JP2011259411A - Color prediction device, color prediction method, program, table creation device, and printing system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable the accuracy of color prediction to be higher and enable the processing load to a device in the color prediction to be reduced.SOLUTION: A color prediction device for calculating a prediction reflection ratio indicating a reproduced color printed by a printer in color matching, comprises: an optical density calculation unit for calculating a print optical density based on a print reflection ratio measured from a printed portion to which the printer performed printing on a print medium; and an optical density prediction unit for calculating a print optical density of the reproduced color based on the print optical density, and calculating a first prediction reflection ratio indicating the reproduced color based on the print optical density of the reproduced color.

Description

  The present invention relates to a color prediction device, a color prediction method, a program, a table creation device, and a printing system that predict a reproduction color in color matching of a reproduction color printed by a printing press.

For example, when the reproduction color is adjusted by area modulation in color matching, as a method of predicting the color of a portion where each color is printed with a given halftone dot area ratio, for example, the Yule-Nielsen spectroscopic Neugebauer model (Yule- There are known techniques such as a method using Nielsen Spectral Neugebauer Model (see, for example, Patent Document 1) and a method using a spectral extended Neugebauer model (see, for example, Non-Patent Document 1).
Here, the “spectral extended Neugebauer model” is a model in which the Neugebauer primary color and the halftone dot area ratio are extended to the spectrum so as to be a function of wavelength, and is hereinafter referred to as a reflectance-based color prediction model. In order to explain this model, an equation for predicting the spectral reflectance when a halftone dot is multiplied with the primary colors of three colors of cyan (C), magenta (M), and yellow (Y) is used. Will be described.

  First, the effective halftone dot area ratio with respect to the halftone dot area ratio of the primary color is calculated according to the equation (1) obtained by reversing the Murray-Davies equation.

Note that the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s (λ) of the paper are measured values.
According to the above equation (1), the spectral effective halftone dot area ratio of each primary color is obtained for the given setting of the halftone dot area ratio of the primary color. These effective halftone dot area ratios include optical dot gain and mechanical dot gain, and correspond to obtaining an optimal n value in the Yule-Nielsen spectroscopic Neugebauer model.
The "mechanical dot gain" here is an error in the effective halftone dot area ratio that occurs when the halftone dots are crushed and thickened by printing with a printing plate or a printing press. It is different for each machine. In addition, the “optical dot gain” here is an error in the effective halftone dot area ratio caused by irregular reflection of a color material (for example, ink or toner) penetrating the surface of the print medium. It differs depending on the characteristics.

  Next, the spectral halftone dot area ratio of the Neugebauer primary color is calculated from the following equation (2).

  Using the spectral halftone dot area ratio of the Neugebauer primary color calculated by the above formula (2), the predicted spectral reflectance is obtained from the following formula (3).

By determining the observation light source from the predicted spectral reflectance and calculating the tristimulus value XYZ, the CIELab value, etc., the reproduction color can be predicted.
In this way, in the above-mentioned spectral extended Neugebauer model, by predicting the spectral reflectance using the effective dot area ratio including the optical dot gain and the mechanical dot gain, in the gradation of each color material single color, In addition, it is possible to perform correction in consideration of the behavior of light such as internal scattering (light scattering inside the printing medium).

JP-T-2007-516663 Special Table 2007-96793 JP-T-2006-295709

Journal of the Japan Society for Printing Science, Vol. 42, No. 5 (2005)

However, in a portion where a plurality of color materials are overlapped, the color materials influence each other, thereby causing a phenomenon different from the internal scattering in the single layer portion of each color material.
For this reason, in the case of a print medium with no internal scattering or little internal scattering, the accuracy of color prediction can be ensured to some extent even by using the above method, but the internal scattering may increase depending on the printing performance of the printing press and the paper. There is also. In this case, there is a problem that the accuracy of color prediction is reduced.
In addition, for each portion where a plurality of color materials overlap, predicting the spectral reflectance taking into account internal scattering in which the color materials influence each other complicates the calculation formula. There is also a problem that the processing speed increases and the processing speed decreases.

  The present invention has been made in consideration of such circumstances, and has been made to solve the above-described problems. The object of the present invention is to increase the accuracy of color prediction and reduce the processing load on the device during color prediction. An object of the present invention is to provide a color prediction device, a color prediction method, a program, a table creation device, and a printing system.

  In order to solve the above problem, a color predicting apparatus according to the present invention is a color predicting apparatus that calculates a predictive reflectance representing a reproduced color printed by a printing machine in color matching, and the printing machine prints on a printing medium. An optical density calculation unit that calculates a printing optical density based on the printed reflectance measured from the printed portion, and calculates a printing optical density of the reproduction color based on the printing optical density, thereby obtaining the printing optical density of the reproduction color. And an optical density prediction unit that calculates a first predicted reflectance representing the reproduced color.

  In order to solve the above problem, a color predicting apparatus according to the present invention is a color predicting apparatus that calculates a predictive reflectance that represents a reproduced color that is printed by a printing machine in color matching, and the printing machine includes a printing medium. The print reflectance of the printed portion printed on the optical fiber is calculated by the Kubelka-Munk equation, and an optical density calculation unit that calculates the print optical density based on the print reflectance, and the print optical density of the reproduced color based on the print optical density is calculated. An optical density prediction unit that calculates and calculates a first predicted reflectance that represents the reproduced color based on the printing optical density of the reproduced color.

  According to the present invention, in the above-described color prediction apparatus, a reflectance prediction unit that calculates a second predicted reflectance that represents the reproduced color based on the print reflectance, the first predicted reflectance, and the second predicted reflection. A mixed prediction unit that mixes rates to obtain a third predicted reflectance representing the reproduced color.

  According to the present invention, in the above-described color prediction apparatus, the optical density prediction unit calculates a multi-order color reflectance measured from a mixed color obtained by printing the plurality of colors on the print medium by the printing press. The third predicted reflectance calculated by mixing the first predicted reflectance representing the mixed color and the second predicted reflectance representing the mixed color calculated by the reflectance prediction unit is approximated A weighting factor calculating unit that calculates a weighting factor, wherein the mixed prediction unit is configured to mix the first predicted reflectance and the second prediction with a mixing ratio according to the weighting factor calculated by the weighting factor calculating unit; It is characterized by mixing reflectance.

  Further, the present invention is characterized in that, in the above-described color prediction apparatus, the reflectance and the optical density are extended to spectroscopy.

  In addition, the present invention is characterized in that the above-described color prediction apparatus is connected to the printing press and inputs the print reflectance measured by an optical element included in the printing press.

  Furthermore, in order to solve the above problem, a color prediction method according to the present invention is a color prediction method for calculating a predicted reflectance that represents a reproduced color printed by a printing machine in color matching, in which the printing machine prints on a print medium. Calculating a print optical density based on the print reflectance measured from the printed portion, calculating a print optical density of the reproduced color based on the print optical density, and reproducing the print optical density based on the print optical density of the reproduced color. Calculating a first predicted reflectance representing a color.

  In order to solve the above problem, the program according to the present invention includes a computer that calculates a predicted reflectance that represents a reproduced color that is printed by a printing machine in color matching, from a printed portion that is printed on a printing medium by the printing machine. An optical density calculating means for calculating a printing optical density based on the measured printing reflectance; calculating a printing optical density of the reproduction color based on the printing optical density; and calculating the reproduction color based on the printing optical density of the reproduction color. It is a program for functioning as an optical density predicting means for calculating the first predicted reflectance to be expressed.

  In order to solve the above problem, a table creation device according to the present invention includes any one of the color prediction devices described above and an estimated reflectance estimated as the reflectance of the subject in real space. Based on the image, a reflectance estimation unit that calculates for each predetermined gradation value, and a convergence in which a difference between the third predicted reflectance calculated by the color prediction device and the estimated reflectance is determined in advance. An optimization calculation unit that determines whether or not a condition is satisfied, and outputs the estimated reflectance and the third predicted reflectance that satisfy the convergence condition as optimum values; The gradation value of the estimated reflectance to be input and the halftone dot area ratio of the third predicted reflectance input as the optimum value from the optimization calculation unit are associated with each other and written to the gradation value halftone dot area ratio table. Correspondence part And further comprising a.

  Further, in the above table creation device, the present invention provides the reproduction color that is set when calculating the third predicted reflectance when the optimization calculation unit determines that the convergence condition is not satisfied. The halftone dot area ratio shown is corrected.

  In order to solve the above problem, a printing system according to the present invention includes any one of the above-described table creation devices, an image input device that inputs image data including the image of the subject, and the gradation value network. Referring to the dot area ratio table, a conversion device that outputs a halftone dot area ratio corresponding to the gradation value of each pixel of the image data, and an image of the subject based on the halftone dot area ratio input from the conversion device A reproduction color output device for printing.

  According to the present invention, it is possible to improve the accuracy of color prediction and reduce the processing load on the device during color prediction.

It is a block diagram which shows an example of the color prediction apparatus which concerns on 1st Embodiment. It is a figure explaining the spectral optical density calculation part in the color prediction apparatus which concerns on 1st Embodiment. It is a flowchart which shows an example of the color prediction method in the color prediction apparatus which concerns on 1st Embodiment. It is a figure which shows an example of the experimental data for demonstrating the effect by the color prediction which concerns on this invention. It is a block diagram which shows an example of the color prediction apparatus which concerns on 2nd Embodiment. It is a flowchart which shows an example of the weighting coefficient determination method in the color prediction apparatus which concerns on 2nd Embodiment. It is a flowchart which shows an example of the color prediction method in the color prediction apparatus which concerns on 2nd Embodiment. It is the schematic which shows an example of a structure of the printing system which concerns on 3rd Embodiment. It is a figure explaining the spectral optical density calculation part in the printed matter spectral reflectance prediction apparatus which concerns on 3rd Embodiment. It is a figure explaining the spectral optical density calculation part in the printed matter spectral reflectance prediction apparatus which concerns on 3rd Embodiment. 10 is a flowchart illustrating an example of a method of creating a gradation value / halftone dot area ratio conversion table in a printing system according to a third embodiment. 10 is a flowchart illustrating an example of a printing method using a gradation value / halftone area ratio conversion table in a printing system according to a third embodiment. It is a figure which shows the other example of the experimental data for demonstrating the effect by the color prediction which concerns on this invention. It is a figure which shows an example of the gradation value / halftone dot area rate conversion table created with the table creation apparatus concerning this embodiment.

Hereinafter, a color prediction apparatus according to an embodiment of the present invention will be described with reference to the drawings.
This color prediction apparatus is a color prediction table (for example, ICC) that represents a reproduction color that reproduces a color expressed in input image data to be printed on a print medium (for example, paper) in color matching for printing. Profile). The color prediction table created by this color prediction device is used for color matching for printing in, for example, a printing machine, a color digital proofing machine, a color inkjet printer, a color laser printer, etc. This is suitable for printing used for color matching to adjust the reproduction color.
In the present embodiment, the color prediction apparatus will be described below using a configuration including a printing unit that realizes a printing function as an example. However, the present invention is not limited to this, and the configuration connected to a printing press or printing The machine may include a color prediction device.
In this case, the printing press prints a predetermined color patch when executing the color prediction process by the color prediction device, measures the spectral reflectance of each color from the color patch with an optical element, and sends it to the color prediction device. Output. The color prediction apparatus performs a color prediction process described below based on the measured spectral reflectance, and creates a color prediction table.

[First Embodiment]
FIG. 1 is a schematic diagram illustrating an example of the configuration of the color prediction apparatus 100 according to the present embodiment.
As shown in FIG. 1, the color prediction apparatus 100 includes an input unit 11, a spectral reflectance database 12, an optical density calculation unit 13, a spectral optical density database 14, a spectral optical density prediction unit 15, and a color prediction table creation. Unit 16, output unit 17, printing unit 18, and measurement unit 19.

The input unit 11 is connected to, for example, an external computer or the like, and inputs an arbitrary halftone dot area ratio setting value for each primary color. More specifically, the input unit 11 includes, for example, a halftone dot setting value (for example, information indicating a plurality of halftone dot area ratios Y _i %) set for creating a color prediction table and the like, and a color patch. A halftone dot setting value set for printing (for example, information indicating a halftone dot area ratio X _i %) is input.

The spectral reflectance database 12 includes a primary color spectral reflectance region 121 and a Neugebauer primary color spectral reflectance region 122.
The spectral reflectance database 12 stores the spectral reflectance of each primary color of the measured color patch. Here, an example in which three colors of yellow (Y), magenta (M), and cyan (C) are used as the primary color patches will be described below. The color patch includes at least a patch portion for printing each primary color (C, M, Y) at a plurality of halftone dot area ratios X _i % (i = C, M, Y), and a dot area ratio of 100 % Of Neugebauer primary color l.
The patch portion printed at the halftone dot area ratio X _i % is a primary color of each primary color, and is a halftone dot setting value set for printing a color patch, for example, at an interval of 5%. Are printed at the halftone dot area ratio X _i % set in step (1). Further, the Neugebauer primary color l includes eight primary colors (l = C, M, Y), secondary colors (l = CM, CY, MY), and tertiary colors (l = CMY), and a background color ( l = W).
The color patch is printed by the printing unit 18 when a halftone setting value set for printing the color patch input from the input unit 11 is input to the printing unit 18.
For example, the printing unit 18 performs plate printing such as offset printing, and performs a series of printing processes including RIP (Raster Image Processor) processing and CTP (Computer To Plate) processing.

The primary color spectral reflectance region 121 includes, for example, a spectral reflectance R _{measure, i} (λ) of the primary color i (i = C, M, Y), and a spectral reflectance R of the primary color i (i = C, M, Y) solid. _{t, i} (λ) and the spectral reflectance R _s (λ) of the paper are stored. Note that the spectral reflectance R _{measure, i} (λ) of the primary color i (i = C, M, Y) is a halftone dot area ratio based on a halftone dot setting value set for printing a color patch. This is an actual measurement value obtained by measuring the spectral reflectance of the color patch in which the printing unit 18 printed the primary colors (C, M, Y) of the primary colors CMY at X _i %. The primary color i (i = C, M, Y) solid spectral reflectance R _{t, i} (λ) is a dot area ratio of 100% and the primary color i primary color (C, M, Y) is printed in the printing unit 18. Is an actual measurement value obtained by measuring the spectral reflectance of the printed color patch. The spectral reflectance R _s (λ) of the paper is an actual measurement value obtained by measuring the spectral reflectance of the paper portion on which nothing is printed. A spectral patch R _{measure, i} (λ) of the primary color i, a spectral reflectance R _{t , i} (λ) of the primary color i, and a spectral reflectance R _s (λ) of the paper are printed by the printing unit 18. For example, the spectral reflectance measured by the measurement unit 19 from the printed paper.
The measurement unit 19 includes, for example, an optical element, measures the spectral reflectance from a sheet on which a color patch is printed, and stores the measured spectral reflectance in the spectral reflectance database 12.

The Neugebauer primary color spectral reflectance region 122 stores the solid spectral reflectance R _{t, l} (λ) of the solid of the Neugebauer primary color l. The spectral reflectance R _{t, l} (λ) is a color obtained by printing the Neugebauer primary color l (l = C, M, Y, CM, MY, YC, CMY, W) by the printing unit 18 with a dot area ratio of 100%. It is the actual value which measured the spectral reflectance of the patch. The spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l is a spectral reflectance measured by, for example, the measuring unit 19 from a sheet on which a color patch is printed by the printing unit 18.

As shown in FIG. 2A, the optical density calculation unit 13 reads the spectral reflectance from the primary color spectral reflectance region 121 of the spectral reflectance database 12, and performs spectral spectroscopy based on this spectral reflectance according to the following equation (4). The optical density is calculated and stored in the primary color spectral optical density area 141 of the spectral optical density database 14. That is, the optical density calculating unit 13 calculates the spectral reflectance _{R its measure} primary _i, the optical density of the primary colors i based on the _i (λ) _{D measure, i} a (lambda). The optical density calculator 13 calculates the optical density D _{t, i} (λ) of the primary color i solid based on the spectral reflectance R _{t, i} (λ) of the primary color i solid. Further, the optical density calculator 13 calculates the optical density D _s (λ) of the paper based on the spectral reflectance R _s (λ) of the paper.
Note that, as described above, the spectral reflectance R _{measure, i} (λ) of the primary color i is a plurality of halftone dots according to the halftone dot area ratio X _i % when the color patch is printed on the paper. The spectral reflectance corresponding to the area ratio X _i % is included. For this reason, the optical density D _{measure, i} (λ) of the primary color i also includes spectral optical densities corresponding to the halftone dot area ratio X _i %.
Further, as shown in FIG. 2B, the optical density calculation unit 13 reads the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l solid from the Neugebauer primary color spectral reflectance region 122 of the spectral reflectance database 12, In accordance with the following equation (4), the spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 solid is calculated based on the spectral reflectance R _{t, l} (λ), and the Neugebauer primary color spectroscopic optics in the spectral optical density database 14 is calculated. Stored in the density area 142.
The spectral reflectance R (λ) is the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s (λ) of the paper. , Is a generic term for the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l solid. The spectral optical density D (λ) includes the optical density D _{measure, i} (λ) of the primary color i, the optical density D _{t, i} (λ) of the primary color i, the optical density D _s (λ) of the paper, and the Neugebauer primary color. It is a general term for the spectral optical density D _{t, l} (λ) of 1 solid.

Returning to FIG. 1, the description of the configuration of the color prediction apparatus 100 will be continued.
The spectral optical density database 14 includes a primary color spectral optical density area 141 and a Neugebauer primary color spectral optical density area 142.
The primary color spectral optical density area 141 stores the optical density D _{measure, i} (λ) of the primary color i, the optical density D _{t, i} (λ) of the primary color i, and the optical density D _s (λ) of the paper.
The Neugebauer primary color spectral optical density area 142 stores the spectral optical density D _{t, l} (λ) of the Neugebauer primary color l.

The spectral optical density prediction unit 15 is, for example, a spectral extended Neugebauer model (hereinafter referred to as an optical density-based color prediction model) developed using the spectral optical density. A primary color spectral dot area ratio calculation unit 152, a spectral optical density calculation unit 153, and a spectral reflectance calculation unit 154 are included.
The spectral effective halftone dot area ratio calculation unit 151 is set for each primary color in order to create a color prediction table according to the following equation (5) with reference to the primary color spectral optical density region 141 of the spectral optical density database 14. The effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} at the halftone dot area ratio Y _i % is calculated for each primary color CMY. This spectral effective halftone dot area ratio calculation unit 151 calculates the effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} calculated for each of the plurality of halftone dot area ratios Y _i % to Neugebauer. The data is output to the primary color spectral dot area ratio calculation unit 152.
Note that the optical density D _{measure, i} (λ) of the primary color i includes spectral optical densities corresponding to the halftone dot area ratio X _i % as described above. Therefore, when the halftone dot area ratios X _i % and Y _i % in the optical density D _{measure, i} (λ) and the effective halftone dot area ratio b _{eff, i} (λ) do not match, the spectral effective halftone dot area ratio calculation unit 151. Calculates an effective halftone dot area ratio b _{eff, i} (λ) while interpolating the mismatched portion using an approximate curve, linear interpolation, or the like.

The Neugebauer primary color spectral halftone dot area ratio calculation unit 152 has an effective halftone dot area ratio b _{eff, i} (λ) corresponding to a plurality of halftone dot area ratios Y _i % input from the spectral effective halftone dot area ratio calculation unit 151. Based on {i = C, M, Y}, the spectral dot area ratio F _{b, l} (λ) of the Neugebauer primary color corresponding to the halftone dot area ratio Y _i % in accordance with the equation (6) shown below is obtained. calculate. The Neugebauer primary color spectral halftone dot area ratio calculation unit 152 calculates the spectral dot area ratio F _{b, l} (λ) of the Neugebauer primary color corresponding to the calculated halftone dot area ratio Y _i % in a plurality of stages. To 153.

Spectroscopic optical density calculation unit 153, Neugebauer primary color spectral dot area ratio of plural steps which are inputted from the halftone dot area ratio calculation unit 152 Y _i% in the spectral area coverage F _b of the corresponding Neugebauer _{primaries, l (lambda)} Based on the spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 solid read out from the Neugebauer primary color spectral optical density area 142 of the spectral optical density database 14, the predicted spectral optical density D is calculated according to the following equation (7). (Λ) is calculated. The spectral optical density calculation unit 153 outputs the calculated predicted spectral optical density D (λ) to the spectral reflectance calculation unit 154.

The spectral reflectance calculation unit 154 calculates the predicted spectral reflectance R _D (λ) based on the predicted spectral optical density D (λ) input from the spectral optical density calculation unit 153 according to the following equation (8). The spectral reflectance calculation unit 154 outputs the predicted spectral reflectance R _D (λ) to the color prediction table creation unit 16.

Based on the predicted spectral reflectance R _D (λ), the color prediction table creation unit 16 determines an observation light source, calculates tristimulus values XYZ, CIELab values, and the like, and creates a color prediction table for predicting reproduced colors. At the same time, the color prediction table is reversely converted to create a color separation table. That is, the color prediction table creation unit 16 expresses the reproduction color predicted based on the information of the input data (CMY value in this embodiment) that is the target of the color prediction process and the predicted spectral reflectance R _D (λ). A color prediction table that associates the reproduction color information to be created is created. The color prediction table creation unit 16 also associates an input value such as XYZ and CIELab values from the color prediction table with an output value of a print dot area ratio (CMY value in this embodiment) for reproducing the input value. A separation table is created and output to the output unit 17 together with the color prediction table.
In this embodiment, the color prediction table and the color separation table are created in a known ICC profile format and output.

  Next, an example of a color prediction method performed by the color prediction apparatus 100 according to the present embodiment will be described with reference to FIG. FIG. 3 is a flowchart illustrating an example of a color prediction method performed by the color prediction apparatus 100 according to the present embodiment.

First, the color prediction apparatus 100 performs the following processing.
The input unit 11 inputs a plurality of halftone dot area ratios X _i % for printing color patches set for each primary color i, and outputs them to the printing unit 18. The printing unit 18 prints the primary colors (C, M, Y) on the paper with a halftone dot area ratio X _i %, and also the Neugebauer primary colors (C, M, Y, CM, MY) with the dot area ratio 100%. , YC, CMY, W) on the paper. Thereby, a color patch for color prediction is printed on the paper.
Then, the measurement unit 19 obtains the spectral reflectance based on the reflected light from the paper on which the color patch is printed using the optical element. The measuring unit 19 includes a spectral reflectance R _{measure, i} (λ) of a primary color i, a spectral reflectance R _{t, i} (λ) of a primary color i, a spectral reflectance R _s (λ) of a sheet, and a Neugebauer primary color l. The solid spectral reflectance R _{t, l} (λ) is measured. Then, the measuring unit 19 spectrally _analyzes the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s (λ) of the paper. Stored in the primary color spectral reflectance region 121 of the reflectance database 12. Further, the measurement unit 19 stores the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color 1 solid in the Neugebauer primary color spectral reflectance region 122.

The optical density calculation unit 13 then transmits the spectral reflectance R _{measure, i} (λ) of the primary color i and the spectral reflectance R _{t, i} (λ) of the primary color i from the primary color spectral reflectance region 121 of the spectral reflectance database 12. , And the spectral reflectance R _s (λ) of the paper, the spectral optical density is calculated based on the spectral reflectance according to the equation (4), and the primary color i is stored in the primary spectral optical density area 141 of the spectral optical density database 14. The optical density D _{measure, i} (λ), the primary color i solid optical density D _{t, i} (λ), and the paper optical density D _s (λ) are stored.
The optical density calculation unit 13 reads the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color 1 solid from the Neugebauer primary color spectral reflectance region 122 of the spectral reflectance database 12, and this spectral reflection according to the equation (4). Based on the rate R _{t, l} (λ), the spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 is calculated and stored in the Neugebauer primary color spectral optical density area 142 of the spectral optical density database 14.
Thus, in the primary color spectral optical density area 141, the optical density _{Dmeasure, i} (λ) of the primary color i, the optical density D _{t, i} (λ) of the primary color i, and the optical density D _s (λ) of the paper. And the Neugebauer primary color spectral optical density area 142 stores the spectral optical density D _{t, l} (λ) of the Neugebauer primary color l.

Then, as shown in FIG. 3, the input unit 11 inputs halftone dot setting values (for example, information indicating a plurality of halftone dot area ratios Y _i %) set for creating a color prediction table. This is output to the spectral effective halftone dot area ratio calculation unit 151 of the optical density prediction unit 15 (step ST1).
Spectroscopic effective dot area ratio calculation unit 151, the optical density _{D its measure} primary i from the primary color spectral optical density region 141 of the spectral optical density database _{14, i} (lambda), primary colors i solid optical density _{D t, i} (lambda) , And the optical density D _s (λ) of the paper, and the effective halftone dot area ratio b in a plurality of halftone dot area ratios Y _i % set for each primary color (C, M, Y) according to the equation (5) _{eff, i} (λ) is calculated for each primary color CMY (step ST2).
The spectral effective halftone dot area ratio calculation unit 151 calculates the effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} corresponding to the calculated halftone dot area ratio Y _i %. The image is output to the Neugebauer primary halftone dot area ratio calculation unit 152.

Next, the Neugebauer primary halftone dot area ratio calculation unit 152 is based on the effective dot area ratio b _{eff, i} (λ) {i = C, M, Y} input from the spectral effective dot area ratio calculation unit 151. In accordance with Equation (6), Neugebauer primary halftone dot area ratio F _{b, l} (λ) {l = C, M, Y, CM, MY, YC, corresponding to a plurality of halftone dot area ratios Y _i %, CMY, W} is calculated (step ST3).
The Neugebauer primary color spectral halftone dot area ratio calculation unit 152 calculates the calculated Neugebauer primary spectral area ratio F _{b, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W}. And output to the spectral optical density calculator 153.

The spectral optical density calculation unit 153 receives the Neugebauer primary color halftone dot area ratio calculation unit 152 from the Neugebauer primary color spectral dot area ratio F _{b, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W} and the spectral optical density D _{t, l} (λ) {l = C, M, Y, solid color of the Neugebauer primary color read out from the Neugebauer primary color spectral optical density area 142 of the spectral optical density database 14. Based on CM, MY, YC, CMY, W}, the predicted spectral optical density D (λ) is calculated according to the equation (7) (step ST4).
The spectral optical density calculation unit 153 outputs the calculated predicted spectral optical density D (λ) to the spectral reflectance calculation unit 154.

Next, the spectral reflectance calculation unit 154 calculates the predicted spectral reflectance R _D (λ) based on the predicted spectral optical density D (λ) input from the spectral optical density calculation unit 153 according to Expression (8) (step). ST5).
The spectral reflectance calculation unit 154 outputs the calculated predicted spectral reflectance R _D (λ) to the color prediction table creation unit 16.

Then, the color prediction table creation unit 16 calculates tristimulus values XYZ, CIELab values, and the like representing the reproduced color based on the predicted spectral reflectance R _D (λ), and input image data that is a target of color prediction processing. A color prediction table for associating the input information in FIG. 5 with the reproduced color information representing the reproduced color predicted based on the spectral reflectance R _D (λ) is created, and a color separation table is also created and output to the output unit 17. .

As described above, the color prediction apparatus 100 calculates the spectral optical density based on the measured spectral reflectance. Thereafter, based on the spectral optical density, the spectral optical density prediction unit 15 calculates a predicted spectral optical density. Then, the spectral optical density prediction unit 15 calculates the spectral reflectance based on the predicted spectral optical density, thereby obtaining the predicted spectral reflectance.
Thus, by performing color prediction using the spectral optical density prediction unit 15 that is an optical density-based color prediction model, the accuracy of color prediction can be increased depending on the characteristics of the paper and the printing apparatus.

Here, the effect of color prediction according to the present embodiment will be specifically described with reference to FIG.
FIG. 4 shows the spectral reflectance obtained by measuring the color printed by the printing unit 18 using the measurement unit 19 based on the halftone dot setting value set by the input unit 11 of the present embodiment, and the input unit 11. It is a figure which shows the difference (RMSE) of the spectral reflectance which the reproduction | regeneration in the set halftone dot setting value estimated by the spectral optical density estimation part 15. FIG. FIG. 4 shows the spectral reflectance difference RMSE for each paper (coated paper 1, coated paper 2, silver-deposited paper) having different influences of internal scattering due to printing. The values shown in FIG. 4 are examples of data obtained by experiments using the color prediction apparatus 100 according to the present invention.
The difference RMSE between the actually measured spectral reflectance and the predicted spectral reflectance is an amount indicating a spectral color difference. Note that the silver-deposited paper is much less affected by internal scattering due to printing than the coated paper 1 and the coated paper 2. The coated paper 1 and the coated paper 2 are papers used for printing by a general printing machine.

  As shown in FIG. 4, the difference (RMSE) between the measured spectral reflectance obtained on each paper and the spectral reflectance predicted by the spectroscopic optical density prediction unit 15 is based on the mixed color prediction model in any paper. It is running low. The spectral reflectance predicted by the spectral optical density prediction unit 15 is predicted by the spectral optical density prediction unit 15 using a reflectance-based color prediction model, an optical density-based color prediction model, and a mixed color prediction model. Spectral reflectance. Note that color prediction using a reflectance-based color prediction model or a mixed color prediction model is according to a second embodiment described later, and details thereof will be described later.

When the reflectance-based color prediction model and the optical density-based color prediction model are compared, in the coated paper 1 and the coated paper 2, the color prediction using the optical density-based color prediction model uses the reflectance-based color prediction model. The spectral reflectance difference (RMSE) is smaller than the predicted color.
On the other hand, in silver vapor deposited paper, color prediction using the reflectance-based color prediction model has a smaller spectral reflectance difference (RMSE) than color prediction using the optical density-based color prediction model.
That is, in the case of a paper that is hardly affected by internal scattering due to printing, such as a silver vapor-deposited paper, the accuracy of the color prediction can be improved to some extent even if the color prediction is performed using the reflectance-based color prediction model. On the other hand, coated paper 1 and coated paper 2, which are general printing papers, are greatly affected by internal scattering due to printing compared to silver vapor-deposited papers, so the accuracy of color prediction is higher than that of an optical density-based color prediction model. It can be seen that the reflectance-based color prediction model is lower.

Therefore, by performing color prediction using the optical density-based color prediction model as in this embodiment, the accuracy of color prediction can be increased depending on the characteristics of the paper and the printing unit 19. For paper that is affected by internal scattering due to printing, such as general printing paper, color prediction using the optical density-based color prediction model is more likely to be performed using the reflectance-based color prediction model. Therefore, the color matching in a printing machine that adjusts the reproduction color by area modulation can be performed with high accuracy.
Also, the color patch used is a patch portion that prints the primary colors C, M, and Y at a plurality of halftone dot area ratios X _i % and a Neugebauer primary color at a dot area ratio of 100%. As long as it includes a patch portion to be processed. In addition, it is not necessary to print a color patch having a complex gradation that represents a multi-level halftone dot area ratio as in the conventional case. This eliminates the need for complicated processing such as measuring the spectral reflectance of the color patch corresponding to each gradation and calculating the predicted spectral reflectance based on the measured spectral reflectance, and the processing load associated with the color prediction apparatus 100 Can be reduced and the processing speed can be improved.

[Second Embodiment]
Next, a color prediction device 200 and a color prediction method according to the second embodiment will be described with reference to FIGS.
FIG. 5 is a schematic diagram illustrating an example of the configuration of the color prediction apparatus 200 according to the present embodiment.
As shown in FIG. 5, the color prediction apparatus 200 includes an input unit 11, a spectral reflectance database 12, an optical density calculation unit 13, a spectral optical density database 14, a spectral optical density prediction unit 15, and a color prediction table creation. Unit 16, output unit 17, printing unit 18, measurement unit 19, spectral reflectance prediction unit 21, multi-order color spectral reflectance database 22, weight coefficient calculation unit 23, and mixture prediction unit 24. . In addition, about the structure similar to the above-mentioned color prediction apparatus 100, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

The spectral reflectance prediction unit 21 is, for example, a spectral extended Neugebauer model, and includes a spectral effective halftone dot area rate calculation unit 211, a Neugebauer primary color spectral halftone dot area rate calculation unit 212, and a spectral reflectance calculation unit 213. including. The spectral reflectance prediction unit 21 corresponds to a reflectance-based color prediction model in this specification.
The spectral effective halftone dot area ratio calculating unit 211 refers to the primary color spectral reflectance region 121 of the spectral reflectance database 12 and corresponds to a plurality of halftone dot area ratios Y _i % for each primary color CMY according to Expression (1). The effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} is calculated. The spectral effective halftone dot area ratio calculation unit 211 outputs the calculated effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} to the Neugebauer primary halftone dot area ratio calculation unit 212. . Thereby, the spectral effective halftone dot area ratio calculation unit 211 obtains the spectral effective halftone dot area ratio of each primary color for the given halftone dot area ratio Y _i % in a plurality of stages.

The Neugebauer primary color spectral halftone dot area ratio calculation unit 212 has an effective halftone dot area ratio a _{eff, i} (λ) corresponding to a plurality of halftone dot area ratios Y _i % input from the spectral effective halftone dot area ratio calculation unit 211. Based on {i = C, M, Y}, according to the equation (2), the spectral dot area ratio F _{a, l} (λ) of Neugebauer primaries {l = C, M, Y, CM, MY, YC, CMY, W} is calculated. The Neugebauer primary color spectral halftone dot area rate calculation unit 212 outputs the calculated Neugebauer primary color spectral halftone dot area rate F _{a, l} (λ) to the spectral reflectance calculation unit 213.

The spectral reflectance calculator 213 reads the solid spectral reflectance R _{t, l} (λ) of the Neugebauer primary color 1 read out from the Neugebauer primary color spectral reflectance region 122 and the Neugebauer primary dot spectral ratio halftone dot area ratio calculator 212. Based on the spectral dot area ratio F _{a, l} (λ) of the primary color, the predicted spectral reflectance R (λ) is calculated according to the equation (3).

The multi-order color spectral reflectance database 22 includes a dot area ratio data region 221.
The halftone dot area ratio data region 221 stores, for example, halftone dot area ratio data which is a two-dimensional data set having a halftone dot area ratio of 50%. The halftone dot area ratio data includes the predicted spectral reflectance R (λ) calculated by the spectral reflectance calculator 213 and the predicted spectral reflectance R _D (λ) calculated by the spectral reflectance calculator 154. . Note that the predicted spectral reflectance R (λ) and the predicted spectral reflectance R _D (λ) are predicted spectral reflectances obtained by the spectral optical density prediction unit 15 and the spectral reflectance prediction unit 21 as described above.
The measured spectral reflectance region 222 stores an actual measurement value of the spectral reflectance measured by the measurement unit 19. The measured spectral reflectance region 222 stores the measured spectral reflectance R _{measure, k} (λ) of the secondary color k (k = YM, MC, CY) corresponding to an arbitrary halftone dot area ratio. The measured spectral reflectance R _{measure, k} (λ) is obtained by measuring the reflected light from the paper on which the color patches of the secondary colors (YM, MC, CY) having the halftone dot area ratio are printed. Spectral reflectance. The measured spectral reflectance region 222 stores, for example, the measured spectral reflectance R _{measure, k} (λ) of the secondary color k (k = YM, MC, CY) at a dot area ratio of 50%.

The weighting factor calculation unit 23 reads the predicted spectral reflectance R (λ) and the predicted spectral reflectance R _D (λ) from the halftone dot area rate data region 221 of the multi-order color spectral reflectance database 22 and follows equation (9). Then, the predicted spectral reflectance R _W (λ) at this weight coefficient w is calculated.

In addition, the weighting factor calculation unit 23 reads the measured spectral reflectance R _{measure, k} (λ) from the measured spectral reflectance region 222 of the multi-order color spectral reflectance database 22 and uses the weighting factor w according to the equation (10). A difference RMSE _k between the predicted spectral reflectance R _W (λ) and the measured spectral reflectance R _{measure, k} (λ) is calculated for each secondary color k.

  However, the above formula assumes a case where the spectral reflectance is from 380 nm to 730 nm, and n is the number of steps of the spectral reflectance wavelength.

Further, the weighting factor calculating unit 23 calculates the difference RMSE _k between the predicted spectral reflectance R _W (λ) and the measured spectral reflectance R _{measure, k} (λ) at the weighting factor w for all secondary colors k (k = CM, MY, YC) to obtain an integrated value ΣRMSE of this RMSE _k .
The weighting factor calculation unit 23 determines whether or not the obtained integrated value ΣRMSE satisfies the convergence condition. If the convergence condition is satisfied, the weighting factor w at this time is determined as the optimum value. On the other hand, when the convergence condition is not satisfied, the weighting factor calculation unit 23 corrects the weighting factor w by, for example, successive approximation in the direction in which the integrated value ΣRMSE converges.

The mixture prediction unit 24 receives the weighting factor w calculated by the weighting factor calculation unit 23. This mixed prediction unit 24 is calculated by the predicted spectral reflectance R (λ) calculated by the spectral reflectance calculation unit 213 of the spectral reflectance prediction unit 21 and the spectral reflectance calculation unit 154 of the spectral optical density prediction unit 15. Based on the predicted spectral reflectance R _D (λ) and the weighting coefficient w, the mixed predicted spectral reflectance R _m (λ) is calculated according to Equation (11).

Next, an example of a method for determining the weighting factor w will be described with reference to FIG.
As shown in FIG. 6, when, for example, w = 0.5 is set as the initial value of the weighting factor w (step ST11), the initial value 0.5 of the weighting factor w is calculated via the input unit 11. Input to the unit 23.
The weighting factor calculation unit 23 calculates the predicted spectral reflectance R (for example, YM) of the secondary color corresponding to the halftone dot area ratio 50% from the halftone dot area ratio data region 221 of the multi-order color spectral reflectance database 22. λ) and the predicted spectral reflectance R _D (λ) are read out, and the predicted spectral reflectance R _{W, ym} (λ) at the weighting factor w = 0.5 is obtained according to equation (9) (step ST12).

Next, the weighting factor calculating unit 23 reads the measured spectral reflectance R _{measure, ym} (λ) from the measured spectral reflectance region 222 of the multi-order color spectral reflectance database 22 and weighting factor w = The difference RMSE _ym between the predicted spectral reflectance R _{W, ym} (λ) at 0.5 and the measured spectral reflectance R _{measure, ym} (λ) is calculated (step ST13).

Then, the weight coefficient calculation unit 23 determines whether or not the difference RMSE _ym , the difference RMSE _mc , and the difference RMSE _cy corresponding to all the secondary colors (CM, MY, YC) have been calculated (step ST 14). Here, since the weight coefficient calculation unit 23 has only calculated the difference RMSE _ym (step ST14-NO), the process returns to step ST12.

That is, the weighting factor calculation unit 23 calculates the predicted spectral reflectance R of the secondary color (for example, MC) corresponding to the halftone dot area ratio 50% from the halftone dot area ratio data region 221 of the multi-order color spectral reflectance database 22. (Λ) and the predicted spectral reflectance R _D (λ) are read, and the predicted spectral reflectance R _{W, mc} (λ) at the weighting coefficient w = 0.5 is calculated according to the equation (9) (step ST12). Next, the weighting factor calculation unit 23 reads the measured spectral reflectance _{Rmeasure, mc} (λ) from the measured spectral reflectance region 222 of the multi-order color spectral reflectance database 22, and weighting factor w = A difference RMSE _mc between the predicted spectral reflectance R _{W, mc} (λ) and the measured spectral reflectance R _{measure, mc} (λ) at 0.5 is calculated (step ST13). Similarly, the difference RMSE _cy between the predicted spectral reflectance R _{W, cy} (λ) and the measured spectral reflectance R _{measure, cy} (λ) is calculated.
Then, based on the difference RMSE _ym , the difference RMSE _mc, and the difference RMSE _cy of all the combinations of secondary colors, an integrated value ΣRMSE that is the sum of these is calculated (step ST15).

Next, the weighting factor calculation unit 23 determines whether or not the integrated value ΣRMSE satisfies the convergence condition (step ST16). When the convergence condition is satisfied, the weighting factor w = 0.5 at this time is determined as the optimum value. (Step ST16-OK).
On the other hand, when the convergence condition is not satisfied (step ST16-NG), the weighting factor calculating unit 23 corrects the weighting factor w by, for example, successive approximation in the direction in which the integrated value ΣRMSE converges (step ST17).
Then, the weight coefficient calculation unit 23 resets the difference RMSE _k and the integrated value ΣRMSE (step ST18), and performs steps ST12 to ST6 based on the corrected weight coefficient w.
In this way, the weighting factor calculation unit 23 determines the weighting factor w and outputs it to the mixture prediction unit 24.

  Next, an example of a color prediction method performed by the color prediction apparatus 200 according to the present embodiment will be described with reference to FIG. FIG. 7 is a flowchart illustrating an example of a color prediction method performed by the color prediction apparatus 200 according to the present embodiment. In addition, about the process similar to the color prediction method by the color prediction apparatus 100 mentioned above, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

As shown in FIG. 7, when the input unit 11 outputs a halftone dot area ratio X _i % for printing a color patch set for each primary color to the printing unit 18, the measuring unit 19 is similar to the above. Is measured, and the measurement unit 19 measures the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s ( λ) is stored in the primary color spectral reflectance region 121 of the spectral reflectance database 12, and the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color _l is stored in the Neugebauer primary color spectral reflectance region 122.
Further, the optical density calculation unit 13 converts the spectral reflectance read from the spectral reflectance database 12 into an optical density, and the optical density _{Dmeasure, i} of the primary color i is added to the primary color spectral optical density area 141 of the spectral optical density database 14. (Λ), the optical density D _{t, i} (λ) of the primary color i solid, and the optical density D _s (λ) of the paper, and the spectral optics of the Neugebauer primary color 1 solid in the Neugebauer primary color spectral optical density area 142. The density D _{t, l} (λ) is stored.

On the other hand, the input unit 11 inputs a halftone dot setting value (for example, information indicating a plurality of halftone dot area ratios Y _i %) set for creating a color prediction table, and the spectral reflectance prediction unit 21 This is output to the spectral effective halftone dot area ratio calculation unit 211.
The spectral effective halftone dot area ratio calculating unit 211 then _converts the spectral reflectance R _{measure, i} (λ) of the primary color i from the primary spectral reflectance area 121 of the spectral reflectance database 12 and the spectral reflectance R _{t, of the} primary color i _{. i} (λ) and the spectral reflectance R _s (λ) of the paper are read out, and the effective halftone dot area ratio a _{eff, i} (λ) at the set halftone dot area ratio Y _i % is the primary color according to the equation (1). Calculation is performed for each CMY (step ST22).
The spectral effective halftone dot area ratio calculation unit 211 calculates an effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} corresponding to the calculated halftone dot area ratio Y _i %. The image is output to the Neugebauer primary halftone dot area ratio calculation unit 212.

Subsequently, the Neugebauer primary color spectral halftone dot area rate calculation unit 212 is based on the effective halftone dot area rate a _{eff, i} (λ) {i = C, M, Y} input from the spectral effective halftone dot area rate calculation unit 211. , Neugebauer primary spectral halftone dot area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC, corresponding to a plurality of halftone dot area ratios Y _i % according to equation (2) CMY, W} is calculated (step ST23).
The Neugebauer primary color spectral dot area ratio calculation unit 212 calculates the calculated Neugebauer primary spectral area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W}. And output to the spectral reflectance calculator 213.

Next, the spectral reflectance calculation unit 213 inputs the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color 1 read out from the Neugebauer primary color spectral reflectance region 122 and the Neugebauer primary color spectral dot area ratio calculation unit 212. Based on the spectral halftone dot area ratio F _{a, l} (λ) of the Neugebauer primary color, the predicted spectral reflectance R (λ) is calculated according to the equation (3) and output to the mixed prediction unit 24 (step ST24).
On the other hand, as described above, the spectral optical density prediction unit 15 calculates the predicted spectral reflectance R _D (λ) and outputs it to the mixture prediction unit 24 (steps ST2 to ST5).
Accordingly, the predicted spectral reflectance R (λ) calculated by the spectral reflectance prediction unit 21 and the predicted spectral reflectance R _D (λ) calculated by the spectral optical density prediction unit 15 are input to the mixed prediction unit 24. To do.

Further, the weighting factor calculation unit 23 calculates the weighting factor w as described above and outputs it to the mixed prediction dividing unit 24.
As a result, the mixed prediction unit 24 weights the predicted spectral reflectance R (λ) by the weighting coefficient (1-w) (step ST25) and weights the predicted spectral reflectance R _D (λ). The coefficient w is weighted (step ST26), and the mixed predicted spectral reflectance R _m (λ) is calculated according to the equation (11) (step ST27).

Then, the color prediction table creation unit 16 calculates tristimulus values XYZ, CIELab values, and the like representing the reproduced color based on the mixed predicted spectral reflectance R _m (λ), and an input image that is a target of color prediction processing. A color prediction table that associates input information in the data with reproduced color information that expresses a reproduced color predicted based on the mixed predicted spectral reflectance R _m (λ) is created, and color separation is performed by inversely converting the color prediction table. A table is created and output to the output unit 17.

As described above, the color prediction apparatus 200 calculates the optimum value of the weighting coefficient w in accordance with the characteristics of the printing unit 19 and the paper, and the predicted spectral reflectance R (λ) calculated by the spectral reflectance prediction unit 21. The predicted spectral reflectance can be optimized by mixing the predicted spectral reflectance R _D (λ) calculated by the spectral optical density prediction unit 15 at a ratio corresponding to the weighting coefficient w. That is, when the accuracy of color prediction by the spectral optical density prediction unit 15 based on the spectral optical density is higher than the spectral reflectance, the predicted spectral reflectance R _D mixed with the mixed predicted spectral reflectance R _m (λ). Increase the ratio of (λ). On the other hand, when the accuracy of color prediction by the spectral reflectance prediction unit 21 based on the spectral reflectance is higher than the spectral optical density, the predicted spectral reflectance R (mixed with the mixed predicted spectral reflectance R _m (λ) is Increase the ratio of λ).
As a result, the color prediction apparatus 200 adjusts the weighting coefficient w according to the characteristics of the paper and the printing unit 19 so as to obtain the optimum predicted spectral reflectance as the predicted spectral reflectance predicted as the spectral reflectance representing the reproduced color. The reflectance can be calculated.

[Third Embodiment]
Next, an example of a printing system according to the third embodiment will be described with reference to FIGS. The printing system according to the third embodiment is a system that realizes color matching for reproducing the color of a subject in a printing system that prints a digital image acquired by a digital imaging device such as a digital camera, a digital video camera, or a scanner. It is.
FIG. 8 is a schematic diagram illustrating an example of a configuration of a printing system according to the embodiment of the present invention.
As shown in FIG. 8, a printing system according to an embodiment of the present invention includes an LUT (Look Up Table) creation device 1000, an image input device 2000, a gradation value / halftone dot area rate conversion device 3000, and a reproduction color output. An apparatus 4000.
The printing system according to the embodiment of the present invention refers to the LUT created by the LUT creation device 1000 and, for example, the reproduction color output device 4000 is based on the digital image data obtained by imaging the subject input by the image input device 2000. To print. The LUT creation apparatus 1000 uses the printed product spectral reflectance prediction apparatus 1300 corresponding to the color prediction apparatuses 100 and 200 as described in the first and second embodiments, so that the gradation value based on the spectral reflectance is obtained. An LUT that associates halftone dot area ratios is created. Accordingly, the reproduction color output device 4000 realizes color reproduction that is close to the subject and spectral reflectance without measuring the spectral reflectance from the subject.

As illustrated in FIG. 8, the LUT creation apparatus 1000 includes an input unit 1100, a spectral reflectance estimation device 1200, a printed material spectral reflectance prediction device 1300, an optimization calculation unit (convergence determination unit) 1400, a gradation value / A halftone dot area rate association unit 1500 and an LUT database 1600 are included. The LUT creation apparatus 1000 creates a gradation value / halftone dot area ratio conversion table that is an LUT for converting a gradation value into a halftone dot area ratio. This gradation value / halftone dot area ratio conversion table is a conversion for converting the gradation value of each pixel representing the color characteristics of digital image data into a halftone dot area ratio corresponding to the printing characteristics of the reproduction color output device 4000. It is a table.
An example of the gradation value / halftone dot area ratio conversion table is shown in FIG. FIG. 14 is a diagram showing an example of a gradation value / halftone dot area rate conversion table created by the LUT creation apparatus 1000 according to the present embodiment.
As shown in FIG. 14, the gradation value / halftone dot area ratio conversion table is determined to be the RGB gradation value used for the calculation of the estimated reflectance by the spectral reflectance estimation apparatus 1200 and the optimum value by the optimization calculation unit 1400. 3 is a table in which the halftone dot area ratio Y _{i of} the mixed predicted spectral reflectance is associated with each other.
Against each tone value data K _j, unique RGB gradation values is predetermined. For example, gradation value data K ₁ = (R = 0, G = 0, B = 0), gradation value data K ₂ = (R = 0, G = 0, B = 15),... Value data K _N = (R = 255, G = 255, B = 255). The RGB gradation values for each gradation value data K _j are associated with each other as “conversion source” data.
As the “conversion destination” data, tone value data K _j and halftone dot area ratio Y _i for realizing color reproduction having spectral reflectance close to that of the subject are associated.

The input unit 1100 inputs N gradation value data K _N that are arbitrarily prepared. For example, N = 20 × 20 × 20 = 8000 RGB gradation value data K _N that can be obtained by dividing the R, G, and B channels into 20 is input. Of the gradation value data K _N , any gradation value is denoted as gradation value data K _j (j = 1, 2,..., N).

For example, the spectral reflectance estimation apparatus 1200 estimates the spectral reflectance from RGB gradation value data. As the spectral reflectance estimation apparatus 1200, for example, a configuration as shown in Patent Document 2 can be used. The spectral reflectance estimation method disclosed in Patent Document 2 obtains color chart image data when a standard plate provided with color charts having different spectral reflectances is input under the same image input conditions as the color reproduction estimation object. To do.
For example, the spectral reflectance estimation apparatus 1200 uses a dependent variable obtained by extracting sensor response values corresponding to each color chart from image data, and a basis function that expresses the spectral reflectance and spectral reflectance of each color chart by a linear combination of specific dimensions. Apply the optimization method using the created independent variables. This spectral reflectance estimation apparatus 1200 creates a conversion coefficient for converting a coefficient for a basis function that expresses the spectral reflectance of an object by a linear combination of a specific dimension into a sensor response value of the image input device. The spectral reflectance estimation apparatus 1200 calculates a spectral reflectance candidate corresponding to the sensor response value based on the conversion coefficient and a basis function for expressing the spectral reflectance of the object by a linear combination of a specific dimension. Then, an estimated spectral reflectance R _j ′ (λ) (hereinafter referred to as estimated spectral reflectance) is determined for each gradation value data K _j by optimizing a spectral reflectance candidate that maximizes the existence probability coefficient. . The spectral reflectance estimation apparatus 1200 outputs the determined estimated spectral reflectance R _j ′ (λ) to the optimization calculation unit 1400.
However, the configuration and method of the spectral reflectance estimation apparatus in the construction of the printing system according to the present invention need not be limited to Patent Document 2 as long as spectral reflectance estimation can be performed.

The printed material spectral reflectance prediction apparatus 1300 includes an input unit 131, a spectral reflectance database 132, an optical density calculation unit 133, a spectral optical density database 134, a spectral reflectance prediction unit 135, and a spectral optical density prediction unit 136. , A printing unit 137, a measurement unit 138, and a mixture prediction unit 139. This printed material spectral reflectance prediction apparatus 1300 has a mixed predicted spectral reflectance R _{j, m} (λ) corresponding to a halftone dot area ratio Y _i % set to create a gradation value / halftone dot area ratio conversion table. Is output.

Here, an example of a method of predicting the spectral reflectance of a printed matter using the printed matter spectral reflectance predicting apparatus 1300 will be described.
When adjusting the output color by area modulation in color matching, for example, a Yule-Nielsen spectroscopic Neugebauer model (YULe model) is used as a method for predicting the halftone dot area ratio of a portion where each color is printed with a given halftone dot area ratio. Techniques such as a method using -NieLsen SpecTRaL NeUGeBaUeR ModeL) and a method using a spectral extended Neugebauer model (for example, see Non-Patent Document 1) are known.

  Here, the “spectral extended Neugebauer model” is a model in which the Neugebauer primary color and the halftone dot area ratio are extended to the spectrum so as to be a function of wavelength. In order to explain this model, a formula for predicting the spectral reflectance when a halftone dot is multiplied with the primary colors of three colors of cyan (C), magenta (M), and yellow (Y) is used. explain.

First, the effective dot area ratio a _{eff, i} (λ) with respect to the dot area ratio Y _i of the primary color i is calculated according to the equation (12) obtained by reversing the Murray-Davies equation. The

Note that the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s (λ) of the paper are measured values.
According to the above equation (13), for a given setting of the halftone dot area ratio Y _i % of the primary color i, the spectral effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} is obtained. These effective halftone dot area ratios a _{eff, i} (λ) include optical dot gain and mechanical dot gain, and correspond to obtaining an optimal n value in the Yule-Nielsen spectroscopic Neugebauer model. .
The "mechanical dot gain" here is an error in the effective halftone dot area ratio that occurs when the halftone dots are crushed and thickened by printing with a printing plate or a printing press. It is different for each machine. In addition, “optical dot gain” here is an error in the effective halftone dot area ratio caused by irregular reflection of a color material (for example, ink or toner) penetrating the surface of the print medium. It differs depending on the characteristics.

Next, the spectral dot area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W} of the Neugebauer primary color l is calculated from the following equation (13).

Using the spectral dot area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W} of the Neugebauer primary color l calculated by the above equation (13), The predicted spectral reflectance R _j (λ) is obtained from Equation (14).

  The printed material spectral reflectance prediction apparatus 1300 uses a spectral reflectance prediction method that can increase the accuracy of predicting the spectral reflectance of a printed material and reduce the processing load on the device when predicting the spectral reflectance of the printed material. is doing.

The input unit 131 is connected to the optimization calculation unit 1400 and inputs the halftone dot area ratio output from the optimization calculation unit 1400. When initial information is sent from the optimization calculation unit 14, an arbitrary halftone dot area rate is input. The input unit 131 is connected to, for example, an external computer or the like, and inputs an arbitrary halftone dot area ratio setting value for each primary color i. Specifically, the input unit 11 is, for example, a halftone dot setting value set to create a gradation value / halftone dot area rate conversion table (for example, information indicating a plurality of halftone dot area ratios Y _i %). ) And a halftone dot setting value set for printing a color patch (for example, information indicating a plurality of halftone dot area ratios X _i %).

The spectral reflectance database 132 includes a primary color spectral reflectance region 1321 and a Neugebauer primary color spectral reflectance region 1322.
The spectral reflectance database 132 stores the spectral reflectance of each primary color i of the measured color patch. Here, an example in which three colors of yellow (Y), magenta (M), and cyan (C) are used as the color patch of the primary color i will be described below. In addition, the color patch includes at least a patch portion on which each primary color i (i = C, M, Y) is printed with a halftone dot area ratio X _i % (i = C, M, Y), and a halftone dot area ratio. And a patch portion on which a Neugebauer primary color 1 having a point area ratio of 100% is printed.
The patch portion printed with the halftone dot area ratio X _i % is a primary color of each primary color i, and is a dot area ratio set for printing a color patch, for example, 5%. Printing is performed at a halftone dot area ratio X _i % set at intervals. Further, the Neugebauer primary color 1 includes eight primary colors (l = C, M, Y), secondary colors (l = CM, CY, MY), and tertiary colors (l = CMY), and the base color. (L = W).
This color patch is printed on the print medium by the printing unit 137 when the halftone dot area ratio X _i % set for printing the color patch input from the input unit 131 is input to the printing unit 137. Is done.
For example, the printing unit 137 performs plate printing such as offset printing, and performs a series of printing processing including RIP (Raster Image Processor) processing and CTP (Computer To Plate) processing.

The primary color spectral reflectance region 1321 is, for example, a spectral reflectance R _{measure, i} (λ) of a primary color i (i = C, M, Y), a spectral reflectance of a primary color i (i = C, M, Y) solid. R _{t, i} (λ) and the spectral reflectance R _s (λ) of the paper are stored. Note that the spectral reflectance R _{measure, i} (λ) of the primary color i (i = C, M, Y) is based on a halftone dot area ratio X _i % set for printing a color patch. A measured value obtained by measuring the spectral reflectance of a color patch in which the printing unit 137 printed a primary color (C, M, Y) of a primary color i (i = C, M, Y) on a printing medium with a point area ratio X _i %. is there. Further, the spectral reflectance R _{t, i} (λ) of the primary color i (i = C, M, Y) is a halftone dot area ratio of 100%, and the primary color (C, M, Y) is the primary color (C, M, Y). Is an actual measurement value obtained by measuring the spectral reflectance of the color patch printed on the print medium. The spectral reflectance R _s (λ) of the paper that is the printing medium is an actual measurement value obtained by measuring the spectral reflectance of the paper portion on which nothing is printed. The spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t , i} (λ) of the primary color i, and the spectral reflectance R _s (λ) of the paper that is the printing medium are the printing unit 137. For example, the spectral reflectance measured by the measurement unit 138 based on the color patch printed on the print medium.
The measuring unit 138 includes an optical element, measures the spectral reflectance of the color patch, and stores the measured spectral reflectance in the spectral reflectance database 132.

The Neugebauer primary color spectral reflectance region 1322 stores the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l solid. The spectral reflectance R _{t, l} (λ) is a dot area ratio of 100% and Neugebauer primary color l (l = C, M, Y, CM, MY, YC, CMY, W) is printed on the printing medium by the printing unit 137. It is the actual measurement value which measured the spectral reflectance of the printed color patch. The spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l is, for example, the spectral reflectance measured by the measuring unit 138 based on the color patch printed by the printing unit 137.

The optical density calculation unit 133, as shown in FIG. 9, reads the spectral reflectance from the primary spectral reflectance area 1321 of the spectral reflectance database 132, according to the following equation (15), in the spectral reflectance R _{j (lambda)} Based on this, the spectral optical density D _j (λ) is calculated and stored in the primary color spectral optical density area 1341 of the spectral optical density database 134. In other words, the optical density calculation unit 133 calculates the optical density D _{measure, i} (λ) of the primary color i based on the spectral reflectance R _{measure, i} (λ) of the primary color i. The optical density calculator 133 calculates the optical density D _{t, i} (λ) of the primary color i solid based on the spectral reflectance R _{t, i} (λ) of the primary color i solid. Further, the optical density calculator 133 calculates the optical density D _s (λ) of the paper based on the spectral reflectance R _s (λ) of the paper.
Incidentally, as described above, the spectral reflectance of the primaries _i R _measure, i _(λ) in accordance with the dot percentage X _i% plural stages color patches are printed, the halftone dot area ratio of the plurality of stages X _i The spectral reflectance corresponding to% is included. For this reason, the optical density D _{measure, i} (λ) of the primary color i also includes spectral optical densities corresponding to the halftone dot area ratio X _i %.
Further, as shown in FIG. 10, the optical density calculation unit 133 reads the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l solid from the Neugebauer primary color spectral reflectance region 1322 of the spectral reflectance database 132, and In accordance with (15), the spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 solid is calculated based on the spectral reflectance R _{t, l} (λ), and the Neugebauer primary color spectral optical density region 1342 of the spectral optical density database 134 is calculated. To store.

The spectral reflectance R _j (λ) is the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s (λ of the paper). ), A general term for the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color l solid. Further, the spectral optical density D _j (λ) is the optical density D _{measure, i} (λ) of the primary color i, the optical density D _{t, i} (λ) of the primary color i, the optical density D _s (λ) of the paper, and Neugebauer. This is a general term for the spectral optical density D _{t, l} (λ) of the primary color 1 solid.

Returning to FIG. 8, the description of the configuration of the printed matter spectral reflectance prediction apparatus 1300 will be continued.
The spectral optical density database 134 includes a primary color spectral optical density area 1341 and a Neugebauer primary color spectral optical density area 1342.
The primary color spectral optical density area 1341 includes an optical density D _{measure, i} (λ) of a primary color i, an optical density D _{t, i} (λ) of a primary color i, and an optical density D _s (λ) of a sheet as a printing medium. Is stored.
The Neugebauer primary color spectral optical density region 1342 stores the spectral optical density D _{t, l} (λ) of the Neugebauer primary color l.

The spectral reflectance predicting unit 135 is, for example, a spectral extended Neugebauer model, which is a spectral effective halftone dot area rate calculating unit 1351, a Neugebauer primary color spectral halftone dot area rate calculating unit 1352, and a spectral reflectance calculating unit 1353. including.
The spectral effective halftone dot area rate calculation unit 1351 refers to the primary color spectral reflectance region 1321 of the spectral reflectance database 132 and corresponds to the multi-level halftone dot area rate Y _i % for each primary color CMY according to the equation (12). The effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} is calculated. The spectral effective halftone dot area ratio calculation unit 1351 outputs the calculated effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} to the Neugebauer primary color spectral halftone dot area ratio calculation unit 1352. . Thereby, the spectral effective halftone dot area ratio calculation unit 1351 obtains the spectral effective halftone dot area ratio of each primary color i for the given halftone dot area ratio Y _i % in a plurality of stages.

The Neugebauer primary halftone dot area ratio calculation unit 1352 has an effective halftone dot area ratio a _{eff, i} (λ) corresponding to a plurality of halftone dot area ratios Y _i % input from the spectral effective halftone dot area ratio calculation unit 1351. Based on {i = C, M, Y}, according to equation (13), Neugebauer primary dot area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W} is calculated. The Neugebauer primary color spectral halftone dot area ratio calculating unit 1352 outputs the calculated spectral halftone dot area ratio F _{a, l} (λ) of the Neugebauer primary color 1 to the spectral reflectance calculating unit 1353.

The spectral reflectance calculation unit 1353 is a Neugebauer primary color spectral reflectance R _{t, l} (λ) read from the Neugebauer primary color spectral reflectance region 1322 and the Neugebauer primary color spectral halftone dot area rate calculation unit 1352. Based on the spectral halftone dot area ratio F _{a, l} (λ) of _l , the predicted spectral reflectance R (λ) is calculated according to the equation (14). The spectral reflectance calculator 1353 outputs the expected spectral reflectance R (λ) to the mixed predictor 139.

The spectroscopic optical density prediction unit 136 is, for example, a spectral extended Neugebauer model (optical density base halftone dot area rate prediction model) developed using the spectroscopic optical density, and a spectral effective halftone dot area rate calculation unit 1361; A Neugebauer primary dot spectral dot area ratio calculation unit 1362, a spectral optical density calculation unit 1363, and a spectral reflectance calculation unit 1364 are included.
The spectral effective halftone dot area ratio calculating unit 1361 refers to the primary color spectral optical density area 1341 of the spectral optical density database 134 and, according to the following expression (16), a plurality of halftone dot area ratios set for each primary color. The effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} at Y _i % is calculated for each primary color CMY. This spectral effective halftone dot area ratio calculation unit 1361 calculates the effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} calculated for each of the plurality of halftone dot area ratios Y _i % to Neugebauer. The data is output to the primary color spectral dot area ratio calculation unit 1362.
Note that the optical density D _{measure, i} (λ) of the primary color i includes spectral optical densities corresponding to the halftone dot area ratio X _i % as described above. Therefore, when the halftone dot area ratio X _i % and Y _i % in the optical density D _{measure, i} (λ) and the effective halftone dot area ratio b _{eff, i} (λ) do not match, the spectral effective halftone dot area ratio calculating unit 1361 Calculates an effective halftone dot area ratio b _{eff, i} (λ) while interpolating the mismatched portion using an approximate curve, linear interpolation, or the like.

The Neugebauer primary halftone dot area ratio calculation unit 1362 has an effective halftone dot area ratio b _{eff, i} (λ) corresponding to a plurality of halftone dot area ratios Y _i % input from the spectral effective halftone dot area ratio calculation unit 1361. Based on {i = C, M, Y}, according to the following equation (17), the spectral dot area ratio F _{b, l} (λ) of the Neugebauer primary color corresponding to the halftone dot area ratio Y _i % is obtained. calculate. The Neugebauer primary halftone dot area ratio calculation unit 1362 calculates the Neugebauer primary halftone dot area ratio F _{b, l} (λ) corresponding to the calculated halftone dot area ratio Y _i % to the spectral optical density calculation unit. 1363.

The spectral optical density calculation unit 1363 is a Neugebauer primary color halftone dot area ratio F _{b, l} (λ) corresponding to a plurality of stages of halftone dot area ratio Y _i % inputted from the Neugebauer primary halftone dot area ratio calculation unit 1362. And the predicted spectral optical density D according to the following equation (18) based on the spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 solid read out from the Neugebauer primary color spectral optical density area 1342 of the spectral optical density database 134. _j (λ) is calculated. The spectral optical density calculation unit 1363 outputs the calculated predicted spectral optical density D _j (λ) to the spectral reflectance calculation unit 1364.

The spectral reflectance calculation unit 1364 calculates the predicted spectral reflectance R _{j, D} (λ) based on the predicted spectral optical density D _j (λ) input from the spectral optical density calculation unit 1363 according to the equation (19) shown below. calculate. The spectral reflectance calculator 1364 outputs the predicted spectral reflectance R _{j, D} (λ) to the mixed predictor 139.

The mixed prediction unit 139 calculates the predicted spectral reflectance R _j (λ) calculated by the spectral reflectance calculation unit 1353 of the spectral reflectance prediction unit 135 and the spectral reflectance calculation unit 1364 of the spectral optical density prediction unit 136. Based on the predicted spectral reflectance R _{j, D} (λ) and the weighting coefficient w, the mixed predicted spectral reflectance R _{j, m} (λ) is calculated according to the following equation (20). The mixed prediction unit 139 outputs the mixed predicted spectral reflectance R _{j, m} (λ) to the optimization calculation unit 14. The weighting factor w is determined by a person by empirically setting w = 0.5, for example, and is set in advance as a set value.

The optimization calculation unit 1400 includes the estimated spectral reflectance R _j ′ (λ) input from the spectral reflectance estimation apparatus 1200 and the mixed predicted spectral reflectance R _{j, m} (λ) input from the mixed prediction unit 139. The difference is calculated and the convergence is determined. If the convergence condition is satisfied, the halftone dot area ratio Y _i acquired from the input unit 131 is output to the gradation value / halftone dot area ratio correspondence unit 1500. If the convergence condition is not satisfied, the halftone dot area rate is updated by the successive approximation optimization method and output to the input unit 131.

The gradation value / halftone dot area ratio associating unit 1500 associates the halftone dot area ratio Y _i input from the optimization calculating unit 1400 with the gradation value data K _j, and performs gradation values in the LUT database 1600.・ Write to halftone dot area rate conversion table.

The LUT database 1600 is a database that stores a gradation value / halftone area ratio conversion table that associates the gradation value data K _j with the halftone area ratio Y _i .

  The image input device 2000 is connected to, for example, an external computer or the like, and inputs digital image data including a subject image. This image input device 2000 uses a three-channel optical sensor of red (R), green (G), and blue (B) as the gradation value of each pixel of image data including an image of a subject to be subjected to spectral reflectance estimation. The potential signal detected by is processed and acquired.

The gradation value / halftone dot area ratio conversion device 3000 uses the gradation value / halftone dot area ratio conversion table to represent the gradation value k _j of each pixel p of the digital image data as a reproduction color (output color). Convert to halftone dot area ratio Y _i .

The reproduction color output device 4000 prints, for example, an image of a subject on a medium based on the halftone dot area rate Y _i input from the gradation value / halftone dot area rate conversion device 3000.

  Next, an example of the configuration of the printing system according to the embodiment of the present invention will be described with reference to FIGS. FIG. 11 is a flowchart showing an example of a method for creating a gradation value / halftone dot area ratio conversion table in the printing system according to the embodiment of the present invention. FIG. 12 is a flowchart illustrating an example of a printing method that approximates and reproduces the spectral reflectance of a subject in the printing system according to the embodiment of the present invention. Note that the creation of the gradation value / halftone dot area ratio conversion table and the printing of the digital image are not on the same flow. This will be explained separately.

  First, the flowchart shown in FIG. 11 will be described. In the flowchart shown in FIG. 11, an example of processing until the LUT creation apparatus 1000 creates a gradation value / halftone area ratio conversion table in the ICC profile format and stores it in the LUT database 1600 will be described.

The input unit 1100 inputs the N pieces of gradient data _{K N.} For example, the gradation value data K _N indicating N = 20 × 20 × 20 = 8000 RGB gradation values that can be obtained by dividing the R, G, and B channels into 20 is input (step ST31).

The input unit 1100 selects the gradation value data K _j from the gradation value data K _N acquired in step ST31, and outputs it to the spectral reflectance estimation apparatus 12. However, j = 1, 2,..., N, and j = 1 is given as an initial value. That is, the input unit 1100 sequentially selects the gradation value data K ₁ , K ₂ , K ₃ ,..., K _N and outputs them to the spectral reflectance estimation apparatus 1200 (step ST32).

The spectral reflectance estimation apparatus 1200 calculates the estimated spectral reflectance R _j ′ (λ) corresponding to the gradation value data K _j input from the input unit 1100, and outputs it to the optimization calculation unit 14 (step ST33). Note that the estimated spectral reflectance R _j ′ (λ) calculated by the spectral reflectance estimation apparatus 1200 is a numerical value estimated based on environment information when the subject is photographed. That is, it is preferable that the environment in which the digital image data acquired by the image input device 2000 is captured is similar to the environment used for calculating the estimated spectral reflectance R _j ′ (λ).

Next, the spectral reflectance database 132 and the spectral optical density database 134 used for calculating the predicted spectral reflectance R (λ) and the predicted spectral reflectance R _D (λ) by printing of the printing unit 137 in steps ST34 to ST44. An example of the creation method will be described. Note that the creation of the spectral reflectance database 132 and the spectral optical density database 134 described in steps ST34 to ST44 here is performed independently of the creation flow of the gradation value / halftone dot area ratio conversion table shown in FIG. It doesn't matter. That is, creation flow gradation value-dot percent conversion table shown in FIG. 11 does not include the processing in steps ST34～44, in step ST45 to be described later, mixing the predicted spectral reflectance is calculated in a separate flow R _{j , M} (λ) may be input.

First, the input unit 131 outputs a halftone dot area ratio X _i % for printing a color patch set for each primary color i to the printing unit 137. The printing unit 137 prints the patch portions of the primary colors (C, M, Y) at the halftone dot area ratio X _i %, and at the same time the Neugebauer primary colors (C, M, Y, CM) at the dot area ratio of 100%. , MY, YC, CMY, W). Thereby, a color patch for predicting the dot area ratio is obtained.
Then, the measurement unit 138 obtains the spectral reflectance from the reflected light of the color patch using an optical element. The measuring unit 138 includes a spectral reflectance R _{measure, i} (λ) of the primary color i, a spectral reflectance R _{t, i} (λ) of the primary color i, a spectral reflectance R _s (λ) of the paper, and a Neugebauer primary color L. The solid spectral reflectance R _{t, l} (λ) is measured. Then, the measurement unit 138 spectrally _analyzes the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, and the spectral reflectance R _s (λ) of the paper. The primary color spectral reflectance area 1321 of the reflectance database 132 is stored. In addition, the measurement unit 138 stores the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color L solid in the Neugebauer primary color spectral reflectance region 1322.

The optical density calculation unit 133 then transmits the spectral reflectance R _{measure, i} (λ) of the primary color i from the primary spectral reflectance region 1321 of the spectral reflectance database 132 and the spectral reflectance R _{t, i} (λ) of the primary color i. , And the spectral reflectance R _s (λ) of the paper are read, and the spectral optical density D _j (λ) is calculated based on the spectral reflectance R _j (λ) according to the above equation (15). Then, the optical density calculation unit 133 calculates the calculated optical density D _{measure, i} (λ) of the primary color i, the optical density D _{t, i} (λ) of the primary color i, and the optical density D _s (λ) of the paper. And stored in the primary color spectral optical density area 1341 of the spectral optical density database 134.
Further, the optical density calculation unit 133 reads the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color 1 solid from the Neugebauer primary color spectral reflectance region 1322 of the spectral reflectance database 132, and this spectral reflection according to the equation (15). Based on the rate R _{t, l} (λ), the spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 is calculated. Then, the optical density calculator 133 stores the calculated spectral optical density D _{t, l} (λ) of the Neugebauer primary color 1 solid in the Neugebauer primary color spectral optical density area 1342 of the spectral optical density database 134.
As a result, the primary color spectral optical density area 1341 has an optical density D _{measure, i} (λ) of the primary color i, an optical density D _{t, i} (λ) of the primary color i, and an optical density D of the paper as the printing medium. _s (λ) is stored, and the Neugebauer primary color spectral optical density area 1342 stores the spectral optical density D _{t, l} (λ) of the Neugebauer primary color l.

The continuation of the flow of creating the gradation value / halftone dot area ratio conversion table after step ST34 will be described again with reference to the flowchart shown in FIG.
The input unit 131 inputs a halftone dot setting value (for example, information indicating a plurality of halftone dot area ratios Y _i %) set to create a gradation value / halftone dot area ratio conversion table, and spectral reflection The output is output to the spectral effective halftone dot area ratio calculating unit 1351 of the ratio predicting unit 135 and the spectral effective halftone dot area ratio calculating unit 1361 of the spectral optical density predicting unit 136 (step ST34).
Here, the initial value of the halftone dot area ratio Y _i given to the input unit 131 is, for example, selected from an arbitrary initial value data set, and other than the initial value is a halftone dot area ratio that is sequentially updated in the optimization calculation unit 1400. .

The spectral effective halftone dot area ratio calculation unit 1351 then transmits the spectral reflectance R _{measure, i} (λ) of the primary color i from the primary color spectral reflectance area 1321 of the spectral reflectance database 132, and the spectral reflectance R _{t, of the} primary color i _{. i} (λ) and the spectral reflectance R _s (λ) of the paper are read out, and the effective halftone dot area ratio a _{eff, i} (λ) at the set halftone dot area ratio Y _i % is the primary color according to the equation (12). Calculation is performed for each CMY (step ST35).
The spectral effective halftone dot area ratio calculating unit 1351 calculates the effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} corresponding to the calculated halftone dot area ratio Y _i %. The image is output to the Neugebauer primary color spectral halftone dot area ratio calculation unit 1352.

Next, the Neugebauer primary halftone dot area ratio calculation unit 1352 is based on the effective halftone dot area ratio a _{eff, i} (λ) {i = C, M, Y} input from the spectral effective halftone dot area ratio calculation unit 1351. , The spectral dot area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC of Neugebauer primary color l corresponding to the halftone dot area ratio Y _i % in accordance with the equation (13). , CMY, W} are calculated (step ST36).
The Neugebauer primary color spectral halftone dot area ratio calculation unit 1352 calculates the spectral halftone dot area ratio F _{a, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W} of the calculated Neugebauer primary color l. Is output to the spectral reflectance calculation unit 1353.

Then, the spectral reflectance calculation unit 1353 inputs the spectral reflectance R _{t, l} (λ) of the Neugebauer primary color 1 solid read out from the Neugebauer primary color spectral reflectance region 1322 and the Neugebauer primary color spectral dot area ratio calculation unit 1352. Based on the spectral halftone dot area rate F _{a, l} (λ) of the Neugebauer primary color l, the predicted spectral reflectance R _j (λ) is calculated according to the equation (14) and output to the mixed prediction unit 139 (step ST37).

Here, the weight coefficient w calculated in advance by the above-described weight coefficient calculation unit 23 (not shown) is stored in the memory of the mixed prediction division unit 139.
The mixed prediction unit 139 reads information indicating the weighting factor w from its own memory, weights the predicted spectral reflectance R _j (λ) with the weighting factor (1-w), and (1−w) · R _j (λ) is calculated (step ST38). The mixed prediction unit 139 stores the calculated (1-w) · R _j (λ) in its own memory.

The spectral effective halftone dot area ratio calculation unit 1361 starts from the primary color spectral optical density region 1341 of the spectral optical density database 134 and the optical density D _{measure, i} (λ) of the primary color i and the optical density D _{t, i} (λ) of the primary color i. , And the optical density D _s (λ) of the paper, and the effective halftone dot area ratio b in a plurality of halftone dot area ratios Y _i % set for each primary color (C, M, Y) according to the equation (16) _{eff, i} (λ) is calculated for each primary color CMY (step ST39).
The spectral effective halftone dot area ratio calculating unit 1361 calculates the effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} corresponding to the calculated halftone dot area ratio Y _i %. The image is output to the Neugebauer primary halftone dot area ratio calculation unit 1362.

The Neugebauer primary color spectral halftone dot area ratio calculating unit 1362 is based on the effective halftone dot area ratio b _{eff, i} (λ) {i = C, M, Y} input from the spectral effective halftone dot area ratio calculating unit 1361. According to (17), Neugebauer primary halftone dot area ratio F _{b, l} (λ) {L = C, M, Y, CM, MY, YC, CMY, corresponding to a plurality of halftone dot area ratios Y _i % W} is calculated (step ST40).
The Neugebauer primary color spectral halftone dot area ratio calculating unit 1362 calculates the calculated Neugebauer primary spectral area ratio F _{b, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W}. And output to the spectral optical density calculator 1363.

The spectral optical density calculation unit 1363 receives the Neugebauer primary color spectral dot area ratio F _{b, l} (λ) {l = C, M, Y, CM, MY, YC, CMY, W} and the spectral optical density D _{t, l} (λ) {L = C, M, Y, CM, of the Neugebauer primary color 1 solid read out from the Neugebauer primary color spectral optical density area 1342 of the spectral optical density database 134. Based on MY, YC, CMY, W}, the predicted spectral optical density D _j (λ) is calculated according to the equation (18) (step ST41).
The spectral optical density calculator 1363 outputs the calculated predicted spectral optical density D _j (λ) to the spectral reflectance calculator 1364.

The spectral reflectance calculation unit 1364 calculates the predicted spectral reflectance R _{j, D} (λ) based on the predicted spectral optical density D _j (λ) input from the spectral optical density calculation unit 1363 according to Expression (19). The spectral reflectance calculator 1364 outputs the calculated predicted spectral reflectance R _{j, D} (λ) to the mixed predictor 139 (step ST42).

The mixed prediction unit 139 reads information indicating the weighting factor w from its own memory, weights the predicted spectral reflectance R _{j, D} (λ) with the weighting factor w, and obtains w · R _{j, D} (λ ) Is calculated (step ST43). The mixed prediction unit 139 stores the calculated w · R _{j, D} (λ) in its own memory.

The mixed prediction unit 139 stores, from its own memory, the predicted spectral reflectance (1-w) · R _j (λ) weighted in step ST38 and the predicted spectral reflectance w · R _j, weighted in step ST43 _{. D} (λ) is read out, and the mixed predicted spectral reflectance R _{j, m} (λ) is calculated according to equation (20) (step ST44).
The mixture prediction unit 139 outputs the calculated predicted mixture spectral reflectance R _{j, m} (λ) to the optimization calculation unit 1400.

The optimization calculation unit 1400 performs convergence determination for the estimated spectral reflectance R _j ′ (λ) calculated by the spectral reflectance estimation apparatus 12 in step ST33.
For example, the determination condition for convergence is E _j <E _{j, 0} (E _j = | R _{j, m} (λ) −R _j ′ (λ) |, where E _{j, 0} is a convergence condition). This convergence condition E _{j, 0} is determined in advance. The convergence condition E _{j, 0} may be a different value for each gradation data K _j or may be the same value.
In this case, the optimization calculation unit 1400 receives the mixed predicted spectral reflectance R _{j, m} (λ) input from the mixed prediction unit 139 and the estimated spectral reflectance R _j ′ (λ) input from the spectral reflectance estimation apparatus 1200. The absolute value E _j of the difference is calculated for each gradation data K _j .
Note that the present invention is not limited to this, and for example, a determination condition using a color difference may be applied. By using the color difference, it is possible to determine convergence even when the values of the mixed predicted spectral reflectance R _{j, m} (λ) and the estimated spectral reflectance R _j ′ (λ) are greatly different.

The optimization calculation unit 1400 _compares “the absolute value E _j of the difference between the mixed predicted spectral reflectance R _{j, m} (λ) and the estimated spectral reflectance R _j ′ (λ)” corresponding to the gradation value data K _j. “Convergence condition E _{j, 0} ” is compared, and if the absolute value E _{j of the} difference is less than the convergence condition E _{j, 0} , it is determined that convergence has occurred. In other words, when the convergence condition is satisfied, the optimization calculation unit 1400 determines that the estimated spectral reflectance R _j ′ (λ) corresponding to the gradation value data K _j calculated by the spectral reflectance estimation apparatus 1200 is the optimal value. To do.
If the optimization calculation unit 1400 determines that the convergence has occurred, the optimization value calculation unit 1400 obtains the gradation value data K _j corresponding to the estimated spectral reflectance R _j ′ (λ) and the gradation value / halftone dot area rate conversion table. The halftone dot area ratio Y _i set for creation is output to the gradation value / halftone dot area ratio associating unit 1500.

Next, the gradation value / halftone dot area ratio associating unit 1500 associates the input gradation value data K _j with the halftone dot area ratio Y _i to associate the gradation value / halftone dot area in the LUT database 1600 with each other. The data is written into the rate conversion table and stored in the LUT database 1600 (step ST46).
Then, the gradation value / halftone area ratio associating unit 1500 determines whether or not the halftone setting value Y _i is associated with all the gradation value data K _j (step ST47).
If dot set value Y _i is associated to a gradation value data K _N (j = _N), grayscale value, dot percent association unit 15, the tone value-dot percent conversion table Finish the creation process. When j = N is not satisfied (for example, j <N), the gradation value / halftone dot area ratio association unit 1500 returns to step ST32 and repeats the process. In this case, the LUT creation apparatus 1000 counts up the gradation value data K _j (j ← j + 1).

On the other hand, if the absolute value E _j of the difference is greater than or equal to the convergence condition E _{j, 0} in step ST45, the optimization calculating unit 1400 determines that the convergence condition is not satisfied.
In this case, the optimization calculation unit 1400 corrects the halftone dot area ratio Y _i % of each primary color i. The optimization calculation unit 1400 uses, for example, a successive approximation optimization method such as a conjugate gradient method or a SimpLex method as a correction method of the halftone dot area ratio Y _i % of each primary color i, and the corrected halftone dot area ratio Y _i. % Is output to the input unit 131. Then, the LUT creation apparatus 1000 returns to step ST34 and repeats the processes of steps ST34 to 45 for the corrected dot area ratio Y _i %.

As described above, the gradation value / halftone dot area ratio conversion table for approximating and reproducing the spectral reflectance of the subject by printing is created by the method for creating the gradation value / halftone dot area ratio conversion table described above. According to the method described here, it is possible to print and measure a small number of color patches without using a very large number of color patches as in the prior art, or higher accuracy than the known spectral reflectance prediction model of printing. In addition, the LUT can be created.
Further, by photographing the subject under the same image input conditions (pointing to the photographing light source and the sensitivity characteristic of the camera) as when creating the gradation value / halftone dot area ratio conversion table, the method described in FIG. In addition, the spectral reflectance of the subject can be approximately reproduced by printing.

Next, an example of a printing method using the gradation value / halftone area ratio conversion table according to the present embodiment will be described with reference to FIG. FIG. 12 is a flowchart illustrating an example of a printing method using the gradation value / halftone area ratio conversion table according to the present embodiment.
The image input device 2000 acquires digital image data including a subject using a digital imaging device (step ST51). For example, the image input device 2000 acquires digital image data generated when the camera captures a subject from the camera. Note that the image input device 2000 may be a camera or the like, and may acquire digital image data by photographing itself.
The image input apparatus 2000 outputs gradation value information indicating the gradation value of each pixel of the acquired digital image data to the gradation value / halftone dot area ratio conversion apparatus 3000. For example, the image input apparatus 2000 performs image processing on digital image data having the number of pixels p (p = x × y), and gradation value data K _{j, p} (p = 1, 2) indicating the gradation value of each pixel. ,..., X × y), and outputs the gradation value information indicating the gradation value data K _{j, p} to the gradation value / halftone dot area ratio converter 3000.

Next, the gradation value / halftone dot area ratio conversion device 3000 refers to the gradation value / halftone dot area ratio conversion table stored in the LUT database 1600 and refers to the gradation value of each pixel p of the acquired digital image data. Data K _{j, p} is converted to a corresponding halftone dot area ratio Y _i %. In other words, the gradation value / halftone dot area rate conversion device 3000 uses the halftone dot area ratio Y _i % associated with the gradation value data K _{j, p in} the gradation value / halftone area ratio conversion table as a pixel. Obtained as a dot area ratio Y _i % indicating the color characteristics of p. The gradation value / halftone dot area rate conversion device 3000 outputs a halftone dot area rate Y _i % value corresponding to each pixel p to the reproduction color output device 4000 (step ST52).
More specifically, the gradation value / halftone dot area ratio conversion device 3000 uses the same gradation value as the gradation value data K _{j, p} of each pixel of the digital image data in the table stored in the LUT database 1600. The halftone dot area ratio Y _i % associated with the data K _j is acquired and output to the reproduction color output device 4000.

The reproduction color output device 4000 prints, for example, an image of a subject on a medium according to the dot area ratio Y _i % corresponding to the gradation value data K _{j, p} of each pixel of the acquired digital image data.

  As described above, by using an LUT created in advance using a model extended to spectroscopy (reflectance-based color prediction model, optical density-based color prediction model), the subject is normally used without being photographed spectroscopically. Spectral reflectance can be approximated by printing from an image taken with such a photographing device. In other words, a subject photographed with a general camera is calculated based on the spectral reflectance without using a device for measuring the spectral reflectance from the subject (for example, a spectroscopic colorimeter or a camera having this function). It is possible to print with the reproduced color. Further, in this case, it is not necessary to estimate the spectral reflectance of the subject each time, and further, it is not necessary to perform optimization calculation for calculating the dot area ratio each time. This cannot be achieved by the conventional technology as described in the prior art document, and is a configuration excellent in simplicity.

  As one method of color matching, a method of matching the input tristimulus value with the output tristimulus value has been adopted. In general, a CIE-XYZ color system or a CIE-Lab color system defined by the CIE (International Lighting Commission) is used as a space for quantifying tristimulus values. An image acquired by a digital imaging device is processed and output in an XYZ color space or an L * a * b * color space, which is a device-independent color space. However, in this method, even if the tristimulus values XYZ and L * a * b * values of both the color of the subject and the color (output color, reproduction value) reproduced under a specific light source match, It has different values under different illumination light sources, and has a problem of light source dependence that cannot reproduce the color of the subject.

  Therefore, as a color matching method for solving the above-mentioned light source-dependent problem and reproducing the color of the subject even under an unspecified light source, a method of spectrally reproducing the color is known (for example, Patent Document 3). reference.). By using a color matching method that spectrally reproduces colors, it is possible to realize a printing system that enables reproduction close to the subject and spectral reflectance.

In general, an LUT is used to construct a printing system. The LUT in color management includes a reference table for converting input color information into a dot area ratio for reproducing by printing. By using the LUT, it is possible to reduce the number of calculations for obtaining the halftone dot area ratio, reduce the processing load on the device that performs the calculation, and prevent a decrease in processing speed.
In order to create this LUT, output signal values of colors such as cyan (C), magenta (M), yellow (Y), black (K), red (R), and green (G) (dot area in printing) And the principal component coefficients obtained by performing principal component analysis on spectral reflectance data obtained from actual measurement of color patches obtained by printing them.

  When the LUT is used in this way, every time the subject changes, it is necessary to acquire spectral reflectance data of the subject and perform principal component analysis on the data. Therefore, the imaging load for acquiring the spectral reflectance by imaging and the calculation load for performing the principal component analysis from the obtained spectral reflectance are large. Furthermore, since the creation of an LUT requires printing and measurement of a large number of color patches in order to obtain print color information, there is a problem that the measurement load related to the acquisition of spectral reflectance data for printing is large. . For example, when the halftone dot area ratios of C, M, Y, K, R, and G are each divided into five, it is necessary to measure color patches of 5 × 5 × 5 × 5 × 5 × 5 = 15625 colors. In addition, when the number of color batches is divided into six to improve accuracy, it is necessary to measure 6 × 6 × 6 × 6 × 6 × 6 = 46656 color patches.

The printing system according to the present embodiment is designed to solve the above problem in consideration of such circumstances, and reduces the load related to the acquisition of spectral reflectance data of the subject and the printing color, and the processing speed. It is possible to provide a printing system that approximates and reproduces the spectral reflectance of an object, which is configured by creating and using a LUT (tone value / halftone dot area ratio conversion table) that can enhance the image quality.
As described above, the gradation value / halftone dot area ratio conversion table according to the present embodiment is a table that associates the gradation value data K _j divided in advance with the halftone dot area ratio Y _i %. That is, the LUT creation apparatus 1000 according to the present embodiment does not perform the optimization process of the dot area ratio Y _i % corresponding to all the gradation values. As a result, the processing load required for creating the gradation value / halftone dot area ratio conversion table can be reduced, and the time required for the creation process can be reduced. However, the gradation value / halftone dot area ratio conversion device 3000 refers to this gradation value / halftone dot area ratio conversion table and determines the halftone dot area ratio Y _i % corresponding to the gradation value not defined in the table. Can be interpolated. Therefore, even if the gradation value / halftone dot area ratio conversion device 3000 receives information indicating a gradation value not defined in the gradation value / halftone dot area ratio conversion table from the image input device 2000, The halftone dot area ratio Y _i % corresponding to the gradation value can be obtained by referring to the gradation value / halftone dot area ratio conversion table and calculating, for example, an interpolation method or polynomial approximation.
The present invention is not limited to this, and the gradation value / halftone dot area ratio conversion table may be a table that associates all gradation values with the halftone dot area ratio Y _i %.

Here, in the color prediction according to the present embodiment, spectral reflectance prediction by a known technique and spectral reflectance calculated by spectral reflectance prediction by the present invention will be described with reference to FIG. Note that the data shown in FIG. 13 is an example of data obtained through experiments.
In this experiment, using the color patch in which the printing unit 19 or the printing unit 1376 prints the primary colors CMY in a checkered pattern with a dot area ratio of 100%, this color patch is measured to obtain the spectral reflectance R _measure (λ). . Further, as described in Steps ST22 to 24 or Steps ST35 to 37 described above, the predicted spectral reflectance R _D (using the spectral optical density prediction unit 15 or the spectral optical density prediction unit 136 which is an optical density-based color prediction model. λ) is calculated.
FIG. 13A shows the relationship between the predicted spectral reflectance R _D (λ) and the spectral reflectance R _measure (λ) obtained by this optical density-based color prediction model.

Further, as described in the above-described steps ST2 to 5 or steps ST39 to ST26, the predicted spectral reflectance R (λ is calculated using the spectral reflectance prediction unit 21 or the spectral reflectance prediction unit 135 which is a reflectance-based color prediction model. ) Is calculated.
FIG. 13B shows the relationship between the predicted spectral reflectance R (λ) and the spectral reflectance R _measure (λ) obtained by this reflectance-based color prediction model.
Further, as described in the above-described steps ST1-5, 22-27, or steps ST35-44, the predicted spectral reflection calculated by the spectral reflectance prediction unit 21 or the spectral reflectance prediction unit 135, which is a reflectance-based color prediction model. The ratio R (λ) and the predicted spectral reflectance R _D (λ) calculated by the spectroscopic optical density prediction unit 15 or the spectroscopic optical density prediction unit 136, which is an optical density-based color prediction model, at a ratio according to the weighting factor. A mixed predicted spectral reflectance R _m (λ) is calculated using a mixed color prediction model to be mixed.
FIG. 13C shows the relationship between the mixed predicted spectral reflectance R _m (λ) and the spectral reflectance R _measure (λ) obtained by this mixed color prediction model.

As shown in FIG. 13A, the predicted spectral reflectance R _D (λ) obtained by the optical density-based color prediction model (optical density model) is compared with the spectral reflectance R _measure (λ). Is generally higher.
On the other hand, as shown in FIG. 13B, the predicted spectral reflectance R (λ) obtained by the reflectance-based color prediction model (reflectance model) is compared with the spectral reflectance R _measure (λ). The rate is lower overall.
Thus, the predicted spectral reflectance R _D (λ) obtained by the optical density-based color prediction model and the predicted spectral reflectance R (λ) obtained by the reflectance-based color prediction model are the spectral reflectance R _measure (λ It can be seen that this is a relationship that takes opposite values.
Note that the relationship between the predicted spectral reflectance R _D (λ) obtained from both models and the predicted spectral reflectance R (λ) is a contradictory relationship obtained in experiments using other color patches. Data were also shown.
Using this relationship, the mixed color prediction model mixes the predicted spectral reflectance R _D (λ) and the predicted spectral reflectance R (λ) obtained from both models at a ratio according to the weighting coefficient w, and mixes them. The predicted spectral reflectance R _m (λ) is calculated. As a result, as shown in FIG. 13C, it is possible to calculate the mixed predicted spectral reflectance R _m (λ) that is close to the actually measured spectral reflectance R _measure (λ).

In addition, this invention is not limited to the said embodiment, The following structures may be sufficient.
For example, in the above-described embodiment, for the sake of simplifying the description, the model of the three primary primary colors has been described. However, the present invention is not limited to this, and for example, five or more colors including four processes and special colors It can also be used in the color matching of the printing unit 19 using a printer or a printing press.
Further, the weight coefficient calculation unit 23 reads the predicted spectral reflectance R _{D, k} (λ) of the secondary color corresponding to the halftone dot area ratio 50%, and calculates the weight coefficient w. Is not limited to this, and may be an estimated spectral reflectance R _{D, k} (λ) obtained from a color patch printed with two or more multi-colors. Also, the dot area ratio may be other than 50%.
Furthermore, the example in which the spectral optical density is calculated based on the spectral reflectance using the natural logarithm as shown in the arithmetic expression of the equation (4) has been described, but the present invention is not limited to this, and for example, 10 is the bottom. The common logarithm may be used.

In the mixed color prediction model illustrated in FIG. 7, the optical density calculation by the optical density calculation unit 13 is the calculation of the predicted spectral reflectance and the predicted spectral optical density by the spectral reflectance prediction unit 21 and the spectral optical density prediction unit 15. The timing is not particularly limited.
In addition, the order in which the spectral reflectance prediction unit 21 calculates the predicted spectral reflectance and the processing in which the spectral optical density prediction unit 15 calculates the predicted spectral reflectance may be arbitrarily determined, and simultaneously in parallel. The order in which the other process is performed after one of the processes is performed first may be used.
In the method for creating the gradation value / halftone dot area ratio conversion table in the printing system described with reference to FIG. 11, the spectral optical density calculation by the spectral optical density calculator 133 is performed by the spectral reflectance predictor 135 and the spectral optical density predictor. The timing may be any timing as long as the predicted spectral reflectance and the predicted spectral optical density can be calculated by the unit 136.
In addition, the order in which the spectral reflectance prediction unit 135 calculates the predicted spectral reflectance and the spectral optical density prediction unit 136 calculates the predicted spectral reflectance may be arbitrarily determined, and simultaneously in parallel. The order in which the other process is performed after one of the processes is performed first may be used.

Further, in the above, the spectral reflectance R _{measure, i} (λ) of the primary color i, the spectral reflectance R _{t, i} (λ) of the primary color i, the spectral reflectance R _s (λ) of the paper, and the solid color of the Neugebauer primary 1 It has been described that the reflected light from the color patch, such as the spectral reflectance R _{t, l} (λ), is measured and the measured spectral reflected light is used. However, the present invention is not limited to this, and the spectral reflectance that approximates the actual measurement value may be calculated using the Kubelka-Munk equation as shown below. In this case, the color prediction device 100 or 200 or the printed product spectral reflectance prediction device 1300 calculates the spectral reflectance that approximates the actual measurement value according to the Kubelka-Munk equation, and the spectral value of the primary color i that is the calculated value. Reflectance _{Rmeasure, i} (λ), spectral reflectance R _{t, i} (λ) of primary color i solid, spectral reflectance R _s (λ) of paper, spectral reflectance R _{t, l} (λ of solid color of Neugebauer) ) Etc., and a structural member (not shown) that is stored in a storage area similar to that described above.

  In the first to third embodiments described above, a program for realizing the functions of the color prediction apparatuses 100 and 200, the LUT creation apparatus 1000, and the printed matter spectral reflectance prediction apparatus 1300 is recorded on a computer-readable recording medium. Then, the program recorded on the recording medium may be read into a computer system and executed. Here, the “computer system” may include an OS and hardware such as peripheral devices. The “computer-readable recording medium” means a flexible disk, a magneto-optical disk, a ROM, a writable nonvolatile memory such as a flash memory, a portable medium such as a CD-ROM, a hard disk built in a computer system, etc. This is a storage device.

  Further, the “computer-readable recording medium” means a volatile memory (for example, DRAM (Dynamic DRAM) in a computer system that becomes a server or a client when a program is transmitted through a network such as the Internet or a communication line such as a telephone line. Random Access Memory)), etc., which hold programs for a certain period of time. The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

  DESCRIPTION OF SYMBOLS 100, 200 ... Color prediction apparatus, 11 ... Input part, 12 ... Spectral reflectance database, 13 ... Optical density calculation part, 14 ... Spectral optical density database, 15 ... Spectral optics Density prediction unit, 16 ... color prediction table creation unit, 17 ... output unit, 21 ... spectral reflectance prediction unit, 22 ... multi-order color spectral reflectance database, 23 ... weight coefficient calculation , 24 ... mixing prediction unit, 1000 ... LUT creation device, 1100 ... input unit, 1200 ... spectral reflectance estimation device, 1300 ... printed spectral reflectance prediction device, 1400 ... Optimization calculation unit, 1500... Gradation value / halftone dot area rate association unit, 1600... LUT database, 2000... Image input device, 3000. 4000 ... Color reproduction Device 131 input unit 132 spectral reflectance database 133 optical density calculation unit 134 spectral optical density database 135 spectral reflectance prediction unit 136 Spectral optical density prediction unit, 137... Printing unit, 138... Measurement unit, 139... Mixing prediction unit, 16... Color prediction table creation unit, 17. Reflectance prediction unit, 22 ... multi-order spectral reflectance database, 23 ... weight coefficient calculation unit, 24 ... mixture prediction unit

Claims (11)

In a color prediction device that calculates a predicted reflectance that represents a reproduced color printed by a printing machine in color matching,
An optical density calculating unit that calculates a printing optical density based on a print reflectance measured from a printed part printed on the printing medium by the printing machine;
An optical density prediction unit that calculates a print optical density of the reproduced color based on the print optical density, and calculates a first predicted reflectance representing the reproduced color based on the print optical density of the reproduced color;
A color prediction apparatus comprising: In a color prediction device that calculates a predicted reflectance that represents a reproduced color printed by a printing machine in color matching,
An optical density calculation unit that calculates a print reflectance of a printed part printed by the printing press on a print medium using a Kubelka-Munk formula, and calculates a print optical density based on the print reflectance;
An optical density prediction unit that calculates a print optical density of the reproduced color based on the print optical density, and calculates a first predicted reflectance representing the reproduced color based on the print optical density of the reproduced color;
A color prediction apparatus comprising: A reflectance predicting unit that calculates a second predicted reflectance representing the reproduced color based on the printed reflectance;
A mixed prediction unit that mixes the first predicted reflectance and the second predicted reflectance to obtain a third predicted reflectance representing the reproduced color;
The color prediction apparatus according to claim 1, further comprising: The first predicted reflectance representing the mixed color calculated by the optical density prediction unit with respect to a multi-order color reflectance measured from a mixed color printed on a printing medium by superimposing a plurality of colors on the printing press. A weighting factor calculating unit that calculates a weighting factor that approximates the third predicted reflectance calculated by mixing the second predicted reflectance that represents the mixed color calculated by the reflectance predicting unit;
The mixed prediction unit
4. The color prediction apparatus according to claim 3, wherein the first predicted reflectance and the second predicted reflectance are mixed at a mixing ratio corresponding to the weighting coefficient calculated by the weighting coefficient calculating unit. .   The color predicting apparatus according to claim 1, wherein the reflectance and the optical density are extended to spectroscopy.   The said color prediction apparatus is connected with the said printing machine, and inputs the said print reflectance measured by the optical element with which the said printing machine is provided, The Claim 1 to 4 characterized by the above-mentioned. Color prediction device. In a color prediction method for calculating a predicted reflectance representing a reproduction color printed by a printing machine in color matching,
Calculating a printing optical density based on a print reflectance measured from a printed part printed on the printing medium by the printing machine;
Calculating a printing optical density of the reproduction color based on the printing optical density, and calculating a first predicted reflectance representing the reproduction color based on the printing optical density of the reproduction color;
A color prediction method comprising: A computer that calculates a predicted reflectance that represents a reproduced color that the printing press prints in color matching;
An optical density calculating means for calculating a printing optical density based on a print reflectance measured from a printed part printed on the printing medium by the printing machine;
Optical density prediction means for calculating a print optical density of the reproduced color based on the print optical density and calculating a first predicted reflectance representing the reproduced color based on the print optical density of the reproduced color;
Program to function as. The color prediction device according to any one of claims 3 to 6;
A reflectance estimator that calculates an estimated reflectance estimated as the reflectance of the subject in real space based on an image including the subject for each predetermined gradation value;
It is determined whether a difference between the third predicted reflectance calculated by the color predicting device and the estimated reflectance satisfies a predetermined convergence condition, and the estimated reflection satisfying the convergence condition An optimization calculator that outputs the rate and the third predicted reflectance as an optimum value;
The gradation value of the estimated reflectance input as an optimum value from the optimization calculator and the halftone dot area ratio of the third predicted reflectance input as an optimum value from the optimization calculator are associated with each other. An associating unit for writing in the halftone dot area ratio table;
A table creating apparatus further comprising: The optimization calculation unit
10. The table according to claim 9, wherein when it is determined that the convergence condition is not satisfied, a halftone dot area ratio indicating the reproduction color set when calculating the third predicted reflectance is corrected. Creation device. The table creation device according to claim 9 or 10, and
An image input device for inputting image data including an image of the subject;
With reference to the gradation value halftone dot area ratio table, a conversion device that outputs a halftone dot area ratio corresponding to the gradation value of each pixel of the image data;
A reproduction color output device for printing an image of the subject based on a halftone dot area ratio input from the conversion device;
A printing system comprising:

Images