JP2020057999A - Information processing device and program - Google Patents

Abstract

To make gloss correction easier than in the case of correcting a component value of each color on the basis of an empirical rule.SOLUTION: An information processing device comprises: an acquisition unit that acquires a color component value and a gloss component value measured from a printed matter; and a generation unit that generates a model that defines a relationship between the respective color components of a basic color and a specific color of a printing apparatus side, that reproduces the acquired color component value and the gloss component value.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing device and a program.

  There is a printing apparatus which can output a glossy printed matter by adding a special color such as silver or gold to the basic colors cyan, magenta, yellow and black.

JP 2010-152533 A

  Conventional color correction uses colorimetric values expressed in the Lab color system, but it is not possible to acquire a gloss component only with these colorimetric values. Further, the degree of gloss is not determined by the component value of the single spot color, but changes depending on the component values of cyan, magenta, yellow, and black.

  An object of the present invention is to facilitate correction of gloss as compared with a case where component values of respective colors are corrected based on an empirical rule.

The invention according to claim 1, wherein an acquisition unit that acquires a value of a color component and a value of a gloss component measured from a printed matter, and a printing apparatus side that reproduces the acquired value of the color component and the value of the gloss component. And a generation unit that generates a model that defines the relationship between the basic color and each component value of the spot color.
The invention according to claim 2, wherein the generation unit specifies the component value of the special color based on the measured value of the color component and the value of the gloss component, and then uses the component value of the special color. 2. The information processing apparatus according to claim 1, wherein a component value of a basic color is specified.
The invention according to claim 3, wherein the generation unit sets the component value of one of the basic colors to a maximum value or a minimum value and provides a value of the component value of the special color that gives the value of the gloss component. The information processing apparatus according to claim 2, wherein
The invention according to claim 4, wherein the generation unit includes a case where a color reproduced by the base color other than the base color set to the maximum value or the minimum value and the special color is included in a color gamut. 4. The information processing apparatus according to claim 3, wherein the component value of the spot color is set as a maximum value or a minimum value of the component values of the spot color satisfying the gloss component value.
The invention according to claim 5, wherein the generation unit is configured to provide a color reproduced by the component value of the basic color and the component value of the special color to fill a color gamut and provide the value of the gloss component. The information processing apparatus according to claim 2, wherein the maximum value of the component values of the spot color that satisfies the range of the component values is specified.
The invention according to claim 6, wherein the generation unit uses, as the color gamut, a color gamut whose outer edge is defined by a limit value regarding a sum of respective component values of the basic color and the special color. An information processing apparatus as described in the above.
The invention according to claim 7 is characterized in that the first model generated for the first printing device to be simulated and the second model generated for the second printing device used for the target are A correction value generation unit that generates a correction value for correcting the input data of the first printing device so that the gloss component of the printed material by the first printing device approaches the gloss component of the printed material by the second printing device based on the first printing device. The information processing apparatus according to claim 1, further comprising:
The invention according to claim 8 is the information processing apparatus according to claim 7, wherein the input data is provided by a combination of both or one of silver and gold, and cyan, magenta, yellow, and black.
The invention according to claim 9 is the information processing apparatus according to claim 7 or 8, wherein the input data is given by a combination of both or one of silver and gold and red, green, and blue.
The invention according to claim 10, wherein when a copy of a target printed matter is printed by the printing apparatus, pixels obtained by optically reading the target printed matter using the model generated in advance for the printing apparatus. 2. The information processing apparatus according to claim 1, further comprising a conversion unit configured to convert a value of a color component and a value of a gloss component for each component into a component value of a basic color and a special color of the printing apparatus.
The invention according to claim 11, wherein the color component is provided based on a diffuse reflection image of the target print, and the gloss component is a difference between a diffuse reflection image and a specular reflection image of the target print. The information processing apparatus according to claim 10, wherein the information processing apparatus is provided based on an image.
In the invention according to claim 12, the color component is provided based on a diffuse reflection image of the printed matter, and the gloss component is provided based on a difference image between a specular reflection image of the printed matter and the diffuse reflection image. The information processing device according to claim 1, wherein
The invention according to claim 13 is a computer which prints a color component value and a gloss component value measured from a printed matter on a computer, and reproduces the acquired color component value and the gloss component value. This is a program for executing a function of generating a model that defines the relationship between the basic color and each component value of the spot color on the device side.

According to the first aspect of the invention, it is possible to easily correct the gloss as compared with the case where the component values of each color are corrected based on an empirical rule.
According to the second aspect of the invention, it is possible to specify the component values of the basic color and the special color for reproducing the gloss of the printed matter by calculation.
According to the third aspect of the present invention, the component values of the basic color and the special color that reproduce the gloss of the printed matter can be specified by calculation.
According to the fourth aspect of the invention, it is possible to specify the component values of the basic color and the special color for reproducing the gloss of the printed matter by calculation.
According to the invention of claim 5, gloss reproducibility can be improved.
According to the invention described in claim 6, gloss reproducibility can be improved.
According to the invention of claim 7, gloss reproducibility can be brought close to the target value.
According to the eighth aspect of the invention, the reproducibility of gloss can be made close to the target value.
According to the ninth aspect, the reproducibility of gloss can be made close to the target value.
According to the tenth aspect, the gloss of the target printed matter can be reproduced in the printed matter.
According to the eleventh aspect, the gloss of the target printed matter can be reproduced in the printed matter.
According to the twelfth aspect, a model can be easily generated.
According to the thirteenth aspect, it is possible to easily correct the gloss as compared with the case where the component values of each color are corrected based on an empirical rule.

FIG. 2 is a diagram illustrating an example of the overall configuration of a gloss adjustment system used in the first embodiment. FIG. 4 is a diagram illustrating an example of a patch chart. FIG. 9 is a diagram illustrating a specific example of a device-dependent profile generated for a simulation printing apparatus. FIG. 9 is a diagram illustrating an example of a functional configuration of a simulation printing apparatus that uses a generated device-dependent profile. 5 is a flowchart illustrating an outline of a processing procedure in the present embodiment. FIG. 3 is a diagram illustrating a relationship between a silver component value and a color gamut. FIG. 3 is a diagram illustrating a relationship between a silver component value and glossiness. FIG. 4 is a diagram illustrating a relationship between glossiness and a silver component value at a certain chromaticity value. FIG. 9 is a diagram illustrating a specific example 1 of a procedure of generating an inverse model by the color + gloss prediction model generation device. 11 is a flowchart illustrating an example of a processing procedure executed by the color + gloss prediction model generation apparatus to acquire a maximum component value of silver. 11 is a flowchart illustrating an example of a processing procedure executed by a color + gloss prediction model generation apparatus to acquire a minimum component value of silver. FIG. 7 is a diagram illustrating a change in color gamut due to the sum of coverage rates. FIG. 9 is a diagram illustrating a specific example 2 of a procedure of generating an inverse model by the color + gloss prediction model generation device. FIG. 9 is a diagram illustrating an example of a glossy outline data group in which the upper limit of the total amount of the coverage rate is 150%. FIG. 14 is a diagram illustrating an image of a process in step 22 of FIG. 13. FIG. 7 is a diagram illustrating a relationship between a silver coverage ratio and a gloss level for each of the total coverage ratios of basic colors. FIG. 4 is a diagram illustrating a geometric condition of BRDF. It is a figure explaining other specific examples of a texture information acquisition device. FIG. 3 is a diagram illustrating an example of a reflection image acquired by a texture information acquisition device. (A) is an example of a diffuse reflection image, (b) is an example of a specular reflection image, and (c) is an example of a difference image between the diffuse reflection image and the specular reflection image. It is a figure explaining the example of functional composition of a texture information acquisition device. FIG. 9 is a diagram illustrating an example of the overall configuration of a gloss adjustment system used in a second embodiment. FIG. 3 is a diagram illustrating a configuration example of a glossy texture reproduction profile. FIG. 4 is a diagram illustrating processing functions of a printing apparatus in which a glossy texture reproduction profile is set. FIG. 2 is a diagram illustrating an example of an external appearance of an image forming apparatus in which a function of acquiring texture information and a printing function are integrated. FIG. 3 is a diagram illustrating a connection example between modules constituting an image forming apparatus. It is a figure explaining the example of composition of a gloss adjustment system. It is a figure explaining other examples of a glossy texture reproduction profile.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
<First Embodiment>
<System configuration>
FIG. 1 is a diagram illustrating an example of the overall configuration of a gloss adjustment system 1 used in the first embodiment.
The gloss adjustment system 1 includes a target printing device 10 used as a reference machine and a simulation printing device 20 to be adjusted.
The simulation printing device 20 is an example of a first printing device, and the target printing device 10 is an example of a second printing device.

When adjusting the gloss reproducibility, the same print data Cin is input to the target printing device 10 and the simulation printing device 20. In the case of this embodiment, print data for gloss correction is given as the print data Cin.
The print data Cin for gloss correction corresponds to a color chart composed of a plurality of patches having different combinations of colors including metal colors and densities.
The print data Cin here is an example of input data.

In the print data Cin for gloss correction in the present embodiment, the color of each pixel is represented by, for example, cyan (C), magenta (M), yellow (Y), black (K), and special color (S).
In the present embodiment, four colors of cyan (C), magenta (M), yellow (Y), and black (K) are referred to as basic colors. The special color (S) is a color component that gives metallic luster, for example, and corresponds to silver (Si), gold (Go), and the like.
In the print data Cin, the color of each pixel may be represented by five colors of a basic color and silver (Si), or may be represented by five colors of a basic color and gold (Go). It may be expressed by six colors of silver (Si) and gold (Go).

The target printing device 10 and the simulation printing device 20 print and output an image corresponding to the print data Cin on a recording medium. In the present embodiment, the recording medium after printing is referred to as printed matter 30.
In the present embodiment, a printed matter 30 output by the target printing apparatus 10 to which the gloss correction print data Cin is given is called a patch chart PT, and is output by the simulation printing apparatus 20 to which the gloss correction print data Cin is given. The printed material 30 is referred to as a patch chart PS.
FIG. 2 is a diagram illustrating an example of the patch charts PT and PS. The patch charts PT and PS are composed of an array of a plurality of patches 31 having different colors and densities. Patch charts PT and PS according to the present embodiment include silver (Si) in addition to the basic colors.

Returning to the description of FIG.
The gloss adjustment system 1 includes a texture information acquisition device 40 that reads texture information from each of the patch charts PT and PS, and a color + gloss prediction model generation device that generates a prediction model that predicts a relationship between the read texture information and the print data Cin. 50 and a device-dependent profile generation device 60 that generates a profile that brings the reproduction characteristics of the printed material 30 (PS) of the simulation printing device 20 closer to the reproduction characteristics of the printed material (PT) of the target printing device 10.
Here, the texture information acquisition device 40 is an example of an acquisition unit. The color + gloss prediction model generation device 50 and the device-dependent profile generation device 60 are both examples of a generation unit. Note that the device-dependent profile generation device 60 is also an example of a correction value generation unit. The correction value generator is one form of a generator.
Note that the texture information acquisition device 40, the color + gloss prediction model generation device 50, and the device-dependent profile generation device 60 are also examples of an information processing device.

In the case of the present embodiment, the texture information includes a color component value and a gloss component value.
The color component values are represented by, for example, L ^* , a ^* , and b ^* systems. The gloss component value is represented by, for example, gloss gloss.
The texture information is acquired for each of the patch charts PT and PS.
In FIG. 1, the texture information of the printed material 30 (PT) of the target printing device 10 and the texture information of the printed material (PS) of the simulation printing device 20 are acquired by using one texture information acquisition device 40. You may acquire using the texture information acquisition device 40 of a stand.
In that case, it is assumed that the reading characteristics of the two texture information acquisition devices 40 have been adjusted by advance adjustment.

The color + gloss prediction model generation device 50 generates a forward model and an inverse model of the printing device based on the provided texture information.
The color + gloss prediction model generation device 50 according to the embodiment generates a forward model for the target printing device 10 and generates an inverse model for the simulation printing device 20.
The forward model is represented by the following equation when the spot color is silver (Si), the color component values are L ^* , a ^* , b ^* , and the gloss component value is gloss (gloss).
(L ^* , a ^* , b ^* , gloss) = f (C, M, Y, K, Si) ... Equation 1
f is a forward function. The input values are the component values of cyan (C), magenta (M), yellow (Y), black (K), and silver (Si), and the output values are L ^* , a ^* , b ^* , and gloss. is there.
The inverse model is expressed by the following equation when the spot color is silver (Si), the color component values are L ^* , a ^* , b ^* , and the gloss value of the gloss component value is gloss.
(C, M, Y, K, Si) = g ⁻¹ (L ^* , a ^* , b ^* , gloss) Equation 2
g ⁻¹ is an inverse function. The input values are the component values of L ^* , a ^* , b ^* , and gloss, and the output values are cyan (C), magenta (M), yellow (Y), black (K), and silver (Si). is there.

The device-dependent profile generation device 60 generates a device-dependent profile for matching the color and gloss reproduction characteristics of the simulation printing device 20 with the color and gloss reproduction characteristics of the target printing device 10.
The device-dependent profile is information unique to the simulation printing device 20 to be adjusted. Therefore, if the simulation printing apparatus 20 to be adjusted is different, the contents of the device-dependent profile are different.
Here, the color component value and the gloss value measured for the patch chart PT output from the target printing apparatus 10 are used as the color component value and the gloss value on the right side of Expression 2.

Note that the relationship between the color component value and the gloss value measured for the patch chart PT and the print data Cin used for printing the patch chart PT is expressed by Expression 1.
Therefore, the device-dependent profile is expressed as the following expression.
Cout = g ⁻¹ (f (Cin)) Equation 3
The function of the device-dependent profile is g ⁻¹ f.
Cout here corresponds to print data for reproducing the input color component values and glossiness by the simulation printing apparatus 20.

FIG. 3 is a diagram illustrating a specific example of the device-dependent profile generated for the simulation printing apparatus 20.
The device-dependent profile shown in FIG. 3 indicates an input / output relationship of print data including only silver (Si) as a special color.
In the example of FIG. 3, when the print data Cin having the component value of silver (Si) of “10” is given, the print data Cout in which the component value of silver (Si) is corrected to “11” is an unillustrated image. If output to the forming unit, the color and gloss reproduction characteristics of the simulation printing device 20 and the color and gloss reproduction characteristics of the target printing device 10 match.
When print data Cin having a silver (Si) component value of “20” is given, print data Cout in which the silver (Si) component value has been corrected to “23” is output to an image forming unit (not shown). Then, the color and gloss reproduction characteristics of the simulation printing device 20 and the color and gloss reproduction characteristics of the target printing device 10 match.
When print data Cin having a silver (Si) component value of “30” is given, print data Cout in which the silver (Si) component value is corrected to “32” is output to an image forming unit (not shown). Then, the color and gloss reproduction characteristics of the simulation printing device 20 and the color and gloss reproduction characteristics of the target printing device 10 match.

FIG. 4 is a diagram illustrating an example of a functional configuration of the simulation printing apparatus 20 that uses the generated device-dependent profile.
The simulation printing apparatus 20 includes a data correction unit 21 that corrects arbitrary print data Cin using the device-dependent profile 21A, and an image that prints a printed matter P on which an image corresponding to the corrected print data Cout is printed on a recording medium. And a forming part 22.
The functions of the data correction unit 21 and the image forming unit 22 in the present embodiment are realized, for example, by executing a program by an image processing processor.
If data matching the input print data Cin exists in the device-dependent profile 21A, the data correction unit 21 outputs the print data Cout associated with the print data Cin.
On the other hand, when data matching the input print data Cin does not exist in the device-dependent profile 21A, the data correction unit 21 prints the print data Cout associated with the approximate value of the input print data Cin, or The print data Cout calculated using the approximate value of the input print data Cin is output.
The image forming unit 22 employs a known technique, and a detailed description thereof will be omitted. The image forming unit 22 uses ink or toner as a color material used for forming an image.

<Processing example of the color + gloss prediction model generation device 50>
Here, a method of generating the inverse model g- ¹ of the simulation printing device 20 by the color + gloss prediction model generation device 50 will be described.
Note that a known method may be used to generate the forward model f of the target printing device 10 by the color + gloss prediction model generation device 50. That is, each component value of the print data as the input data may be associated with the color component value and the gloss value measured for the patch chart PT.
FIG. 5 is a flowchart illustrating an outline of a processing procedure according to the present embodiment. The symbol S in the figure represents a step.

First, the color + gloss prediction model generation device 50 uses the silver (Si) used in the simulation printing device 20 based on the color component value and the gloss component value of each patch measured from the patch chart PT output by the target printing device 10. ) Is specified (step 1).
This relationship is expressed by the following equation.
Si = h ^-1 (L ^* , a ^* , b ^* , gloss)) Equation 4
Here, the component value of silver (Si) is specified first because the component value of silver (Si) has a large effect on the width of a reproducible color gamut and glossiness (gloss).

FIG. 6 is a diagram illustrating the relationship between the component values of silver (Si) and the color gamut. The horizontal axis of FIG. 6 is a ^* , and the vertical axis is L ^* .
As shown in FIG. 6, the basic color gamut excluding the gloss is maximum when the component value of silver (Si) is 0, and becomes minimum when the component value is 100.
The arrows in the figure indicate the reduction of the outer edge of the color gamut as the component value of silver (Si) increases.
The component value is a numerical value representing the consumption amount of the color material corresponding to silver (Si), and is given as a numerical value when the consumption amount required to cover the entire printing range is 100.

FIG. 7 is a diagram for explaining the relationship between the component value of silver (Si) and the glossiness (gloss). The horizontal axis in FIG. 7 is the gloss (gloss), and the vertical axis is the chromaticity value (L ^* a ^* b ^* ). An area indicated by a rectangle represents a color gamut corresponding to a component value of silver (Si).
As shown in FIG. 7, there is a tendency that the glossiness (gloss) tends to increase as the component value of silver (Si) increases. However, the saturation decreases as the component value of silver (Si) increases. In other words, as the component value of silver (Si) increases, the color gamut is compressed in the gray direction.
Also, from FIG. 7, it can be seen that there is a range in the component values of silver (Si) that can obtain the same gloss (gloss).
For example, the color component value and glossiness (gloss) indicated by white circles at the positions where the arrows intersect in the figure are not only included in the color gamut where the component value of silver (Si) is "20", but also in the color gamut of silver (Si). Is included also in the color gamut of “50”. On the other hand, the color component values and glossiness (gloss) indicated by white circles are not included in the color gamut where the component value of silver (Si) is “0”, and are not included in the color gamut where the component value is “100”. .

FIG. 8 is a diagram illustrating the relationship between the glossiness (gloss) and the component value of silver (Si) at a certain chromaticity value (L ^* a ^* b ^* ). The horizontal axis of FIG. 8 is the gloss (gloss), and the vertical axis is the component value of silver (Si).
The chromaticity values (L ^* a ^* b ^* ) in FIG. 8 correspond to specific chromaticity values indicated by arrows in FIG.
FIG. 8 shows the relationship between the locus of the maximum component value and the locus of the minimum component value of silver (Si) that satisfies each glossiness (gloss).
In Step 1, one component value of silver (Si) is specified within a range satisfying the chromaticity value and glossiness that can be reproduced by the simulation printing apparatus 20. In other words, one component value of silver (Si) is specified in a range of a line segment sandwiched between a straight line indicated by a dotted line and each locus in the drawing. A specific specific method will be described later.

Returning to the description of FIG.
Next, the color + gloss prediction model generation device 50 reproduces, with the simulation printing device 20, the silver (Si) component value specified in step 1 and the color component value and the gloss component value corresponding to each component value. The component value of the basic color is specified (step 2).
This relationship is expressed by the following equation.
(C, Y, M, K) = g _h ^-1 (L ^* , a ^* , b ^* , gloss, Si) Equation 5
The function g _h ^-1 here is an inverse model of the simulation printing apparatus 20 in which silver (Si) is added as a constraint.
The values specified in steps 1 and 2 are recorded as the print data Cout of the device-dependent profile shown in FIG.
Hereinafter, specific examples 1 and 2 of the processing procedure illustrated in FIG. 5 will be described.

<Specific example 1>
FIG. 9 is a diagram illustrating a specific example 1 of a procedure of generating an inverse model by the color + gloss prediction model generation device 50. The symbol S in the figure represents a step.
The process shown in FIG. 9 corresponds to step 1 (see FIG. 5).
First, the color + gloss prediction model generation device 50 acquires the maximum component value of silver (Si) from L ^* a ^* b ^* and gloss (step 11).
FIG. 10 is a flowchart illustrating an example of a processing procedure executed by the color + gloss prediction model generation device 50 to obtain the maximum component value of silver (Si).
In the present embodiment, the predicted value of the component value of silver (Si) is determined so that the color gamut is as wide as possible.
First, the color + gloss prediction model generation device 50 calculates the remaining three colors from L ^* a ^* b ^* and gloss corresponding to a patch whose arbitrary one of the basic colors (ie, CMYK) has a component value of “0”. And the component value of silver (Si) are predicted (step 111).
For example, the component values of cyan (C) are set to zero, and magenta (M), yellow (Y), and black (K) for reproducing a given color component value (L ^* a ^* b ^* ) and gloss (gloss). ) And silver (Si) are predicted by calculation.

Subsequently, the color + gloss prediction model generation device 50 determines whether or not the predicted component values of the three colors and the component value of silver (Si) are within the color gamut (step 112).
If a positive result is obtained in step 112, the color + gloss prediction model generation device 50 sets the component value of silver (Si) predicted in step 111 as the maximum component value in step 11 (see FIG. 9) (step 11). 113).
If a negative result is obtained in step 112, the color + gloss prediction model generation device 50 determines whether or not the prediction with the component value set to “0” has been executed for all of the basic colors (that is, CMYK). (Step 114).
If an affirmative result is obtained in step 114, the color + gloss prediction model generation device 50 returns to step 111, and performs the above-described processing for the color component of the basic color that has not been predicted with the component value being “0”. repeat.

If a negative result is obtained in step 114, the color + gloss prediction model generation device 50 proceeds to step 115. The case where a negative result is obtained in step 114 means that the component value predicted for the remaining three colors and the component value of silver (Si) are set to “0” for one of the four basic colors, and the simulation printing device 20 (See FIG. 1).
In this case, the color + gloss prediction model generation device 50 gives the silver (Si) having the largest prediction value among the minimum values of the prediction times among the prediction values of the basic color (that is, CMYK) predicted in step 111. Is set as the maximum component value (step 115).

  For example, the minimum value of the prediction values of the remaining basic colors in the prediction cycle when the component value of cyan (C) is “0” is “20” of magenta (M), and the component value of magenta (M) is “0”. The minimum value of the prediction values of the remaining basic colors in the prediction cycle of “15” is “15” for yellow (Y), and the prediction value of the remaining basic colors in the prediction cycle of “0” for the component value of yellow (Y) Is “10” for cyan (C), and the minimum value for the prediction values of the remaining basic colors in the prediction cycle when the component value of black (K) is “0” is “15” for magenta (M). In this case, the maximum value is “20” which is the component value of magenta (M) in the prediction cycle when the component value of cyan (C) is “0”. Therefore, in this example, the component value of silver (Si) predicted in the prediction time when the component value of cyan (C) is “0” is set as the maximum component value.

Returning to the description of FIG.
When the maximum component value of silver (Si) is obtained in step 11, the color + gloss prediction model generation device 50 calculates the measured L ^* a ^* b ^* and gloss and the silver (Si) obtained in step 11. The component value of the basic color (CMYK) that satisfies the maximum component value is predicted by calculation (step 12).
Next, the color + gloss prediction model generation device 50 determines whether or not the predicted component value of the basic color (CMYK) is within the color gamut (step 13).
If a positive result is obtained in step 13, the color + gloss prediction model generation device 50 sets the maximum component value of silver (Si) acquired in step 11 to the maximum color gamut represented by the basic color (CMYK). It is determined as a component value of silver (Si) to be converted (step 14).
If a negative result is obtained in step 13, the color + gloss prediction model generation device 50 acquires the minimum component value of silver (Si) from the values of L ^* a ^* b ^* and gloss (step 15).

FIG. 11 is a flowchart illustrating an example of a processing procedure executed by the color + gloss prediction model generation device 50 to acquire the minimum component value of silver (Si).
In the present embodiment, the predicted value of the component value of silver (Si) is determined so that the color gamut is as wide as possible.
First, the color + gloss prediction model generation device 50 calculates the remaining three colors from L ^* a ^* b ^* and gloss corresponding to the patch whose arbitrary one component value of the basic colors (ie, CMYK) is “100”. And the silver (Si) component value are predicted (step 151). For example, the component value of cyan (C) is set to “100”, and the values of magenta (M), yellow (Y), black (K), and silver (Si) that reproduce the given L ^* a ^* b ^* and gloss are provided. Each component value is predicted by calculation.

Subsequently, the color + gloss prediction model generation device 50 determines whether the predicted three color component values and the silver (Si) component value are within the color gamut (step 152).
If a positive result is obtained in step 152, the color + gloss prediction model generation device 50 sets the component value of silver (Si) predicted in step 151 as the minimum component value of step 15 (see FIG. 9) (step 15). 153).
If a negative result is obtained in step 152, the color + gloss prediction model generation device 50 determines whether or not the prediction with the component value of “100” has been executed for all of the basic colors (that is, CMYK). (Step 154).
If an affirmative result is obtained in step 154, the color + gloss prediction model generation device 50 returns to step 151, and performs the above-described processing for the color component of the basic color that has not been predicted with the component value being “100”. repeat.

If a negative result is obtained in step 154, the color + gloss prediction model generation device 50 proceeds to step 155. The case where a negative result is obtained in step 154 means that the component value predicted for the remaining three colors and the component value of silver (Si) are set to “100” for one of the four basic colors, and the simulation printing device 20 (See FIG. 1).
In this case, the color + gloss prediction model generation device 50 gives the smallest predicted value among the maximum values of the prediction times among the predicted values of the basic color (that is, CMYK) predicted in step 151 (Si). ) Is set as the minimum component value (step 155).

  For example, the maximum value in the prediction values of the remaining basic colors in the prediction cycle when the component value of cyan (C) is “100” is “90” of magenta (M), and the component value of magenta (M) is “100”. The maximum value of the predicted values of the remaining basic colors in the prediction cycle set to is “95” of yellow (Y), and the predicted value of the remaining basic colors in the prediction cycle set to “100” for the component value of yellow (Y) Is "90" for cyan (C), and the maximum value for the prediction values of the remaining basic colors in the prediction cycle when the component value of black (K) is "100" is "80" for magenta (M). In this case, the minimum value is “80” which is the component value of magenta (M) in the prediction cycle when the component value of black (K) is “100”. Therefore, in this example, the component value of silver (Si) predicted at the prediction time when the component value of black (K) is “100” is set as the minimum component value.

Returning to the description of FIG.
When the minimum component value of silver (Si) is obtained in step 15, the color + gloss prediction model generation device 50 determines the measured L ^* a ^* b ^* and gloss and the silver (Si) obtained in step 15. The component value of the basic color (CMYK) that satisfies the minimum component value is predicted by calculation (step 16).

Next, the color + gloss prediction model generation device 50 determines whether or not the predicted basic color (CMYK) component value is within the color gamut (step 17).
If a negative result is obtained in step 17, the color + gloss prediction model generation device 50 determines that the simulation printing device 20 has no solution capable of reproducing the color and gloss of the target printing device 10 (that is, no solution). judge. The result of this determination is displayed on the operation screen (not shown), for example, by the color + gloss prediction model generation device 50 (step 18).

If an affirmative result is obtained in step 17, the color + gloss prediction model generation device 50 determines the basic color (CMYK) from L ^* a ^* b ^* and gloss and the minimum component value of silver (Si) obtained in step 15. ) Is calculated, and the maximum component value of silver (Si) in the color gamut is obtained by a binary search method (step 19).
The binary search here is executed in a range defined by the maximum component value obtained in step 11 and the minimum component value obtained in step 15. Specifically, the maximum component value of silver (Si) within the color gamut of the simulation printing apparatus 20 is obtained in a section between two circles shown in FIG.
The color + gloss prediction model generation device 50 determines the maximum component value obtained in step 19 as a silver (Si) component value that maximizes the color gamut (step 20).

<Specific example 2>
Here, the maximum value of the silver (Si) by the binary search is obtained by using the limit value of the sum of the coverage rates per unit area for each of the basic colors (C, M, Y, K) and the silver (Si). A processing method that does not require a component value search process will be described.
FIG. 12 is a diagram illustrating a change in color gamut due to the sum of coverage rates.
As shown in FIG. 12, the color gamut of only the basic color (CMYK) excluding silver (Si) is the widest, and the color gamut narrows toward gray as the coverage ratio of silver (Si) increases. That is, the reproducible color gamut is narrowed. In addition, as shown in FIG. 8, as the coverage rate of silver (Si) increases, the width of change in gloss gloss decreases.
Further, as described later, the change in the glossiness gloss is affected not only by the coverage ratio of silver (Si) but also by the total amount of the coverage ratio of each color component constituting the basic color.
Therefore, in this specific example, a method of more easily specifying the component value of silver (Si) using the limit information Limit of the total amount of the coverage rate will be described.
This relationship is expressed by the following equation.
Si = h ^-1 (L ^* , a ^* , b ^* , gloss, Limit) ... Equation 6

FIG. 13 is a diagram illustrating a specific example 2 of a procedure of generating an inverse model by the color + gloss prediction model generation device 50. In FIG. 13, the parts corresponding to those in FIG. 9 are denoted by the same reference numerals, and the symbol S in the figure indicates a step.
Also in this specific example, the color + gloss prediction model generation device 50 acquires the maximum component value of silver (Si) from L ^* a ^* b ^* and gloss in the procedure of step 11 shown in FIG. A detailed operation example is as shown in FIG.

Next, the color + gloss prediction model generation device 50 generates a gloss outline data group according to the total amount limit value (step 21).
FIG. 14 is a diagram illustrating an example of a glossy outline data group in which the upper limit of the total amount of the coverage rate is set to 150%. Note that the upper limit value (total amount limit value) of the total amount of the coverage rate is given in advance.
The first to sixth data groups from the top are obtained when the coverage ratio of cyan (C) is increased from 0% to 50% in increments of 10% when the component value of silver (Si) is 100%. Corresponding.
In the case of these six data groups, gloss is maximum when only silver (Si) is used.

The seventh to ninth data groups from the top correspond to the case where the coverage ratio of cyan (C) is further increased in increments of 10% while the coverage ratio of silver (Si) is decreased in increments of 10%. . The total coverage rate of these three data groups is constant. In the three data groups, when the coverage ratio of cyan (C) is 70% and the coverage ratio of silver (Si) is 80%, the gloss becomes maximum.
The 10th to 17th data groups from the top increase the coverage ratio of cyan (C) and magenta (M) in steps of 10% to 10%, respectively, while increasing the coverage ratio of silver (Si). This corresponds to a case where the change is made within a range that satisfies the total amount limit value. In these eight data groups, when the coverage rate of cyan (C) and magenta (M) is 10% and the coverage rate of silver (Si) is 100%, gloss becomes maximum.

Returning to the description of FIG.
When the generation of the gloss outline data group in step 21 is completed, the color + gloss prediction model generation device 50 sets the maximum silver (Si) component value acquired in step 11 and the silver (Si) satisfying the total amount limit value in step 21. The intersection with the minimum component value is determined, and the component value of silver (Si) satisfying the total amount limit value is determined (step 22).
FIG. 15 is a diagram illustrating an image of the process in step 22 of FIG. The horizontal axis in FIG. 15 is gloss, and the vertical axis is the component value of silver (Si).
As shown in FIG. 15, the maximum component value of silver (Si) satisfying the total amount limit value is specified as a circle in the figure. For this reason, the process of specifying the component value of silver (Si) is simplified as compared with the case of the specific example 1.
Incidentally, it is preferable that the coverage ratio of silver (Si) is limited to a range in which gloss increases monotonically in relation to the total amount of the coverage ratio for the basic colors (C, M, Y, K).

FIG. 16 is a diagram illustrating the relationship between the coverage ratio of silver (Si) and the glossiness (gloss) according to the total coverage ratio of the basic colors (C, M, Y, and K). (A) shows the case where the total amount of the coverage ratio for the basic colors (C, M, Y, K) is 5%, and (b) shows the case where the total amount of the coverage ratio for the basic colors (C, M, Y, K) is 20%. %, (C) is the coverage rate for the basic colors (C, M, Y, K) when the total amount of the coverage rates for the basic colors (C, M, Y, K) is 50%. Is 90%, (e) is the basic color (C, M, Y, K) when the total coverage of the basic colors (C, M, Y, K) is 150%, and (f) is the basic color (C, M, Y, K) Is the case where the total amount of the coverage rate is 200%.
The horizontal axis in FIG. 16 is the coverage ratio of silver (Si), and the vertical axis is the gloss (gloss).
When the total coverage of the basic colors (C, M, Y, K) is 5% and 20%, even if the coverage of silver (Si) is increased from 0% to 100%, the gloss is approximately equal. Increase monotonically.

However, when the total coverage of the basic colors (C, M, Y, K) is 50%, the gloss is maximized at a coverage of 90% for silver (Si), and the coverage of silver (Si) is maximized. At a rate of 100%, a decrease in gloss is observed as shown by a circle in the figure.
Similarly, when the total coverage rate of the basic colors (C, M, Y, K) is 90%, the gloss becomes maximum when the coverage rate of silver (Si) is 60%, and the gloss of silver (Si) becomes maximum. As the coverage ratio increases from more than 60%, a decrease in gloss is observed as indicated by a circle in the figure.
When the total coverage of the basic colors (C, M, Y, K) is 150% or 200%, no change is observed in the gloss even if the coverage of silver (Si) increases.
Since the metallic luster is reproduced by a change in the coverage ratio of silver (Si), the maximum component value of silver (Si) obtained in step 11 is preferably in a range where gloss changes monotonically due to the change.
The above-mentioned upper limit of 150% of the total amount of the coverage rate is a value obtained for the case shown in (d). In this case, the total amount of the coverage ratio for the basic colors (C, M, Y, K) is 90%, and the maximum component value (coverage ratio) of silver (Si) that satisfies the monotone increase is 60%, and the total amount is Becomes 150%.
In the case of (c), the total amount of the coverage rate for the basic colors (C, M, Y, K) is 50%, and the maximum component value (coverage rate) of silver (Si) that satisfies the monotone increase. Is 80%, the total amount may be set to 130%.

Returning to the description of FIG.
When the component value of silver (Si) that satisfies the total amount limit value Limit is determined in step 22, the color + gloss prediction model generation device 50 calculates L ^* a ^* b ^* and the silver (Si) determined in step 22. The component value of the basic color (CMYK) is specified from the component values of (Step 23).
This relationship is expressed by the following equation.
(C, Y, M, K) = g _h ⁻¹ (L ^* , a ^* , b ^* , Si) Equation 7
The function g _h ^-1 here is an inverse model of the simulation printing device 20 reflecting the total amount limit value.
The values specified in steps 22 and 23 are recorded as the print data Cout of the device-dependent profile shown in FIG.

<Example of texture information acquisition device>
Here, a specific example of the texture information acquisition device 40 (see FIG. 1) will be described.
<Specific example 1>
The texture information acquisition device 40 can be configured by a three-dimensional displacement measuring instrument that measures the intensity ratio between incident light and its reflected light in all directions of the hemisphere.
The function representing the intensity ratio here is the bidirectional reflectance distribution function (BRDF). As the three-dimensional displacement angle measuring device, a measuring device that can change an incident angle and a light receiving angle, such as a goniophotometer, is used.
FIG. 17 is a diagram showing the geometric conditions of BRDF. BRDF represents the ratio of the radiance dL ₀ of the reflected light to the small solid angle in the viewing direction V to the irradiance dE of light incident from the small solid angle in the light source direction L to a certain point x on the reflecting surface. Function. Here, θ _i is an angle with respect to the normal direction N of the incident light, and θ _o is an angle with respect to the normal direction N of the reflected light.
The value of BRDF is represented by the following equation.

<Specific example 2>
FIG. 18 is a diagram illustrating another specific example of the texture information acquisition device 40.
The texture information acquisition device 40 shown in FIG. 18 is expressed from a hardware viewpoint. The texture information acquisition device 40 illustrated in FIG. 18 optically reads the surface of the printed matter 30 and acquires two types of image data as a result of the reading.
One of the image data is an image of a light component diffusely reflected on the surface of the printed matter 30 (hereinafter, referred to as a “diffuse reflection image”), and the other is an image of a light component reflected specularly on the surface of the printed matter 30. An image (hereinafter, referred to as a “specular reflection image”).
Specular reflection is reflection in which the incident angle and the reflection angle of light are the same angle with respect to the reflection surface, and is also called specular reflection. Therefore, the specular reflection image is the same as the specular reflection image.

The texture information acquisition device 40 illustrated in FIG. 18 includes a platen glass 41 on which the printed matter 30 is arranged, a carriage 42 with a built-in optical system for reading, and a texture information extraction unit 43 for extracting texture information. .
The carriage 42 moves along the surface of the flat platen glass 41. The carriage 42 shown in FIG. 18 is provided with light sources 42A, 42B, 42C, an imaging optical system 42D, and an imaging sensor 42E. The carriage 42 is moved by a drive mechanism (not shown). Known technology is used for a drive mechanism (not shown) and a control system thereof.
Incidentally, the platen glass 41 is attached to a housing (not shown). In addition, a carriage 42 and a texture information extraction unit 43 are housed in a casing (not shown).

Each part of the carriage 42 shown in FIG. 18 has a predetermined length in a direction perpendicular to the paper surface. The direction perpendicular to the paper corresponds to the main scanning direction of the carriage 42. On the other hand, the direction indicated by the arrow corresponds to the sub-scanning direction of the carriage 42.
When reading information on the surface of the printed matter 30, the carriage 42 moves in the direction of the arrow. Hereinafter, the direction in which the carriage 42 moves is referred to as a front (front) side, and the opposite side is referred to as a rear (rear) side.
The platen glass 41 is a glass plate that supports a surface to be read among the surfaces of the printed material 30. The platen glass 41 may be a transparent member, for example, an acrylic plate.
When the surface of the printed matter 30 is optically read, the periphery of the printed matter 30 may be covered with a cover member having a light-shielding property, and the outside light may be blocked so that the outside light does not enter the inside of the carriage 42.
The carriage 42 is driven to move in the sub-scanning direction at a predetermined speed when reading the printed matter 30.

The light source 42 </ b> A is a light source disposed on the front side with respect to the reading position of the printed matter 30, and emits light at an angle of, for example, 45 degrees with respect to the normal direction of the printed matter 30. The light source 42A is used for reading a diffuse reflection image of the printed matter 30.
The light source 42 </ b> B is a light source disposed on the rear side with respect to the reading position of the printed matter 30, and emits light at an angle of, for example, 45 degrees with respect to the normal direction of the printed matter 30. The light source 42B is also used for reading a diffuse reflection image of the printed matter 30.
Since both the light source 42A and the light source 42B are for reading the diffuse reflection image, only one of them may be used.
The light source 42C is a light source disposed on the rear side with respect to the reading position of the printed matter 30, and emits light at an angle of, for example, 10 degrees with respect to the normal direction of the printed matter 30. The light source 42C is provided at a position that does not block the principal ray of the reflected light. However, the light source 42C may be disposed on the front side with respect to the reading position of the printed matter 30. The angle at which the light emitted from the light source 42C is incident on the printed matter 30 may be, for example, 5 degrees to 10 degrees. The light source 42C is used for reading a specular reflection image of the printed matter 30.

It is desirable that the width or angle of the light beam emitted from the light source 42C to the surface of the printed matter 30 be narrow. Therefore, when the width or angle of the light beam emitted from the light source 42C is wide, a means for limiting the width or angle of the light beam that irradiates the surface of the printed matter 30 is provided. As the means here, a cover or an aperture for reducing the width or angle of the light beam by blocking a part of the light beam, a lens for condensing the light beam, and the like are used.
It is desirable that the light emitted from the light source 42C has a uniform and continuous luminance value in the main scanning direction as compared with the light sources 42A and 42B. This is because the light emitted from the light source 42C is used for reading gloss information on the surface of the printed matter 30. Light sources that satisfy this requirement include, for example, fluorescent lamps and rare gas fluorescent lamps (such as xenon fluorescent lamps).
The light source 42C may be an aggregate of a plurality of light sources. For example, the light source 42C diffuses light emitted from the plurality of white LEDs (Light Emitting Diodes) arranged in the main scanning direction and the plurality of white LEDs so that the luminance distribution of the reading position in the main scanning direction becomes uniform. You may comprise a diffusion plate etc.
In the case of FIG. 18, three light sources 42A, 42B, and 42C are arranged on the carriage 42, but the light emitted from one light source is branched into a plurality of light, and the light path of the branched light is reflected. The light may be incident on the reading position at a different angle.

The carriage 42 has an imaging optical system 42D and an imaging sensor 42E.
The imaging optical system 42D is formed of, for example, a reflection mirror or an imaging lens, and forms the diffuse reflection component and the specular reflection component reflected in the normal direction from the surface of the printed matter 30 on the light receiving surface of the imaging sensor 42E. Image.
The imaging sensor 42E outputs the intensity of the diffuse reflection light and the specular reflection light imaged on the light receiving surface as an image signal. As the imaging sensor 42E, for example, a light receiving element such as a CCD (Charge Coupled Device) linear image sensor or a CMOS (Complementary MOS) image sensor is used.
A color filter (not shown) is disposed on the surface of the imaging sensor 42E. Therefore, the imaging sensor 42E outputs a color image signal representing the color of the reading position of the printed matter 30 for each of the diffuse reflection image and the specular reflection image. The color image signal is given by three color component values of red (R), green (G), and blue (B).

FIG. 19 is a diagram illustrating an example of a reflection image acquired by the texture information acquisition device 40 (see FIG. 18). (A) is an example of a diffuse reflection image, (b) is an example of a specular reflection image, and (c) is an example of a difference image between the diffuse reflection image and the specular reflection image.
The reflection image shown in FIG. 19 is obtained when a color chart in which 16 color patches having different combinations of colors and densities are arranged in a matrix is printed on the surface of the printed material 30.
The horizontal axis of the color chart corresponds to four colors having different combinations of hue and gloss, and the vertical axis corresponds to the coverage rate indicating the consumption of the color material used when printing the print 30.
The lower the coverage ratio, the smaller the amount of color material per unit area, and the lower the color density. On the other hand, the higher the coverage ratio, the greater the amount of color material per unit area, and the higher the color density.

In the case of FIG. 19, the four colors forming the color chart include a mixed color of cyan and silver (hereinafter also referred to as “cyan + silver color”), a mixed color of magenta and silver (hereinafter also referred to as “magenta + silver color”), magenta, It is cyan.
The color chart shown in FIG. 19 includes color patches in which the coverage is changed in four stages for each color. The color patches shown in FIG. 19 have the same shape. Specifically, the color patches are square.

As described above, the specular reflection image shown in (b) is an image in which the metallic gloss component on the surface of the printed matter 30 is mainly acquired, but also includes a diffuse component. Here, the metallic luster corresponds to the metallic color expressed by the silver toner.
(C) is a difference image expressing the metallic luster included in the specular reflection image. The difference image is generated by calculating a difference between the diffuse reflection image and the specular reflection image.
Since the metallic luster is basically generated by the silver toner, in the difference image, a gloss component appears in two rows of a color patch corresponding to the cyan + silver color and a color patch corresponding to the magenta + silver color including the silver toner. I have. Further, the difference in the coverage ratio also appears in the difference in the reflectance of the metallic luster. That is, the higher the coverage ratio, the brighter the color patch looks.

Returning to the description of FIG.
In the case of the carriage 42 used in the present embodiment, the relationship between the incident angle of irradiation light and the light receiving angle received by the imaging sensor 42E is kept constant over the entire surface of the printed material 30. This relationship cannot be realized by imaging with an imaging camera or the like.
Therefore, by calculating the difference between the diffuse reflection image acquired under the diffuse reflection condition (incident angle is 45 degrees) and the specular reflection image acquired under the specular reflection condition (incident angle is 10 degrees), It is possible to accurately extract information on metallic luster. This means that the region of metallic luster and the reflectance (mirror reflectance of a two-dimensional plane) can be obtained at once by a simple difference calculation.
The diffuse reflection condition (incident angle of 45 degrees) and the specular reflection condition (incident angle of 10 degrees) are calibrated by the same white calibration plate.
The texture information extraction unit 43 included in the texture information acquisition device 40 generates the above-described difference image from the diffuse reflection image and the specular reflection image.

FIG. 20 is a diagram illustrating an example of a functional configuration of the texture information acquisition device 40.
20, the parts corresponding to those in FIG. 18 are denoted by the same reference numerals.
The carriage 42 in the present embodiment functions as a diffuse reflection image acquisition unit 42F and a specular reflection image acquisition unit 42G.
The diffuse reflection image acquisition unit 42F receives the reflection light of the light emitted from the light sources 42A and 42B (see FIG. 18) by the imaging sensor 42E (see FIG. 18) and outputs the light as a diffuse reflection image. The diffuse reflection image corresponds to a color component.
The specular reflection image acquisition unit 42G receives the reflection light of the light emitted from the light source 42C (see FIG. 18) by the imaging sensor 42E, and outputs it as a specular reflection image. The specular reflection image corresponds to the gloss component.
Switching between the period in which the light sources 42A and 42B emit light and the period in which the light source 42C emits light is controlled. That is, the diffuse reflection image and the specular reflection image are separately acquired.

The texture information extraction unit 43 has functions as a difference image acquisition unit 43A and average value calculation units 43B and 43C.
The difference image obtaining unit 43A calculates a difference between the diffuse reflection image and the specular reflection image for each position on the printing surface to obtain a difference image. There are two methods of obtaining the difference image, a method of obtaining the difference image by subtracting the diffuse reflection image from the specular reflection image, and a method of obtaining the difference image by subtracting the specular reflection image from the diffuse reflection image. The difference image acquisition unit 43A calculates the difference image by one or both methods.

The average value calculation unit 43B calculates an average RGB value of the diffuse reflection image. When the color chart is printed on the printed material 30 (see FIG. 18), the average value calculation unit 43B calculates the average RGB value for each color patch. In the case of FIG. 20, the average RGB value is (R, G, B) = (185, 151, 95). The average RGB value is an example of an average pixel value.
The average value calculation unit 43C calculates an average RGB value of the difference image corresponding to the color patch. Of course, when the color chart is printed on the printed material 30, the average value calculation unit 43C calculates the average RGB value for each color patch. In the present embodiment, the average RGB value of the difference image is represented by an average ΔRΔGΔB value for distinction. Δ represents a difference. The average ΔRΔGΔB value is an example of an average pixel value.

In the case of FIG. 20, the average ΔRΔGΔB value is represented by (ΔR, ΔG, ΔB) = (69, 88, 88). The average ΔRΔGΔB value here corresponds to the gloss component value.
When a color chart is printed on the printed matter 30, texture information on a plurality of color patches is acquired by two scans.
This means that the time required to acquire the texture information can be reduced as compared to the BRDF measurement method. Further, the acquisition of the texture information may be a difference operation, and the load on the calculation resources can be reduced.

<Embodiment 2>
In the above-described embodiment, the mechanism for matching the gloss reproduction characteristic of the simulation printing apparatus 20 with the gloss reproduction characteristic of the target printing apparatus 10 has been described. However, in the present embodiment, a printed matter printed in the past (hereinafter, “ A description will be given of a mechanism for reproducing the gloss of the target printed matter on the copied printed matter when the target printed matter is duplicated (copy printing).

<System configuration>
FIG. 21 is a diagram illustrating an example of the overall configuration of a gloss adjustment system 1A used in the second embodiment. In FIG. 21, the parts corresponding to those in FIG.
The gloss adjustment system 1A includes a printing apparatus 100, a texture information acquisition apparatus 40 that acquires texture information of the printed matter 30 printed by the printing apparatus 100, and the acquired texture information and print data Cin used for printing the printed matter 30. And a glossy texture reproduction profile generation device 110 that generates a correspondence relationship with
The glossy texture reproduction profile generation device 110 here is an example of a generation unit and also an example of an information processing device.
In the printing apparatus 100 according to the present embodiment, the reproduction characteristic of the gloss component has been adjusted between the printing apparatus 100 and the target printing apparatus 10 by the mechanism described in the first embodiment.

When adjusting the texture of the printed matter output in the past so that the texture can be reproduced in the copy printed matter, as shown in FIG. 1, print data Cin corresponding to the gloss correction color chart is input to the printing apparatus 100.
The print data Cin here also specifies the arrangement of a plurality of color patches having different combinations of colors and densities, as in the first embodiment.
Also in the case of the present embodiment, it is assumed that the print data Cin is composed of cyan (C), magenta (M), yellow (Y), black (K), and spot color (S). The special color (S) is a color component that gives metallic luster, and includes, for example, silver (Si) and gold (Go). In the following description, silver (Si) is used as a special color.

Since the configuration of the texture information acquisition device 40 is the same as that of the first embodiment, the details are omitted, but the texture information of the printed matter 30 is optically read. The texture information here includes a color component value and a gloss component value.
In the case of the present embodiment, the color component values are represented by, for example, L ^* , a ^* , and b ^* systems. The gloss component value is represented by, for example, gloss (gloss).
The glossy texture reproduction profile generation device 110 outputs, as a glossy texture reproduction profile, the correspondence between the print data Cin corresponding to the printed matter 30 from which the texture information has been read and the read texture information. In the case of the present embodiment, the correspondence is expressed by the following equation.
(L ^* , a ^* , b ^* , gloss) = f (C, M, Y, K, Si) Equation 8
The generated glossy texture reproduction profile is fed back to the printing apparatus 100.

FIG. 22 is a diagram illustrating a configuration example of a glossy texture reproduction profile. In FIG. 22, for convenience of explanation, the data is represented in the form of a lookup table, but the data format is not limited to the lookup table.
Note that the left column of the lookup table is the print data Cin corresponding to each patch constituting the color chart, and the right column is the color component value and the gloss component value acquired from each patch.
In the case of FIG. 22, in the printing apparatus 100 used for printing the printed matter 30, when (C, M, Y, K, Si) = (0, 20, 0, 0, 80) is given as the print data Cin, the printed matter is printed. It can be seen that the color component value of 30 is (60, -5, -5) and the gloss component value is (100).
Further, by using this lookup table, it is possible to obtain print data for reproducing the given color component value and gloss component value by the printing apparatus 100.

FIG. 23 is a diagram illustrating processing functions of the printing apparatus 100 in which the glossy texture reproduction profile 101A is set. 23, the parts corresponding to those in FIG. 21 are denoted by the same reference numerals.
A printing apparatus 100 shown in FIG. 23 includes a data conversion unit 101 that converts a color component value and a gloss component value of a target print product input from the texture information acquisition device 40 into print data defined by a basic color and a special color, and print data. Has a function as an image forming unit 102 that prints an image corresponding to the image on a recording medium.
Here, a glossy texture reproduction profile 101A is set in the data conversion unit 101. The data conversion unit 101 converts the color component value and the gloss component value of each pixel into the corresponding basic color and special color component values using the glossy texture reproduction profile 101A. The data conversion unit 101 is an example of a conversion unit.

If there is no data that matches the input color component value and the gloss component value in the glossy texture reproduction profile 101A, the data conversion unit 101, for example, selects one of the registered data whose distance in the color space is short. The color component value and the gloss component value may be obtained and output as an approximate value.
If there is no data that matches the input color component value and the gloss component value in the glossy texture reproduction profile 101A, the data conversion unit 101, for example, selects a plurality of registered data whose distance in the color space is short. A set of a color component value and a gloss component value may be acquired, and an approximate value may be calculated and output based on those values.
If there is no data that matches the input color component value and the gloss component value in the gloss texture reproduction profile 101A, the data conversion unit 101 extracts registered data having the same gloss component value, for example. The output value may be determined by specifying data in which the color component value is close to the input value.

<Other embodiments>
Although the embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is apparent from the description of the appended claims that various modifications or improvements to the above-described embodiment are included in the technical scope of the present invention.

(1) In the first embodiment, the simulation printing device 20, the texture information acquisition device 40, and the like are described as being separate devices. In the second embodiment, the printing device 100 and the texture information acquisition device 40, and the like are separate devices. Although described as being devices, they may be configured as one device.
FIG. 24 is a diagram illustrating an example of the appearance of an image forming apparatus 200 in which a function of acquiring texture information and a printing function are integrated.
The image forming apparatus 200 includes an image reading device 210 that reads an image of a document, and an image recording device 220 that records an image on a sheet, which is an example of a recording medium.

The image reading device 210 is mounted on an image recording device 220 constituting a main body of the device. The image reading device 210 includes an image reading unit that optically reads an image formed on a document, and a document conveying unit that conveys the document to the image reading unit.
Here, the image reading device 210 corresponds to the texture information acquiring device 40 described above.
The image recording device 220 includes a mechanism for forming an image on the surface of a sheet and a mechanism for conveying the sheet.
The image recording device 220 forms an image on the sheet P pulled out from the sheet tray 230.
The image recording device 220 here corresponds to the simulation printing device 20 and the printing device 100 described above.

FIG. 25 is a diagram illustrating a connection example between modules constituting the image forming apparatus 200.
The image recording device 220 is provided with a control device 221 for controlling the operation of the entire device. The control device 221 includes a CPU (Central Processing Unit) 221A, a ROM (Read Only Memory) 221B in which firmware and a BIOS (Basic Input Output System) are stored, and a RAM (Random Access Memory) used as a work area of the CPU 221A. 221C. The control device 221 constitutes a so-called computer.

The image recording device 220 has a hard disk device (HDD) 222 which is an example of a nonvolatile storage device. The hard disk device 222 stores image data of an image read by the image reading device 210, print data Cin provided from the outside, image data of an image transmitted and received through FAX communication, and the like.
The hard disk device 222 also stores the device-dependent profile 21A (see FIG. 4) and the glossy texture reproduction profile 101A (see FIG. 23) described in the first embodiment.
Further, the image recording device 220 is provided with a display unit 223 and an operation receiving unit 224 as a user interface. As the display unit 223, for example, a liquid crystal display or an organic EL (Electro Luminescence) display is used. As the operation receiving unit 224, for example, a touch sensor is used.

In addition, the image recording device 220 is provided with an image processing unit 225 and an image forming unit 226 that execute various processes (for example, color correction and gradation correction) required for forming an image.
The image processing unit 225 includes the above-described color + gloss prediction model generation device 50 (see FIG. 1), the device-dependent profile generation device 60 (see FIG. 1), the glossy texture reproduction profile generation device 110 (see FIG. 21), and the data conversion unit. 101 (see FIG. 23).
The image forming unit 226 forms a printed matter based on the print data processed by the image processing unit 225. The image forming unit 226 here corresponds to the image forming unit 22 (see FIG. 4) and the image forming unit 102 (see FIG. 23).
Further, the image recording device 220 is provided with a communication unit 227 for executing communication with an external device through connection with a LAN.
Incidentally, the control device 221 and each unit are connected through a bus 228 and a signal line (not shown).

(2) The target printing device 10 (see FIG. 1) and the simulation printing device 20 (see FIG. 1) in the first embodiment may be connected via a network.
FIG. 26 is a diagram illustrating a configuration example of the gloss adjustment system 1. 26, parts corresponding to those in FIG. 1 are denoted by the same reference numerals.
In the case of the gloss adjustment system 1 shown in FIG. 26, the target printing device 10, the simulation printing device 20, and the server 310 are connected to the network 300.
The network 300 here may be a LAN (Local Area Network), the Internet, or a cloud network.

The server 310 here has all of the functions corresponding to the texture information acquisition device 40 (see FIG. 1), the color + gloss prediction model generation device 50 (see FIG. 1), and the device-dependent profile generation device 60 (see FIG. 1). Run.
Note that a plurality of servers 310 may be provided. In such a case, the functions of the texture information acquisition device 40, the color + gloss prediction model generation device 50, and the device-dependent profile generation device 60 are realized by cooperation of the plurality of servers.
The server 310 stores the generated device-dependent profile in the corresponding simulation printing device 20 via the network 300.

(3) In the second embodiment, a description will be given of a glossy texture reproduction profile in a case where color component values are expressed in L ^* , a ^* , and b ^* systems, and gloss component values are expressed in gloss (gloss). However, when the texture information acquisition device 40 having the configuration shown in FIG. 20 is used, the color component value is represented by the average RGB value, and the gloss component value is represented by the average RGB value of the difference image (that is, the average ΔRΔGΔB value). You.
FIG. 27 is a diagram illustrating another example of the glossy texture reproduction profile.
In the case of FIG. 27 as well, for convenience of explanation, it is expressed in the form of a lookup table, but the data format is not limited to the lookup table.
The left column of the lookup table is the print data Cin corresponding to each patch constituting the color chart, and the right column is the average RGB value and average RGB value corresponding to the color component value and the gloss component value obtained from each patch. ΔRΔGΔB value.
In the case of FIG. 27, in the printing apparatus 100 (see FIG. 21) used for printing the printed matter 30 (see FIG. 21), (C, M, Y, K, Si) = (0, 20, 0) as the print data Cin. , 0,80), the color component value of the printed matter 30 is (185,151,95) and the gloss component value is (69,88,88).

(4) In the above-described first and second embodiments, the case where the print data Cin is given in the form of CMYK special colors has been described, but it may be given in the form of RGB special colors.
In this case, the component values of the print data in the device-dependent profile and the glossy texture reproduction profile may be replaced with red (R), green (G), blue (B), and a special color.
Alternatively, in the target printing device 10 (see FIG. 1), the simulation printing device 20 (see FIG. 1), and the printing device 100 (see FIG. 21), the input print data is converted from the RGB spot color format to the CMYK spot color format. A data conversion unit may be prepared.

1, 1A: Gloss adjustment system, 10: Target printing device, 20: Simulation printing device, 21: Data correction unit, 22, 102: Image forming unit, 30: Printed matter, 31: Patch, 40: Texture information acquisition device, 42F ... Diffuse reflection image acquisition section, 42G ... Specular reflection image acquisition section, 43 ... Texture information extraction section, 43A ... Difference image acquisition section, 43B, 43C ... Average value calculation section, 50 ... Color + gloss prediction model generation apparatus, 60 ... Device-dependent profile generation device, 100: printing device, 101: data conversion unit, 110: glossy texture reproduction profile generation device, 200: image forming device, 210: image reading device, 220: image recording device, 300: network, 310 ... server

Claims (13)

An acquisition unit that acquires the value of the color component and the value of the gloss component measured from the printed matter,
An information processing apparatus comprising: a generation unit that reproduces the acquired color component value and the gloss component value and that generates a model that defines a relationship between component values of a basic color and a special color on the printing apparatus side. The generation unit specifies the component value of the special color based on the measured value of the color component and the value of the gloss component, and then specifies the component value of the basic color using the component value of the special color. Item 2. The information processing device according to item 1.   The said generation part specifies the component value range of the said special color which gives the value of the said gloss component in the state which set one component value of the said basic colors to the maximum value or the minimum value. Information processing device.   The generation unit, when the color reproduced in the basic color and the special color other than the basic color set to the maximum value or the minimum value is included in the color gamut, the component value of the special color, The information processing apparatus according to claim 3, wherein the value is set as a maximum value or a minimum value of the component value of the spot color that satisfies the value of the gloss component.   The generation unit may include a color that is reproduced by the component value of the basic color and the component value of the special color, fills a color gamut, and satisfies a range of component values of the special color that gives a value of the gloss component. The information processing apparatus according to claim 2, wherein the maximum value of the component values is specified.   The information processing apparatus according to claim 5, wherein the generation unit uses, as the color gamut, a color gamut whose outer edge is defined by a limit value regarding a sum of respective component values of the basic color and the special color.   The first printing device based on the first model generated for the first printing device to be simulated and the second model generated for the second printing device used for the target; The information according to claim 1, further comprising a correction value generation unit configured to generate a correction value for correcting input data of the first printing device such that a gloss component of the printed material approaches a gloss component of the printed material by the second printing device. Processing equipment.   The information processing apparatus according to claim 7, wherein the input data is provided by a combination of both or one of silver and gold and cyan, magenta, yellow, and black.   The information processing apparatus according to claim 7, wherein the input data is provided by a combination of both or one of silver and gold, and red, green, and blue.   When printing a copy of the target print on the printing device, using the model generated in advance for the print device, the color component value and the gloss component of each pixel obtained by optically reading the target print are used. The information processing apparatus according to claim 1, further comprising: a conversion unit configured to convert the value of (i) into each component value of the basic color and the special color of the printing apparatus.   The said color component is provided based on the diffuse reflection image of the target printed matter, and the gloss component is provided based on the difference image between the diffuse reflection image and the specular reflection image of the target printed matter, Claims. The information processing apparatus according to claim 10.   The information according to claim 1, wherein the color component is provided based on a diffuse reflection image of the printed matter, and the gloss component is provided based on a difference image between a specular reflection image of the printed matter and the diffuse reflection image. Processing equipment. On the computer,
A function to obtain the value of the color component and the value of the gloss component measured from the printed matter,
And a function for generating a model for reproducing the acquired color component values and the gloss component values, the model defining the relationship between the component values of the basic color and the special color on the printing apparatus side.