first spectral distribution and the measured value of the second spectral distribution, respectively. Can be.

In operation 235, the calculator 160 may calculate a first color difference between the first color characteristic and the second color characteristic. That is, the first color difference may be a color difference generated based on the candidate visibility function.

In operation 240, the calculator 160 may calculate the third color characteristic of the measured value of the first spectral distribution and the fourth color characteristic of the second spectral distribution measurement using the second visibility function. Here, the second visibility function may be a known visibility function. The second visibility function may be an existing visibility function used as a standard, such as, for example, CIE 170: 2006 visibility function. The second visibility function is referred to as a reference visibility function below.

The reference visibility function may include an S cone function, an M cone function, and an L cone function. The S cone function may be a function representing the sensitivity of the blue region in the CIE 170: 2006 visibility function.

The candidate visibility function may be generated based on the reference visibility function. The candidate visibility function may be a function in which the S cone function, the M cone function, and the L cone functions of the reference visibility function are shifted along the wavelength axis, respectively. The candidate visibility function may include a function in which 1) the M cone function of the reference visibility function, 2) the L cone function of the reference visibility function and 3) the S cone function of the reference visibility function are shifted along the wavelength axis. For example, the candidate visibility function may be a visibility function obtained by shifting the S cone function of the reference visibility function along the wavelength axis.

In operation 245, the calculator 160 may calculate a second color difference between the third color characteristic and the fourth color characteristic. That is, the first color difference may be a color difference generated based on the reference visibility function.

In operation 250, the calculator 160 may compare the first color difference and the second color difference.

In operation 210 described above, the visual matching unit 140 adjusts the first field such that the color characteristics of the first field and the color characteristics of the second field are similar to each other. According to the above adjustment, the measured value of the first spectral distribution and the measured value of the second spectral distribution are similar to each other. In addition, the color characteristics of each of the measurements of both spectral distributions calculated using the visibility function should also be similar to each other. In other words, the smaller the difference between the measurements of both spectral distributions calculated using the candidate visibility function, the better the characteristics of the candidate visibility function can be considered.

In addition, when the first color difference generated based on the candidate visibility function is smaller than the second color difference generated based on the reference visibility function, the color characteristic value calculated by applying the candidate visibility function is calculated by applying the reference visibility function. It may be considered to more accurately represent the visual perception characteristics of humans as compared to color characteristic values. That is, when the first color difference is smaller than the second color difference, the candidate visibility function used to generate the first color difference may be regarded as showing better characteristics than the reference visibility function used to generate the second color difference. . Thus, when the first color difference is smaller than the second color difference, the calculator 160 may determine the candidate visibility function as the modified visibility function.

There may be a plurality of candidate visibility functions described above. That is, the apparatus 100 for generating the visibility function may determine candidate visibility functions satisfying a specific condition among the one or more all candidate visibility functions as the modified visibility functions. Accordingly, there may be a plurality of modified visibility functions, and the visibility function generating device 100 may select an optimal visibility function among one or more modified visibility functions.

The S cone functions of each of the candidate visibility functions may be a visibility function in which the S cone functions of the reference visibility functions are shifted by different distances along the wavelength axis. For example, the calculation unit 160 may generate each of the plurality of candidate visibility functions by shifting the S cone function of the reference visibility function by a different distance along the wavelength axis. The calculator 160 may acquire the plurality of candidate visibility functions by repeatedly moving the S cone function of the reference visibility function by a predetermined interval along the wavelength axis.

When there are a plurality of candidate visibility functions, steps 255 to 280 to be described below may be performed. When there are a plurality of candidate visibility functions, in step 250, when the first color difference is smaller than the second color difference, step 255 may be performed, and when the first color difference is greater than or equal to the second color difference, step 260 may be performed. Can be performed.

In operation 255, the calculator 160 may add the modified visibility function of operation 250 to the candidate visibility function group. That is, when the first color difference generated using the candidate visibility function is smaller than the second color difference generated using the reference visibility function, the candidate visibility function generating the first color difference may be included in the candidate visibility function group. .

In operation 260, the calculator 160 may check whether there is an additional candidate visibility function that may be used in the operations 230 and 235 described above. Step 265 may be performed if there are additional candidate visibility functions. If no additional candidate visibility function is present, step 270 may be performed.

In operation 265, the calculator 160 may change the candidate visibility function to be used in operations 230 and 235. Here, the calculation unit 160 may generate the modified candidate visibility function by shifting the S cone function of the existing candidate visibility function by a predetermined distance along the wavelength axis.

After step 265 is performed, steps 230-250 can be repeated using the modified candidate visibility function.

In operation 270, the calculator 160 may select one of the one or more modified visibility functions in the candidate visibility function group as an optimal visibility function. The modified visibility functions in the candidate visibility functions group may be regarded as visibility functions each having better characteristics than the existing reference visibility functions.

The calculator 160 may select a visibility function that satisfies a specific condition among one or more visibility functions as an optimal visibility function. The optimal visibility function may be regarded as the visibility function having the best characteristic among the modified visibility functions.

For example, the calculator 160 may select a visibility function that generates the smallest first color difference among one or more visibility functions in the candidate visibility function group as an optimal visibility function.

In operation 280, the output unit 180 may provide a selected optimal visibility function.

The modified visibility function or the optimal visibility function generated in this embodiment may exhibit improved performance compared to the existing standard visibility function (eg, CIE 170: 2006), and more closely approximate the color characteristics perceived by human vision. It can be explained.

Here, the candidate visibility function, the modified visibility function or the optimum visibility function may be an LMS visibility function based on the LMS cone signal.

The calculation unit 160 applies the matrix transformation according to Equation 1 below to the LMS visibility function based on the LMS cone signal, thereby converting the LMS visibility function based on the LMS cone signal based on the x _F y _F z _F signal. Can be converted to a visibility function.

는 파장을 나타낼 수 있다.

는 가시광 대역 내의 파장일 수 있으며, 가시광 대역은 380nm부터 790nm까지의 파장을 갖는 대역일 수 있다.here,

May represent a wavelength.

May be a wavelength within the visible light band, and the visible light band may be a band having a wavelength from 380 nm to 790 nm.

,

및

는 각각

가 입력 값일 때의 L 콘 함수, M 콘 함수 및 S 콘 함수의 값을 나타낼 수 있다.

,

및

는 실제로 LMS 콘이 측정된 값일 수 있다.

,

And

Respectively

It can represent the value of the L cone function, M cone function and S cone function when is an input value.

,

And

May actually be the value at which the LMS cone is measured.

및

는 각각

가 입력 값일 때의 x_F 신호의 값, y_F 신호의 값 및 z_F 신호의 값을 나타낼 수 있다.

And

Respectively

When x is an input value, it may represent the value of the x _F signal, the value of the y _F signal, and the value of the z _F signal.

The calculator 160 may calculate a tristimulus value, which is one of the most general forms of color characteristics, using a visibility function based on an x _F y _F z _F signal.

The calculation unit 160 may express color characteristics in the form of tristimulus values X _F Y _F Y _F by applying a visibility function based on an x _F y _F z _F signal to a spectral distribution measurement (or data). Here, the spectral distribution measurement may be a spectral distribution measurement of a patch displayed on any object or monitor. In addition, the spectral distribution measurement may be a spectral distribution measurement in the visible light region.

The calculation unit 160 may apply the visibility function based on the x _F y _F z _F signal to the spectral distribution measurement (or data) based on Equation 2 below, and the tristimulus values X _F , Y _F and Y _F Can be calculated.

는 광원의 분광분포를 나타낼 수 있다.

는 물체 색을 위해 요구될 수 있다.

는 물체의 반사율을 나타낼 수 있다. 상기의 물체는 색 측정의 대상일 수 있다. 상기의 반사율은 가시광 대역에서의 반사율일 수 있다. 즉,

는 가시광 대역에서의 색 측정의 대상인 물체의 반사율(spectral reflectance factor of a color sample)일 수 있다.
here,

May represent the spectral distribution of the light source.

May be required for the object color.

May represent the reflectance of the object. The object may be an object of color measurement. The reflectance may be a reflectance in the visible light band. In other words,

May be a spectral reflectance factor of a color sample that is an object of color measurement in the visible light band.

3 illustrates an optimal visibility function according to one embodiment.

The graph of FIG. 3 may represent an example of the optimal visibility function described above with reference to FIG. 2. The graph of FIG. 3 can show the LMS cone sensitivity of the optimal visibility function.

In Fig. 3, the L cone function, the M cone function and the S cone function of the optimal visibility function are respectively shown. The x axis of the graph represents wavelength and the y axis represents sensitivity.

4 to 6 illustrate the performance of the CIE 1931 visibility function, the performance of the CIE 170: 2006 visibility function, and the optimum visibility function, respectively, according to an example.

To measure the performance of the visibility functions, four filters can each be used. That is, the second light illuminating the reference field (ie, the second field) may be emitted from the incandescent lamp 120 and pass through one of the four filters 130.

The visual matching unit 140 may generate four test colors by using four filters. The observer can see each of the four test colors in the reference field, and can adjust the matching field to show a color characteristic almost similar to that of each of the four test colors. For example, the observer adjusts the color of the matching field by adjusting the magnitude of the current applied to each of the R LED light source, the G LED light source, and the B LED light source so that each of the four test colors can exhibit a color characteristic that is almost similar to that of each of the four test colors. Can be. The spectral distribution measuring unit 150 may measure the spectral distribution of each of the reference field and the matching field that the observer recognizes as having the same color characteristic in the visible light region.

4 to 6 show the color characteristics of the reference field and the color characteristics of the matching field calculated by applying the CIE 1931 visibility function, the CIE 170: 2006 visibility function, and the optimum visibility function described above with reference to FIG. 2, respectively. Here, the color characteristic means a color coordinate value. The color coordinate systems of FIGS. 4 to 6 may correspond to the visibility functions of FIGS. 4 to 6, respectively. 4 to 6, the x axis represents u 'and the y axis represents v'.

Observers can adjust the color of the matching field such that the reference field and the matching field have similar color characteristics to each other. Therefore, as color value values calculated using the spectral distribution measurement of the reference field, the spectral distribution measurement of the matching field, and the visibility function overlap each other in the color coordinate system, the phenomenon of visually similar color characteristics can be predicted more accurately. It can be seen.

4 to 6, the color of the reference field is indicated by a diamond symbol. The color of the matching field is indicated by a triangle symbol. The dotted line surrounding the diamond symbol and the triangle symbol indicates that the diamond symbol and the triangle symbol are the result of measurements performed by a particular observer. That is, in FIGS. 4 to 6, four observers each adjusted the color of the matching field. Since four test colors were shown in the reference field, there is one diamond symbol and four triangle symbols within one dotted line.

4 to 6, the difference between the average value of the color data of the reference field and the average value of the color data of the field is calculated using the CIELAB ΔE ^* _ab color difference.

4 illustrates the performance of a CIE 1931 visibility function according to an example.

In FIG. 4, when the CIE 1931 visibility function was used, ΔE ^* _ab color difference was indicated to have a value from 16.3 to 24.6 depending on the observer.

5 illustrates the performance of a CIE 170: 2006 visibility function according to an example.

In FIG. 5, when the CIE 170: 20061 visibility function was used, the ΔE ^* _ab color difference was shown to have a value from 10 to 12.8, depending on the viewer.

6 illustrates the performance of an optimal visibility function according to an example.

In FIG. 6, when the optimum visibility function described above with reference to FIG. 2 is used, ΔE ^* _ab color difference is indicated as having a value from 1.4 to 6.7 depending on the observer.

Thus, the color characteristics of the reference field and the matching field that the observers perceive as having the same or similar color characteristics can be seen to be predicted more accurately when the optimal visibility function described above is used than when the conventional standard visibility function is used.

7 is a flowchart of a method of applying a visibility function to a monitor color characteristic model, according to an exemplary embodiment.

The particular algorithm used within the monitor may define the relationship between the RGB values input to the monitor and the characteristics of the colors reproduced on the monitor in response to the RGB values.

The modified visibility function or optimal visibility function described above with reference to FIG. 2 may be applied to a monitor color characteristic model in a professional monitor.

The color characteristic model of the monitor may consist of a function that defines the relationship between the input RGB value and the output color characteristic shown on the monitor. For example, a web designer may output (or implement) an object or an image having his or her desired color characteristic on a monitor by adjusting an input RGB value using a color characteristic model.

As illustrated with reference to FIGS. 4 to 6, by using a corrected visibility function or an optimal visibility function, a color characteristic closer to a color characteristic actually perceived by a human eye may be predicted than a conventional standard visibility function. Therefore, if the input RGB value and output color characteristic relationship of the monitor's color characteristic model is defined by the corrected visibility function or the optimal visibility function, the web designer can determine that the monitor is closer to the color characteristic that the monitor desires based on the characteristic relationship. You can set the input RGB signal to implement.

In step 710, an RGB signal is input. The RGB signal may represent a specific RGB value.

In step 720, the spectral distribution of the output color output by the monitor can be measured, and a measurement of the spectral distribution can be generated.

In step 730, the output color characteristics of the spectral distribution measurements can be calculated using the visibility function. Here, the visibility function may be the modified visibility function or the optimal visibility function described above with reference to FIG. 2.

That is, data obtained by measuring the spectral distribution of colors reproduced on the monitor in response to various combinations of the input RGB signals may be generated, and tristimulus value XYZ values may be calculated by using the data and the visibility function. Further, a relationship between the input RGB value of the input RGB signal and the XYZ value of the output color characteristic can be derived.

In step 740, information about the input RGB value and the output color characteristic may be added to the input RGB value and output color characteristic relationship model. Here, the relational model may mean a data structure that provides unidirectional or bidirectional mapping between input RGB values and output color characteristics. For example, the relationship model can be a function or a look-up table.

In step 750, it may be checked whether there are additional RGB values to be added to the relationship model. If there are additional RGB values, step 760 may be performed. If there are no additional RGB values, step 770 may be performed.

In step 760, the RGB signal may be changed so that additional RGB values are input. Thereafter, step 710 is repeated.

In step 770, an input RGB value and output color characteristic relationship model is provided.

A user of a relational model, such as a web designer, can refer to the relational model to determine an input RGB value that matches his or her desired target color characteristics.

8 is a flowchart of a medical diagnosis method using a visibility function, according to an exemplary embodiment.

The difference between the color characteristics of the first observation site and the color characteristics of the second viewing site output from the medical monitor can be used for medical diagnosis. Therefore, there is a need for a means that can predict and quantify the differences in color characteristics as close as possible to the differences between the color characteristics perceived by the medical practitioner.

In this embodiment, the components of the visibility function generating device 100 or the visibility function generating device 100 described above with reference to FIG. 1 may be used for medical diagnosis.

In operation 810, the spectral distribution measurement unit 150 may generate the first spectral distribution measurement value by measuring the spectral distribution of the first observation portion of the subject to be output from the monitor. Here, the monitor may be a medical monitor. The measured spectral distribution can be a spectral distribution for the visible light region.

In operation 815, the calculator 160 may calculate the first color characteristic by applying a visibility function to the first spectral distribution measurement. Here, the visibility function may be the modified visibility function or the optimal visibility function described above with reference to FIG. 2.

In operation 820, the spectral distribution measurement unit 150 may generate a second spectral distribution measurement value by measuring the spectral distribution of the second observation portion of the subject to be output from the monitor.

In operation 825, the calculator 160 may calculate the second color characteristic by applying a visibility function to the second spectral distribution measurement.

In operation 830, the calculator 160 may calculate a color difference between the first color characteristic of the first viewing portion and the second color characteristic of the second viewing portion. The calculated color difference may be CIELAB ΔE ^* _ab color difference. The calculated color difference may be a difference in quantified color characteristics between the first observation site and the second viewing site.

In operation 840, the calculator 160 may generate a medical diagnosis result for the subject based on the calculated color difference. For example, when the calculated color difference is greater than or equal to the reference value, the calculator 160 may determine that the subject represents a symptom of a specific disease.

9 is a flowchart illustrating a method of applying a visibility function to an apparatus for evaluating color characteristics of an LED light source, according to an exemplary embodiment.

The color rendering index may refer to the correspondence with the color visible in sunlight and may be used as one of the tools for evaluating the quality of the light source.

In the visible light band, the spectral distribution of the LED light source has a narrower width and a sharper peak than the spectral distribution of the incandescent light source. Due to the characteristics of the spectral distribution of the LED light source, the color rendering property may not match the human visual perception characteristics when the existing standard visibility function is used.

In the following, a method is disclosed in which the modified visibility function or optimal visibility function described above with reference to FIG. 2 calculates the color rendering properties of an LED light source.

In step 910, the spectral distribution of the LED light source is measured, and the spectral distribution measurement of the LED light source is generated. The spectral distribution of the LED light source may have a spicy wavelength compared to the spectral distribution of the tungsten light source.

In step 915, color characteristics of the LED light source are calculated by applying a visibility function to the spectral distribution measurements of the LED light source. Here, the visibility function may be the modified visibility function or the optimal visibility function described above with reference to FIG. 2.

In step 920, the spectral distribution of the reference light source is measured, and the spectral distribution measurement of the reference light source is generated.

In step 925, the color characteristics of the reference light source are calculated by applying a visibility function to the spectral distribution measurements of the reference light source.

In step 930, the distance between the LED light source and the reference light source on the color coordinate system may be calculated based on the color property of the LED light source and the color property of the reference light source.

In step 940, the color rendering of the LED light source may be calculated based on the calculated distance. Here, the smaller the calculated distance, the better the color rendering of the LED light source may be determined.

In step 950, calculated color rendering may be provided.

As described above, the modified visibility or optimal visibility function described above is obtained from a professional monitor for color professionals, a medical monitor that requires accurate color information about a part of the human body, and different kinds of image devices. It can be used by devices using color information to be reproduced. In addition, the modified visibility function or optimal visibility function described above can be used by an imaging device that calculates color information using a spectral distribution of object colors reflecting human sensitivity to visible light.

The technical contents described above may be embodied in the form of program instructions that may be executed by various computer means and may be recorded in a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, the technical details described in the embodiments can be variously modified and modified. Therefore, the scope of the invention should not be limited to the described embodiments, but should be determined by the equivalents of the appended claims, as well as the appended claims.

100: visibility function generator
140: visual matching unit
150: spectral distribution measuring unit
160: the calculation unit
170:

Claims (19)

In the method by which the electronic device generates a visibility function,
Calculating a first color characteristic of the first spectral distribution and a second color characteristic of the second spectral distribution by applying a first visibility function to the measurement of the first spectral distribution and the measurement of the second spectral distribution, respectively;
Calculating a first color difference between the first color property and the second color property;
Calculating a third color characteristic of the first spectral distribution and a fourth color characteristic of the second spectral distribution by applying a second visibility function to the measurement of the first spectral distribution and the measurement of the second spectral distribution, respectively;
Calculating a second color difference between the third color property and the fourth color property; And
If the first color difference is smaller than the second color difference, determining the first visibility function as a modified visibility function
Lt; / RTI >
The second visibility function includes a short (S) cone function, a middle (M) cone function, and a long (L) cone function,
And the first visibility function is a visibility function obtained by shifting the S cone function of the second visibility function along a wavelength axis. The method of claim 1,
Generating a measurement of the first spectral distribution and a measurement of the second spectral distribution by measuring the spectral distribution of the first field and the spectral distribution of the second field, respectively.
Further comprising, the visibility function generation method. The method of claim 2,
Adjusting a first light emitted from a first light source illuminating the first field such that an observer recognizes that the first field and the second field visually have the same color characteristics as each other
Further comprising, the visibility function generation method. The method of claim 2,
And a second light illuminating the second field is emitted from an incandescent lamp and passed through a filter. The method of claim 3,
The first light source includes a light-emitting diode (LED) light source, a green LED light source, and a blue LED light source,
The adjusting of the light may include adjusting the first light by adjusting a magnitude of current applied to each of the red LED light source, the green LED light source, and the blue LED light source. The method of claim 1,
The second visibility function is a CIE 170: 2006 visibility function. The method of claim 1,
The first visibility function is plural and the modified visibility function is one or more,
Each of the S cone functions of the plurality of first visibility functions is a function in which the S cone functions of the second visibility function are shifted by different distances along the wavelength axis,
Selecting an visibility function that produces the smallest first color difference among the one or more modified visibility functions as an optimal visibility function; And
Providing the optimal visibility function
Further comprising, the visibility function generation method. Generating a first spectral distribution measurement by measuring the spectral distribution of the first observation site of the subject to be output from the monitor;
Generating a second spectral distribution measurement by measuring a spectral distribution of the second observation site of the subject output from the monitor;
Calculating a first color characteristic by applying a first visibility function to the first spectral distribution measurement;
Calculating a second color characteristic by applying the first visibility function to the second spectral distribution measurement;
Calculating a color difference between the first color characteristic and the second color characteristic; And
Generating a medical diagnosis result for the subject based on the color difference
Comprising a medical diagnostic method using the visibility function. 9. The method of claim 8,
Generating the first visibility function
Further comprising:
Generating the first visibility function,
Calculating a third color characteristic of the third spectral distribution and a fourth color characteristic of the fourth spectral distribution by applying a second visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively;
Calculating a second color difference between the third color property and the fourth color property;
Calculating a fifth color characteristic of the third spectral distribution and a sixth color characteristic of the fourth spectral distribution by applying a third visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively;
Calculating a third color difference between the fifth color property and the sixth color property; And
If the second color difference is smaller than the third color difference, determining the second visibility function as the first visibility function
Lt; / RTI >
The third visibility function includes a short (S) cone function, a middle (M) cone function, and a long (L) cone function,
And the second visibility function is a visibility function obtained by shifting the S cone function of the third visibility function along a wavelength axis. A computer-readable recording medium containing a program for carrying out the method of any one of claims 1 to 9. A spectral distribution measuring unit that provides a measurement of the first spectral distribution and a measurement of the second spectral distribution; And
Calculating a first color characteristic of the first spectral distribution and a second color characteristic of the second spectral distribution by applying a first visibility function to the measured value of the first spectral distribution and the measured value of the second spectral distribution, respectively, Calculating a first color difference between a first color characteristic and the second color characteristic, and applying a second visibility function to the measurement of the first spectral distribution and the measurement of the second spectral distribution, respectively, to determine the third color of the first spectral distribution. Calculating a color characteristic and a fourth color characteristic of the second spectral distribution, calculating a second color difference between the third color characteristic and the fourth color characteristic, and wherein the first color difference is smaller than the second color difference, A calculator configured to determine the first visibility function as a modified visibility function
Lt; / RTI >
The second visibility function includes a short (S) cone function, a middle (M) cone function, and a long (L) cone function,
And the first visibility function is a visibility function obtained by shifting the S cone function of the second visibility function along a wavelength axis. 12. The method of claim 11,
And the spectroscopic distribution measuring unit generates the measured value of the first spectral distribution and the measured value of the second spectral distribution by measuring the spectral distribution of the first field and the spectral distribution of the second field, respectively. The method of claim 12,
A visual matching unit that adjusts first light emitted from a first light source illuminating the first field so that an observer recognizes that the first field and the second field visually have the same color characteristics.
≪ / RTI > The method of claim 12,
And the second light illuminating the second field is emitted from an incandescent lamp and passes through a filter. The method of claim 13,
The first light source
Further comprising:
The first light source includes:
Light-emitting diode (LED) light sources;
Rust LED light source;
And blue LED light source
/ RTI >
And the visual matching unit adjusts the first light by adjusting a magnitude of current applied to each of the red LED light source, the green LED light source, and the blue LED light source. 12. The method of claim 11,
The second visibility function is a CIE 170: 2006 visibility function. 12. The method of claim 11,
Provider
Further comprising:
The first visibility function is plural and the modified visibility function is one or more,
Each of the S cone functions of the plurality of first visibility functions is a function in which the S cone functions of the second visibility function are shifted by different distances along the wavelength axis,
The calculation unit selects a visibility function that generates the smallest first color difference among the one or more modified visibility functions as an optimal visibility function,
The providing unit provides the optimum visibility function. The first spectral distribution measurement is generated by measuring the spectral distribution of the first observation site of the subject output from the monitor, and the second spectral distribution measurement by measuring the spectral distribution of the second observation site of the subject output from the monitor. Spectroscopic distribution measuring unit for generating a; And
A first color characteristic is calculated by applying a first visibility function to the first spectral distribution measurement, a second color characteristic is calculated by applying the first visibility function to the second spectral distribution measurement, and the first color characteristic is calculated. And a calculation unit configured to calculate a color difference between the second color characteristics and generate a medical diagnosis result for the subject based on the color difference.
Comprising a medical diagnostic device using a visibility function. 19. The method of claim 18,
The calculator generates a first visibility function,
The calculating unit calculates a third color characteristic of the third spectral distribution and a fourth color characteristic of the fourth spectral distribution by applying a second visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively. And calculating a second color difference between the third color characteristic and the fourth color characteristic, and applying a third visibility function to the measured value of the third spectral distribution and the measured value of the fourth spectral distribution, respectively. Compute a fifth color characteristic and a sixth color characteristic of the fourth spectral distribution, calculate a third color difference between the fifth color characteristic and the sixth color characteristic, and wherein the second color difference is smaller than the third color difference. The first visibility function is generated by determining the second visibility function as the first visibility function,
The third visibility function includes a short (S) cone function, a middle (M) cone function, and a long (L) cone function,
And the second visibility function is a visibility function obtained by shifting the S cone function of the third visibility function along a wavelength axis.

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