JPH11175048A - Color image conversion coefficient calculation method and color image conversion method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To bring close views of a color between a display image and a hard copy image without performing work so-called color equalization by vision. SOLUTION: As an illuminational condition of an environment that a display is set up, a color temp., luminance or illuminance and chromaticity of an objective color related to a certain specified color are detected. From the color temp., the luminance or the illuminance of the objective color related to the specified color, the color temp. of a display color (light source color) becoming the color equalization with the objective color related to the specified color is estimated as a color equalization point 40. By the chromaticity of the estimated color equalization point and color adaptation models by plural color data, as a conversion coefficient for converting an input color image to the color image to be displayed on a display, the coefficient making to coincide the views of the color of a light source color color image with an objective color image is calculated.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for calculating a color image conversion coefficient for matching the color appearance between a display image and a hard copy image, and the color appearance conversion between a display image and a hard copy image. And a method for converting a color image.

[0002]

2. Description of the Related Art The rapid development of computer technology has facilitated the creation and editing of color images, and the opportunity to create and edit color images has increased not only in designers but also in general offices. There is a problem that the color of the hard copy image and the color of the display image do not match when the image created in is printed.

For this reason, a color management technology or a color management system has been developed. In color management, a color expressed in a device-specific color space, such as an RGB space or a YMCK space, is represented by CIEX.
It manages a common color space independent of devices, such as YZ space and CIEL ^* a ^* b ^* space, based on
In addition to the description of the characteristics of each device in the device profile (Characterization) and the color conversion between the device-specific color space and the common color space using the device profile (Conversion), individual differences of each device and Calibration for aging is also included.

[0004] Conventionally, it has been considered that if the values are the same in the same color space, that is, if they match colorimetrically, they look the same color visually. In fact, color management systems have evolved with colorimetric matching as a color management goal, and when viewed under D50 or D65 illumination,
It has been said that if they are colorimetrically matched, they also match the appearance.

However, as already known from the literature (for example, Okajima et al., “Optics” 20, 1991, 363-368p), the color display and the color hard copy require the light source color (color of light emitted from the light source). ) And the object color (the color of the reflective or transmissive object), and even if the colors are colorimetrically matched by the conventional color management technology, the colors of the respective images do not appear to match. Especially in a general office environment where the surroundings of the display are brightly illuminated by fluorescent lamps with a low color temperature,
This tendency becomes remarkable.

[0006] To solve this problem, Japanese Patent Application Laid-Open No. H9-1997 discloses a method for matching the color appearance of an object color of a color hard copy with a light source color (display color, emission color) of a color display.
No. 98301 discloses a method of visually matching an object color and a light source color for a predetermined color, determining a color conversion parameter based on the correspondence, and performing color conversion on an image.

[0007]

However, the method disclosed in Japanese Patent Application Laid-Open No. 9-98301 has the disadvantage that the user of the system has to try a number of times until satisfactory color matching is achieved. is there. Moreover, the degree of matching of the color appearance between the display image and the hard copy image also depends on the color matching accuracy of the user, and if the color matching accuracy is poor, the difference between the two becomes more noticeable. It could be.

Therefore, the present invention performs a complicated and inaccurate operation of visual color matching even when a color image is created and edited on a display in a brightly illuminated environment like a general office. This makes it possible to make the color appearance of the display image and that of the hard copy image close to each other.

[0009]

According to the first aspect of the present invention, there is provided a method for calculating a conversion coefficient for converting an input color image into a color image to be displayed on a color display.
A first recognition step of recognizing an illumination condition of an environment in which the color display is installed, a second recognition step of recognizing characteristics of the color display, and a recognition result of the first and second recognition steps. A coefficient calculating step of calculating, as the conversion coefficient, a coefficient for matching the color appearance of the light source color image and the color of the object color image.

According to a third aspect of the present invention, in the method for converting an input color image into a color image to be displayed on a color display, a first recognition step of recognizing an illumination condition of an environment in which the color display is installed; A second recognition step of recognizing the characteristics of the color display, and a coefficient for matching the color appearance of the light source color image and the color appearance of the object color image based on the recognition results of the first and second recognition steps. And an image data conversion step of converting the input color image into a color image to be displayed on the color display using the coefficient calculated in the coefficient calculation step.

[0011]

In the color image conversion coefficient calculating method according to the first aspect of the present invention, in the first recognition step, the illumination condition of the environment in which the color display is installed is determined as the lighting condition of the object color for a specific color. The color temperature, the luminance or the illuminance, or the chromaticity are recognized, and the characteristics of the color display are recognized in the second recognition step.

In the coefficient calculating step, based on the illumination conditions recognized in the first recognition step and the display characteristics recognized in the second recognition step,
The chromaticity of the light source color that is the same color as the object color for the specific color is predicted, and the color data is corrected by the chromatic adaptation model. As the conversion factor,
A coefficient for matching the color appearance of the light source color image and the color of the object color image is calculated.

Therefore, even in a brightly illuminated environment such as a general office, a conversion for matching the color appearance between a display image and a hard copy image without performing the complicated and inaccurate work of visual color matching. The coefficient can be obtained.

In the color image conversion method according to the third aspect of the present invention, a complicated and inaccurate operation of visually matching colors is performed even in a brightly illuminated environment like a general office. Instead, the input color image is converted to a color image to be displayed on the display such that the color appearance of the display image matches the color appearance of the hard copy image.

[0015]

DESCRIPTION OF THE PREFERRED EMBODIMENTS [First Embodiment: Embodiment as Method for Calculating Color Image Conversion Coefficients] FIG. 1 shows an embodiment of a method for calculating color image conversion coefficients according to the first aspect of the present invention. The color image conversion coefficient calculation method according to the present embodiment generally includes a first recognition step 10, a second recognition step 20, and an ambient light information correction coefficient calculation step 30.

In a first recognition step 10, ambient light information of the environment where the display is installed is fetched to recognize the lighting conditions of the environment where the display is installed. In the second recognition step 20, the display characteristics information is fetched and the characteristics of the display are recognized. However, the display is a color display such as a color CRT display.

The capture of ambient light information in the first recognition step 10 and the capture of display characteristic information in the second recognition step 20 are performed, for example, by a method as shown in FIG. That is, in the environment where the display is installed, the hard copy 1 is irradiated with the illumination light 2 of the environment where the display is installed, and the reflected light 3 from the hard copy 1 is emitted as shown in FIG. The light 6 emitted from the display screen of the display 5 is detected by the sensor 7 as detected by the sensor 4 and as shown in FIG.

Specifically, in the first recognition step 10, the color temperature, luminance or illuminance, and chromaticity of an object color for a specific color are detected as the illumination conditions of the environment in which the display is installed. The specific color may be white or an achromatic color close to white, but may be hard copy paper white. In the method actually performed, the Munsell color chart N9 was set as the specific color. In the second recognition step 20, for example, the gradation reproduction characteristics of RGB single color and gray are detected as the characteristics of the display.

In the ambient light information correction coefficient calculating step 30, on the basis of the illumination conditions recognized in the first recognition step 10 and the display characteristics recognized in the second recognition step 20,
As a conversion coefficient for converting the input color image into a color image to be displayed on the display, a coefficient for matching the color appearance of the light source color image with the color appearance of the object color image is calculated.

In this embodiment, the ambient light information correction coefficient calculating step 30 includes a specific color chromaticity prediction step 40, a color appearance matching correction step 50, and a display conversion coefficient calculating step 60. The matching correction step 50 includes a color appearance matching correction main step 51 and a color appearance matching conversion coefficient calculating step 55.

In the specific color chromaticity prediction step 40, the color of the object color of the specific color and the luminance or illuminance recognized in the first recognition step 10 become the same as the object color of the specific color. The color temperature of the display color (light source color) is predicted as a color matching value (color matching point), and the predicted color matching value is converted into chromaticity.

As shown by the color matching curves 8 and 9 in FIG. 2, when the color temperature of an object color for a specific color is in the range of 5000 K or more, the color temperature of a display color that matches the object color is generally In an appropriate use environment, the color temperature is substantially equal to the color temperature of the object color regardless of the illuminance. However, when the color temperature of the object color is less than 5000 K, as the color temperature of the object color decreases, the color temperature of the display color that matches the object color becomes higher than the color temperature of the object color.

When the illumination of the environment in which the display is installed is dark, that is, when the brightness of the hard copy is within a range that can be reproduced by the display, such a difference in appearance is mainly due to the appearance of the light source color and the object color. (The color matching curve 8 in FIG. 2) indicates that when the illumination of the environment where the display is installed is bright and the brightness of the hard copy exceeds the range that can be reproduced on the display, the color reproduction range further differs. In addition, the difference between the color temperature of the display color that is equal to the object color and the color temperature of the object color is larger than that in the case of the color matching curve 8 (color matching curve 9 in FIG. 2). In addition,
In the case of the color matching curve 9 as well, the term “color matching” is used for convenience, but it is not strictly color matching, but means to match the closest color.

In the specific color / chromaticity prediction step 40, a function indicating a color matching curve such as a color matching curve 8, 9 is selected from the luminance or illuminance recognized in the first recognition step 10, and the selected function is used. Based on the color temperature recognized in the first recognition step 10, a color matching value, that is, a color temperature of a display color that matches the object color is predicted. The conversion of the predicted color matching values to chromaticity is
This is performed by using an LUT (lookup table) in which the relationship between the color temperature and the chromaticity of the equal color value is written in advance.

In a color appearance matching correction step 50, a color image for displaying an input color image on a display is obtained by using the color matching points predicted in the specific chromaticity prediction step 40 and a color adaptation model using, for example, eight pieces of color data. As a conversion coefficient for converting the color image into a color image, a color appearance matching conversion coefficient, that is, a coefficient for matching the color appearance between the light source color image and the object color image is calculated.

The eight pieces of color data constituting the chromatic adaptation model are provided with eight suitable points P1 to P8 on the L ^* a ^* b ^* space as shown in FIG. 4 as XYZ chromaticity coordinates of CIE1931. .

FIG. 3 shows an example of a color appearance matching correction processing routine in the color appearance matching correction process 50. In the color appearance matching correction processing routine 100, first, step 10 is executed.
1, the number n of the eight color data is set to 1,
Next, the process proceeds to step 102, where the n-th data is selected from the eight color data, and then the process proceeds to step 103, where the selected color data is stored in the CIE 193 as described above.
The XYZ chromaticity coordinates are converted to the XYZ chromaticity coordinates of 1 and then the process proceeds to step 104, where the XYZ chromaticity coordinates are converted to LMS cone response values.

LM from XYZ chromaticity coordinates of CIE1931
The conversion into the S cone response value is performed by converting the XYZ chromaticity coordinates before the conversion into X, Y, Z, and the LMS cone response value after the conversion into L, M, S
Then, the following arithmetic expression, Done by

Next, the routine proceeds to step 105, where the LM
The S cone response values L, M, and S are converted to the LMS cone response values Lw, M of the specific color in the environment where the display is installed.
Normalize with w and Sw. That is, as LMS cone response values L ′, M ′, S ′ after normalization, L ′ = L / Lw (21) M ′ = M / Mw (22) S ′ = S / Sw ( 23) Ask for.

The LMS cone response values Lw, Mw, and Sw of the specific color in the environment where the display is installed indicate the chromaticity of the specific color recognized in the first recognition step 10 by the CIE 19.
The XYZ chromaticity coordinates are calculated by converting the XYZ chromaticity coordinates into LMS cone response values according to equations (11) to (13).

On the other hand, based on the color matching points predicted in the specific color chromaticity prediction step 40, a value in the LMS space of the specific color to be corrected is obtained. Specifically, as shown in FIG. 5 (the flowchart is shown in block form for convenience), first, in step 201, the specific color in the environment where the display is installed, which is recognized in the first recognition process 10. Is converted to the XYZ chromaticity coordinates of the CIE 1931, and the chromaticity of the isochromatic point of the specific color predicted in the specific chromaticity prediction process 40 is converted to the XYZ chromaticity coordinates of the CIE 1931.
The YZ chromaticity coordinates are converted into LMS cone response values, and then the process proceeds to step 203, where partial adaptation correction is performed on the LMS cone response values.

The LMS cone response value of a specific color in the environment where the display is installed is calculated using Lw, M as described above.
Assuming that w and Sw are L and the LMS cone response values of the isochromatic points for the specific color are L "w, M" w and S "w, the partial adaptation correction in step 203 is performed by the following operation Formula, {k · L ″ w + (1-k) Lw} (31) {k · M ″ w + (1-k) Mw} (32) {k · S ″ w + (1-k) Sw｝ (33) k is a constant between 0 and 1, and is usually preferably about 0.5 to 0.75.

A color appearance match correction processing routine 10 shown in FIG.
At 0, the process proceeds from step 105 to step 106,
Equation (3) calculated in step 203 in FIG.
1) LM after partial adaptation correction represented by (32) and (33)
Using the S cone response value, the LMS cone response values L ′, M ′, and S ′ represented by Equations (21), (22), and (23) normalized in Step 105 are expressed as follows: The following equation: L ″ = ｛k · L ″ w + (1−k) Lw｝ L ′ (41) M ″ = ｛k · M ″ w + (1−k) Mw｝ M ′ ... (42) S ″ = ｛k · S ″ w + (1−k) Sw｝ S ′ (43) Color appearance matching correction is performed.

Further, the routine proceeds to step 107, where the LMS cone response values L ″, M ″,
S '' is calculated by the following equation: Is converted to XYZ chromaticity coordinates of CIE1931.

Thereafter, the routine proceeds to step 108, where X
The YZ chromaticity coordinates are converted into image data L ^* 'a ^* ' b ^* 'in L ^* a ^* b ^* space which is a device independent color space.

Next, the routine proceeds to step 109, where
The number n of the color data is incremented by one, and the process proceeds to step 110 to determine whether n is greater than 8. If it is determined that n is less than 8, the process returns to step 102 and returns to step 102. Steps 102 to 109 are repeated. The above processing is performed in the color appearance matching correction main step 51 in the color appearance matching correction step 50 shown in FIG.

If it is determined in step 110 that n is larger than 8, the process proceeds from step 110 to step 1
Proceeding to step 11, in a color appearance matching conversion coefficient calculation step 55 in the color appearance matching correction step 50, a 3 × 8 matrix coefficient is calculated as a color appearance matching conversion coefficient. In this case, a mapping relationship f between the values of the eight colors before the color appearance matching correction and the values after the color appearance matching correction is obtained, and the color difference between the value before correction and the value after correction is minimized. Thus, a 3 × 8 matrix coefficient is calculated.

That is, let each coefficient of the 3 × 8 matrix coefficient be mij (i = 1 to 3, j = 1 to 8),
Assuming that the image data values before conversion are L ^* , a ^* , b ^* , and the image data values after conversion are L ^* ′, a ^* ′, b ^* ′, L ^* ′ = m11 · L ^* + m12 · a ^* + m13 B ^* + m14 ^* L ^** a ^* + m15 ^* L ^** b ^* + m16 ^* a ^** b ^* + m17 ^* L ^** a ^** b ^* + m18 ^* K (61) a ^* '= m21 ^* L ^* + m22 ^{· a * + m23 · b *} + m24 · L * · a * + m25 · L * · b * + m26 · a * · b * + m27 · L * · a * · b * + m28 · K (62) b * '= m31 · L ^* + m32 ^* a ^* + m33 ^* b ^* + m34 ^* L ^** a ^* + m35 ^* L ^** b ^* + m36 ^* a ^** b ^* + m37 ^* L ^** a ^** b ^* + m38 ^* K (63) . K is a constant and is usually set to 1.

Assuming that the measured values of the above eight colors are Ln ^* and the predicted values are Ln ^* '(n = 1 to 8), taking the L ^* value as an example, the difference (error) En is given by: En = Ln ^* −Ln ^* ′ (71), and the total error E is given by E = ΣEn ² (72).

In order to minimize the sum E of the errors, L ^* ′ of the 3 × 8 matrix coefficients in the equation (61) is used ^.
The coefficient m1j (j = 1 to 8) for the value is determined. Coefficient for a ^* 'value of equation (62) m2j (j = 1~
8) and the coefficient m3 for the b ^* 'value in equation (63)
j (j = 1 to 8) is similarly determined.

As described above, as a conversion coefficient for converting an input color image into a color image to be displayed on a display, a coefficient for matching the color appearance between the display image and the hard copy image can be calculated.

Regarding the visual system, even with the same color, the appearance may be different between a continuous stimulus such as a hard copy and a discontinuous and periodic stimulus such as a CRT display. However, also for this, by using the information obtained in the second recognition step 20 in addition to the information obtained in the first recognition step 10, a coefficient corresponding to the difference in appearance can be calculated. .

In the display conversion coefficient calculating step 60, the display characteristics recognized in the second recognition step 20 are used to calculate
A coefficient for color conversion from the XYZ space to the RGB space unique to the display is calculated. Since color additivity is generally established between the XYZ space and the RGB space, the RGB single color and gray tone reproduction characteristics are detected as the display characteristics as described above, so that the RGB color is converted from the XYZ space.
A coefficient for color conversion to the B space can be uniquely determined. The conversion coefficient is calculated by, for example, a 3 × 3 matrix operation.

[Second Embodiment: Embodiment as Color Image Conversion Method] FIG. 6 shows an embodiment of a color image conversion method according to the third aspect of the present invention. The color image conversion method according to this embodiment includes a first recognition step 10 and a second
, An ambient light information correction coefficient calculating step 30, and an image data converting step 70.

The first recognition step 10 and the second recognition step 20 are the same as those of the first embodiment shown in FIG. The environmental light information correction coefficient calculation step 30 is the same as that of the first embodiment in that it includes a specific color chromaticity prediction step 40, a color appearance matching correction step 50, and a display conversion coefficient calculation step 60.

In this embodiment, however, the color appearance matching correction step 50 comprises only the color appearance matching correction main step 51. Accordingly, the color appearance matching correction step 50
Color appearance matching correction as described later is performed. The image data conversion step 70 includes a color appearance matching conversion step 80 and a display conversion step 90.

Xwa = Xwm + Xcc (81) Ywa = Ywm + Ycc (82) Zwa = Zwm + Zcc (83) .

Here, Xwa, Ywa, and Zwa are chromaticities of colors whose appearance matches the specific color on the hard copy on the display, and Xwm, Ywm, and Zwm are the colorimetry and the specific color on the hard copy on the display. The chromaticities Xcc, Ycc, and Zcc of the colors that match each other are corrections whose magnitude changes depending on the color temperature.

FIG. 7 shows the relationship between the color temperature T and the correction amounts Xcc, Ycc, Zcc when the illumination of the environment where the display is installed is dark and the brightness of the hard copy is within the range that can be reproduced on the display. The relation is as follows: Xcc = 68717exp (−0.0021T) (91) Ycc = 27699exp (−0.0019T) (92) Zcc = 93281exp (−0.0022T) (93) Is done.

Therefore, the relational expressions (91) and (92)
From (93) and the above-mentioned relational expressions (81), (82), and (83), it is possible to predict a color matching point for a specific color.
In the specific color chromaticity prediction step 40 of this embodiment, by calculating the correction amounts Xcc, Ycc, and Zcc in this way, the color matching point of the specific color is predicted.

In the color appearance matching correction step 50, the color matching point predicted in the specific color chromaticity prediction step 40 is, for example, 72
Color appearance matching correction is performed using a color adaptation model based on 9 (9 × 9 × 9) color data, and in a color appearance matching conversion step 80, the input image is corrected based on the color data after the color appearance matching correction. The data is color-converted so that the color appearance of the light source color image and the color appearance of the object color image match. The 729 pieces of color data constituting the color adaptation model are CIE1
931 as XYZ chromaticity coordinates.

FIG. 8 shows an example of a color appearance match correction conversion processing routine in the color appearance match correction step 50 and the color appearance match conversion step 80. In the color appearance match correction conversion routine 300, first, In step 301,
The number n of the 729 pieces of color data is set to 1, and then the process proceeds to step 302, where
Select the th data, then go to step 303,
The selected color data is converted into CIE 1931 XYZ chromaticity coordinates as described above, and then the process proceeds to step 304, where the XYZ chromaticity coordinates are converted into LMS cone response values. The conversion from the CIE 1931 XYZ chromaticity coordinates to the LMS cone response value is performed using the above equations (11), (12), and (13).
Done by

Next, the routine proceeds to step 305, where the LM
The S cone response values L, M, and S are calculated by using the above-described equations (21) and (2).
2) According to (23), LMS cone response values Lw, Mw, Sw of a specific color in the environment where the display is installed.
Normalize with

On the other hand, in the same manner as in the first embodiment, based on the color matching points predicted in the specific color chromaticity prediction step 40, specific processing to be corrected is performed by the processing shown in FIG. A value of the color in the LMS space is obtained in advance.

In the color appearance matching correction conversion processing routine 300 of FIG. 8, the process proceeds from step 305 to step 306, and the equations (31), (32), and (33) calculated in step 203 of FIG. Using the LMS cone response value after the partial adaptation correction represented, the LMS represented by the equations (21), (22), and (23) normalized in step 305
For the cone response values L ′, M ′, S ′, Equation (41)
(42) According to (43), color appearance matching correction is performed.

Further, the process proceeds to step 307, where the LMS cone response values L ″, M ″,
S ″ is calculated by the formulas (51), (52), and (53) using the CIE
It is converted into 1931 XYZ chromaticity coordinates.

Thereafter, the flow advances to step 308, where X
The YZ chromaticity coordinates are converted into image data L ^* 'a ^* ' b ^* 'in L ^* a ^* b ^* space which is a device independent color space.

Next, the routine proceeds to step 309, where
The number n of the 29 color data is incremented by one,
The process further proceeds to step 310, where it is determined whether or not n is greater than 729. If it is determined that n is less than or equal to 729, the process returns to step 302 and proceeds to steps 302 to 30.
Repeat step 9.

If it is determined in step 310 that n is larger than 729, the process proceeds from step 310 to step 311 in which the 729 color data obtained so far are stored in a three-dimensional LUT as shown in FIG. I do. The above processing is performed in the color appearance matching correction step 50 shown in FIG.

Next, proceeding to step 312, in the color appearance matching conversion step 80 in the image data conversion step 70, the color data stored in the three-dimensional LUT is used.
The input image data is transformed in L ^* a ^* b ^* space.

The address of the three-dimensional LUT indicates the coordinates (position) in the L ^* a ^* b ^* space.
When the coordinate value between the grid points is obtained as shown by the point Q, the input image data is regarded as an internal division point of the grid point, and the converted image data is converted into eight surrounding grid points Q0 to Q0. The weighted sum of the output values of Q7 is obtained. That is, the value of the converted image data is D, and eight surrounding grid points Q0
Assuming that the output values of to Q7 are Di (i = 0 to 7), Becomes α, β, and γ are values of 0 or more and 1 or less.

Although this example uses cubic interpolation, an interpolation method such as tetrahedral interpolation, pyramid interpolation, or prism interpolation may be used.

As described above, for the visual system, continuous stimuli such as hard copy, CRT
A discontinuous and periodic stimulus such as a display may look different,
By using the information obtained in the second recognition step 20 in addition to the information obtained in the first recognition step 10, a coefficient corresponding to the difference in appearance can be calculated and converted.

As in the first embodiment, the display conversion coefficient calculation step 60 uses the display characteristics recognized in the second recognition step 20 to convert the color from the XYZ space to the display-specific RGB space. Calculate the coefficient. XYZ
Since color additivity is generally established between the space and the RGB space, as described above, R
By detecting the tone reproduction characteristics of GB single color and gray, it is possible to uniquely determine the coefficient of the color conversion from the XYZ space to the RGB space. The conversion coefficient is, for example,
It is calculated by a 3 × 3 matrix operation.

In the display conversion step 90 in the image data conversion step 70, the L ^* a ^* b ^* image data from the color appearance matching conversion step 80 is converted into the display conversion coefficient calculation step 60.
Is converted into display image data, that is, display-specific RGB image data, by the conversion coefficient from. At this time, the L ^* a ^* b ^* image data from the color appearance matching conversion step 80 is once converted into values Xcrt, Ycr on the display when the environment in which the display is installed is a dark room.
The data is converted to t, Zcrt and sent to the display conversion step 90.

As described above, the input color image can be converted into the color image to be displayed on the display so that the color appearance of the display image matches the color appearance of the hard copy image.

[Other Embodiments or Modifications] In the above-described first embodiment, in the color image conversion coefficient calculation method, in the specific chromaticity prediction step 40, a function using luminance and color temperature as parameters is used. Predict the chromaticity of the color matching points,
In the color appearance matching correction step 50, a conversion coefficient for directly converting input image data is calculated by using a chromatic adaptation model and a least squares method. In the chromaticity chromaticity prediction step 40, the chromaticity of the isochromatic point is predicted using a function using the chromaticity as a parameter. In the color appearance matching correction step 50, the coefficient for the three-dimensional LUT is calculated using the chromatic adaptation model. Is calculated.
Of course, in the color image conversion coefficient calculation method, the coefficient for the three-dimensional LUT may be calculated in the color appearance matching correction step 50, and in the color image conversion method, the input may be calculated in the color appearance matching correction step 50. A conversion coefficient for directly converting image data may be calculated.

[0068]

As described above, according to the present invention, even when a color image is created and edited on a display in a brightly lit environment such as a general office, the color matching by visual observation is complicated. In addition, the colors of the display image and the hard copy image can be made closer to each other without performing an incorrect operation.

[Brief description of the drawings]

FIG. 1 is a diagram showing one embodiment of a color image conversion coefficient calculation method of the present invention.

FIG. 2 is a diagram showing a prediction curve using luminance and color temperature as parameters in the case of FIG. 1;

FIG. 3 is a diagram showing a color appearance match correction processing routine in the case of FIG. 1;

FIG. 4 is a diagram provided for describing a processing routine of FIG. 3;

FIG. 5 is a diagram showing a part of processing in the case of FIG. 1;

FIG. 6 is a diagram showing one embodiment of a color image conversion method of the present invention.

FIG. 7 is a diagram showing a prediction curve using chromaticity as a parameter in the case of FIG. 6;

8 is a diagram showing a color appearance matching correction conversion processing routine in the case of FIG. 6;

FIG. 9 is a diagram showing a configuration of a three-dimensional LUT.

FIG. 10 is a diagram provided for explanation of interpolation in a three-dimensional LUT.

FIG. 11 is a diagram illustrating an example of a method for capturing ambient light information and display characteristic information.

[Explanation of symbols]

 1 0 First Recognition Step 20 Second Recognition Step 30 Ambient Light Information Correction Coefficient Calculation Step 40 Specific Chromaticity Prediction Step 50 Color Appearance Match Correction Step 60 Display Conversion Coefficient Calculation Step 70 Image Data Conversion Step 80 Color Appearance Matching conversion process 90 Conversion process for display

──────────────────────────────────────────────────の Continued on the front page (51) Int.Cl. ⁶ Identification symbol FI H04N 1/60 H04N 1/40 D 1/46 1/46 Z // G06T 5/00 G06F 15/68 310A

Claims (5)

[Claims] 1. A method for calculating a conversion coefficient for converting an input color image into a color image to be displayed on a color display, comprising: a first recognition step of recognizing an illumination condition of an environment in which the color display is installed. A second recognition step of recognizing the characteristics of the color display; and a recognition result of the first and second recognition steps.
A coefficient calculating step of calculating, as the conversion coefficient, a coefficient for matching a color appearance between the light source color image and the object color image, a color image conversion coefficient calculation method. 2. The color image conversion coefficient calculation method according to claim 1, wherein the coefficient calculation step is performed when the color temperature as the illumination condition recognized by the first recognition step is less than 5000K. A method for calculating a color image conversion coefficient, comprising: calculating a conversion coefficient at which a color temperature of the light source color image is higher than a color temperature as the illumination condition. 3. A method for converting an input color image into a color image to be displayed on a color display, comprising: a first recognition step of recognizing an illumination condition of an environment in which the color display is installed; A second recognition step of recognizing; and a recognition result of the first and second recognition steps.
A coefficient calculating step of calculating a coefficient for matching the color appearance of the light source color image and the color appearance of the object color image; and a color image displaying the input color image on the color display by the coefficient calculated in the coefficient calculating step. An image data conversion step of converting the image data into a color image. 4. The color image conversion method according to claim 3, wherein said coefficient calculating step is performed when said color temperature as said illumination condition recognized by said first recognition step is less than 5000K. And calculating a coefficient at which the color temperature of the light source color image is higher than the color temperature as the illumination condition. 5. An image processing apparatus for calculating a conversion coefficient according to the color image conversion coefficient calculation method according to claim 1 or 2, or converting an input color image according to the color image conversion method according to claim 3.