COLOR DATA CONVERSION METHOD, COLOR DATA CONVERSION DEVICE, AND COLOR DATA CONVERSION PROGRAM

Abstract

To provide color conversion processing using color chart data such as CxF data which enables determination of presence / absence of essential data and compensation of insufficient data to be automatically performed without requiring operation of a user.SOLUTION: A color data conversion method allows a computer to analyze input data, and thereby specifies insufficient data among data required for executing CxF color conversion processing (step S30). The method allows the computer to compensate spectral characteristic data of a patch constituting a CxF chart, when the spectral characteristic data is insufficient (step S45). The method allows the computer to compensate print order data, when the complete print order data is not obtained (step S55). The method performs color conversion processing using the compensated data (step S60).SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a color data conversion method for converting color data relating to spot colors included in submitted data into color data for a printing device used for printing. [Background technology]

[0002] In recent years, digital printing using digital printing devices such as inkjet printers has become widespread in the printing industry. However, in the field of labels and packaging, analog printing (offset printing, gravure printing, flexography, etc.) using printing devices that use printing plates is still often used in recent years. However, there is an increasing demand for shorter delivery times for design and content production, and when analog printing is adopted, the problem is that when there is a change in design, etc., the cost incurred by remaking printing plates and reverting the process is high. In this regard, digital printing does not use printing plates, so there is no need to replace or remake printing plates. In other words, by adopting digital printing, it is possible to print small lots at low cost, and it is also possible to meet the demand for shorter delivery times for design and content production at low cost.

[0003] In the field of labels and packaging, there is a tendency to use spot colors frequently to enhance color expression. For this reason, in order to print on a digital printing device using print data generated for analog printing, it is necessary to convert the color data related to spot colors in the input data into color data for digital printing devices so that the colors obtained by overprinting multiple ink colors, including spot colors, are reproduced as faithfully as possible on the digital printing device. One such conversion method is a conversion process (hereinafter referred to as "CxF color conversion process") that uses data in a format called CxF (hereinafter referred to as "CxF data"), which is standardized as an ISO standard. CxF is an abbreviation for "Color Exchange Format."

[0004] CxF data is typically composed of spectral characteristic data (spectral value data such as spectral reflectance) of multiple patches constituting a color chart called a "CxF chart" as shown in FIG. 22. In the example shown in FIG. 22, the CxF chart is composed of 22 patches. The 11 patches in the upper row are patches obtained by applying the target ink on a substrate such as printing paper with a dot percentage in increments of 10%. The 11 patches in the lower row are patches obtained by applying the target ink on black (solid black) with a dot percentage in increments of 10%. In this way, the CxF chart contains multiple patches corresponding to multiple levels of ink density. In the CxF color conversion process, the spectral characteristics corresponding to the color obtained by overprinting multiple ink colors including spot colors are predicted based on the spectral characteristic data of these multiple patches, and the CMYK values ​​(combination of C value, M value, Y value, and K value to be given to the digital printing device used for printing) corresponding to the predicted spectral characteristics are obtained. In the CxF color conversion process, the spectral characteristics corresponding to the color obtained by overprinting spot color ink and process color ink are also predicted, so the CxF data may include not only spot color data but also process color data. Note that in the following, it is assumed that the CxF data is composed of the spectral characteristics data of the above 22 patches.

[0005] Regarding CxF color conversion processing, JP2020-017902A discloses a method for accurately predicting the color (spectral characteristics) obtained by overprinting multiple colors of ink, including spot colors. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2020-017902 A Summary of the Invention [Problem to be solved by the invention]

[0007] In order to execute the CxF color conversion process, the spectral characteristic data of the above 22 patches as CxF data and printing order data representing the printing order of multiple colors (ink colors) when analog printing is performed using the data to be processed are required. Hereinafter, these data required to execute the CxF color conversion process are simply referred to as "essential data". In the conventional system, when executing the CxF color conversion process, the user judges whether or not all the essential data is included in the submitted data. Then, when there is missing data (essential data that is not included in the submitted data), the user needs to operate the GUI to supplement the missing data. Therefore, the automation of the entire process including the CxF color conversion process has not been realized. In addition, sufficient knowledge about analog printing is required to supplement the printing order data. Therefore, it is difficult for a user of a digital printing device who does not have sufficient knowledge about analog printing to supplement the printing order data.

[0008] In view of the above circumstances, the present invention relates to color conversion processing using color chart data such as CxF data, and aims to enable the determination of the presence or absence of required data and the supplementation of missing data to be performed automatically without the need for user operation. [Means for solving the problem]

[0009] A first invention is a color data conversion method for converting color data relating to spot colors included in input data into color data for a printing device used for printing, using a computer, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, the method comprising: an input step of providing, as input data, image data of a printing target including color data and spectral characteristic data relating to spot colors used in the image data to the computer; an insufficient patch identifying step in which the computer analyzes the input data to identify, as an insufficient patch, a patch for which spectral characteristic data is not obtained regarding a spot color used in the image data; A spectral characteristic complementation step in which the computer complements the spectral characteristic data of the insufficient patch; a first conversion step in which the computer converts color data related to spot colors and included in the image data into spectral characteristic data based on the color chart data including the spectral characteristic data complemented in the spectral characteristic complement step; a second conversion step in which the computer converts the spectral characteristic data obtained in the first conversion step into color data for the printing device; The present invention is characterized by comprising:

[0010] The second invention is the first invention, The color data conversion method includes: a printing order data confirmation step in which the computer determines whether or not the input data includes printing order data that indicates a printing order of a plurality of ink colors when analog printing is performed based on the image data; a printing order supplementing step in which the computer supplements the printing order data when it is determined in the printing order data confirmation step that the printing order data is not included in the input data; Further comprising: In the first conversion step, the computer converts color data included in the image data into spectral characteristic data, taking into consideration the printing order data complemented in the printing order complementing step.

[0011] The third invention is the second invention, The printing order complementing step includes: a brightness classification step in which the computer classifies the ink colors used in the analog printing into a plurality of brightness levels according to their brightness; a saturation classification step in which, when two or more ink colors are classified into the same lightness level in the lightness classification step, the computer classifies the two or more ink colors into a plurality of saturation levels according to their saturation; Including, In the printing order complementing step, the computer complements the printing order data so that ink colors classified into a lightness level corresponding to a higher lightness are printed earlier than ink colors classified into a lightness level corresponding to a lower lightness, and ink colors classified into a saturation level corresponding to a higher saturation are printed earlier than ink colors classified into a saturation level corresponding to a lower saturation with respect to two or more ink colors classified into the same lightness level.

[0012] The fourth invention relates to the third invention, When two or more ink colors are classified into the same saturation level in the saturation classification step, the computer complements the printing order data so that the printing order of the two or more ink colors is an order based on hue that follows a predetermined rule.

[0013] The fifth invention is any one of the second to fourth inventions, In the submission step, the printing order data is provided to the computer as a first external file separate from a file constituting the image data, In the printing order data confirmation step, the computer determines whether or not the printing order data is included in the submitted data by analyzing the first external file.

[0014] The sixth invention is any one of the first to fifth inventions, In the input step, spectral characteristic data for spot colors used in the image data is provided to the computer as a second external file separate from a file constituting the image data, In the step of identifying the insufficient patch, the computer identifies the insufficient patch by analyzing the second external file.

[0015] The seventh aspect of the present invention is any one of the first to sixth aspects of the present invention, In the spectral characteristic complementing step, the spectral characteristic data of the insufficient patch is obtained from a spectral characteristic database that holds the spectral characteristic data of the plurality of patches for a plurality of spot colors.

[0016] The eighth aspect of the present invention is any one of the first to seventh aspects of the present invention, The spectral characteristic complementing step includes: a reference color selection step of selecting, as a reference color, a color close to the prediction target color from among a plurality of sample colors for which the spectral characteristic data of the plurality of patches is obtained, the special color for which the insufficient patch is identified as a prediction target color; a relational expression calculation step of determining, for the reference color, a relational expression representing a relationship between the spectral characteristic data of the reference patch and the spectral characteristic data of the insufficient patch, using a patch having a maximum ink density among the plurality of patches as a reference patch; a spectral characteristic calculation step of calculating the spectral characteristic data of the insufficient patch for the prediction target color by applying the spectral characteristic data of the reference patch for the prediction target color to the relational expression; The present invention is characterized by comprising:

[0017] A ninth aspect of the present invention relates to the eighth aspect of the present invention, The spectral characteristic complementation step is characterized by further including a minimum density patch data acquisition step of acquiring spectral characteristic data of a patch having a minimum ink density for the prediction target color from a spectral characteristic database that holds the spectral characteristic data of the plurality of patches for a plurality of spot colors.

[0018] A tenth aspect of the present invention is any one of the first to seventh aspects of the present invention, The spectral characteristic complementing step includes: a first relational equation calculation step of determining a spot color for which the insufficient patch is identified as a prediction target color, determining a patch for which spectral characteristics data has been obtained regarding the prediction target color as a characteristic-acquired patch, and calculating a first relational equation that expresses a relationship between the spectral characteristics data of a patch having a maximum ink density and the spectral characteristics data of the characteristic-acquired patch for each of a plurality of sample colors for which spectral characteristics data of the plurality of patches has been obtained; a prediction step of calculating a predicted value of the spectral characteristics data of the patch whose characteristics have been acquired for the prediction target color by applying the spectral characteristics data of the patch having the maximum ink density for the prediction target color to a corresponding first relational expression for each of the plurality of sample colors; a difference value calculation step of calculating a difference value between the predicted value calculated in the prediction step and the value of the spectral characteristic data of the patch whose characteristics have been acquired for the prediction target color for each of the plurality of sample colors; a reference color selection step of selecting, as a reference color, the sample color that has the smallest difference value in the difference value calculation step from among the plurality of sample colors; a second relational equation calculation step of calculating, as second relational equations, a relational equation expressing a relationship between the spectral characteristics data of a patch having a maximum ink density and the spectral characteristics data of an insufficient patch having an ink density greater than that of the characteristic-acquired patch, and a relational equation expressing a relationship between the spectral characteristics data of the characteristic-acquired patch and the spectral characteristics data of an insufficient patch having an ink density less than that of the characteristic-acquired patch; a spectral characteristic calculation step of calculating, for the prediction target color, spectral characteristic data of an insufficient patch having an ink density greater than that of the characteristic-acquired patch by applying the spectral characteristic data of the patch having the maximum ink density to a corresponding second relational expression, and calculating, for the insufficient patch having an ink density less than that of the characteristic-acquired patch by applying the spectral characteristic data of the characteristic-acquired patch to a corresponding second relational expression; The present invention is characterized by comprising:

[0019] The eleventh aspect of the present invention is any one of the first to seventh aspects of the present invention, In the spectral characteristic complementation step, the special color for which the insufficient patch is identified is set as the prediction target color, and spectral characteristic data of the insufficient patch corresponding to an ink density between the ink density of the first patch and the ink density of the third patch is obtained by spline interpolation using spectral characteristic data of a first patch having the highest ink density among three patches for which spectral characteristic data has been obtained for the prediction target color, spectral characteristic data of a third patch having the lowest ink density among the three patches, and spectral characteristic data of a second patch having an ink density lower than that of the first patch and higher than that of the third patch.

[0020] A twelfth aspect of the present invention is any one of the first to seventh aspects of the present invention, In the spectral characteristic complementing step, The spot color for which the insufficient patch is identified is set as a prediction target color, The present invention is characterized in that the spectral characteristic data of the insufficient patch for the prediction target color is obtained by linear interpolation using the spectral characteristic data of the patch with the maximum ink density for the prediction target color and the spectral characteristic data of the patch with the minimum ink density for the prediction target color.

[0021] A thirteenth aspect of the present invention is any one of the first to seventh aspects of the present invention, the plurality of patches include eleven first type patches obtained by applying a spot color ink on a base material in eleven ink densities ranging from a minimum density to a maximum density, and eleven second type patches obtained by applying a spot color ink on a black base material in eleven ink densities ranging from the minimum density to the maximum density, The spectral characteristic complementation step is characterized in that the special color identified as the insufficient patch is set as the prediction target color, the patch identified as the insufficient patch among the 11 second type patches is set as the prediction target patch, and the spectral characteristic data of the reference patch for the prediction target color is provided as input data to a trained neural network that uses as input data the spectral characteristic data of a reference patch which is the patch with the greatest ink density among the 11 first type patches and as output data the spectral characteristic data of the prediction target patch, thereby obtaining the spectral characteristic data of the prediction target patch for the prediction target color.

[0022] A fourteenth aspect of the present invention is a color data conversion device that converts color data relating to spot colors included in input data into color data for a printing device used for printing, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, comprising: an input unit that receives, as input data, image data of a print target including color data and spectral characteristic data related to spot colors used in the image data; an insufficient patch identifying unit that identifies a patch for which spectral characteristic data is not obtained regarding a spot color used in the image data as an insufficient patch by analyzing the input data; a spectral characteristic complementing unit that complements the spectral characteristic data of the insufficient patch; a first conversion unit that converts color data related to spot colors and included in the image data into spectral characteristic data based on the color chart data including the spectral characteristic data complemented by the spectral characteristic complement unit; a second conversion unit that converts the spectral characteristic data obtained by the first conversion unit into color data for the printing device; The present invention is characterized by comprising:

[0023] A fifteenth aspect of the present invention is a color data conversion program for converting color data relating to spot colors included in input data into color data for a printing device used for printing, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, the program comprising: On the computer, an insufficient patch identifying step of identifying a patch for which spectral characteristic data is not obtained for a spot color used in the image data by analyzing the input data, which is composed of image data of a print target including color data and spectral characteristic data for a spot color used in the image data, as an insufficient patch; a spectral characteristic complementation step of complementing the spectral characteristic data of the insufficient patch; a first conversion step of converting color data related to spot colors and included in the image data into spectral characteristic data based on the color chart data including the spectral characteristic data complemented in the spectral characteristic complement step; a second conversion step of converting the spectral characteristic data obtained in the first conversion step into color data for the printing device; Execute the command. Effect of the Invention

[0024] According to the first aspect of the present invention, after submitted data including image data to be printed is provided to a computer, the computer analyzes the submitted data to identify patches for which spectral characteristic data is not available for spot colors used in the image data as insufficient patches. The computer then complements the spectral characteristic data of the insufficient patches. As described above, the presence or absence of spectral characteristic data, which is data required for the process of converting color data related to spot colors included in the submitted data into color data for a printing device, is determined and the missing spectral characteristic data is complemented automatically without requiring user operation. This improves the efficiency of printing process of image data including spot colors by a digital printing device.

[0025] According to the second aspect of the present invention, if the input data does not include printing order data that indicates the printing order of multiple ink colors when analog printing based on image data is performed, the printing order data is supplemented by the computer without the need for user operation. This makes it possible to execute digital printing of image data including spot colors so that even users with little knowledge of analog printing can obtain printed products with good accuracy.

[0026] According to the third invention described above, when printing order data is not included in the submitted data, it is possible to supplement the printing order data so that the colors obtained by overprinting multiple colors of ink, including spot colors, can be reproduced with high accuracy by the digital printing device.

[0027] According to the fourth aspect of the invention, the same effects as those of the third aspect of the invention can be obtained.

[0028] According to the fifth aspect of the present invention, even if the printing order data is submitted as a file separate from the file constituting the image data, the same effect as the first aspect of the present invention can be obtained.

[0029] According to the sixth invention, the same effect as the first invention can be obtained even when the spectral characteristic data for the spot colors used in the image data is submitted as a file separate from the file constituting the image data.

[0030] According to the seventh aspect of the present invention, it is possible to efficiently complement the spectroscopic characteristic data.

[0031] According to the eighth aspect of the present invention, the spectral characteristics data of the insufficient patch for the prediction target color is calculated based on the "relationship between the spectral characteristics data of the reference patch (the patch with the maximum ink density) and the spectral characteristics data of the insufficient patch" for a color close to the prediction target color. Therefore, the spectral characteristics data of the insufficient patch for the prediction target color is calculated with high accuracy. In other words, it is possible to complement the spectral characteristics data so that the color obtained by overprinting multiple ink colors, including spot colors, is reproduced with high accuracy by the digital printing device.

[0032] According to the ninth invention described above, even if the submitted data does not include the spectral characteristic data of a patch with the minimum ink density for the prediction target color, it is possible to complement the spectral characteristic data of other insufficient patches by obtaining the spectral characteristic data of that patch from the spectral characteristic database.

[0033] According to the tenth aspect of the present invention, by utilizing the spectral characteristic data of the patch for which characteristics have been acquired for the prediction target color, it is possible to complement the spectral characteristic data of the insufficient patch for the prediction target color with high accuracy.

[0034] According to the eleventh aspect of the present invention, even if spectral characteristic data for various colors is not stored in a database or the like, it is possible to complement the spectral characteristic data of insufficient patches for the color to be predicted with relatively high accuracy without requiring user operation.

[0035] According to the twelfth invention described above, even if spectral characteristic data for various colors is not stored in a database or the like, it is possible to complement the spectral characteristic data of an insufficient patch for the prediction target color without requiring user operation.

[0036] According to the thirteenth aspect of the present invention, when a trained neural network for predicting spectral characteristic data is prepared, it is possible to efficiently complement the spectral characteristic data of the second type patch.

[0037] According to the fourteenth aspect of the present invention, the same effects as those of the first aspect of the present invention can be obtained.

[0038] According to the fifteenth aspect of the present invention, the same effects as those of the first aspect of the present invention can be obtained. [Brief description of the drawings]

[0039] [Figure 1] FIG. 1 is a diagram for explaining the terms used in this specification regarding the CxF chart. [Diagram 2]1 is a diagram illustrating the overall configuration of a printing system according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a hardware configuration diagram of the print data generating device in the embodiment. [Figure 4] 3 is a block diagram showing a functional configuration of a portion that functions as a color data conversion device among the functional configurations of the print data generating device in the embodiment. FIG. [Diagram 5] 6 is a flowchart showing the procedure of a print data generation process in the embodiment. [Figure 6] 2 is a diagram showing a schematic internal structure of a PDF file as submitted data in the embodiment. FIG. [Figure 7] 10 is a flowchart for illustrating details of a step of identifying missing data in the embodiment. [Figure 8] 10 is a flowchart showing a detailed procedure of a process for complementing print order data in the embodiment. [Figure 9] 10 is a flowchart showing a procedure of a spectral characteristics calculation process by a first spectral characteristics calculation method in the embodiment. [Figure 10] 11 is a diagram for explaining normalization when calculating a relational expression in the embodiment. FIG. [Figure 11] FIG. 11 is a diagram for explaining combination data in the embodiment. [Figure 12] FIG. 11 is a diagram for explaining calculation of a relational expression in the embodiment. [Figure 13] FIG. 2 is a diagram showing an example of one plot in the embodiment. [Figure 14] 13 is a flowchart showing a procedure of a spectral characteristics calculation process by a second spectral characteristics calculation method in the embodiment. [Figure 15] FIG. 11 is a diagram for explaining a second spectral characteristic calculation method in the embodiment. [Figure 16] 13 is a flowchart showing a procedure of a spectral characteristics calculation process by a third spectral characteristics calculation method in the embodiment. [Figure 17] FIG. 11 is a diagram showing an example of the structure of a neural network used in the spectral characteristic calculation process by the fourth spectral characteristic calculation method in the embodiment. [Figure 18] 11 is a diagram for explaining a process during learning using a neural network in the embodiment. FIG. [Figure 19] 13 is a flowchart showing a procedure for classifying the detection status, etc. of an insufficient patch for each spot color in the embodiment. [Figure 20] 10 is a flowchart showing an outline of a procedure for CxF color conversion processing in the embodiment. [Figure 21] FIG. 13 is a diagram (part of a flowchart) for explaining a second modified example of the embodiment. [Figure 22] FIG. 1 is a diagram for explaining a CxF chart. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] <0. Introduction> Before describing the embodiments, the terms used in this specification and basic matters related to the present invention will be described with reference to Fig. 1. Regarding the CxF chart shown in Fig. 1, the upper patch (patches in the row labeled with reference number 51) (patches obtained by applying the target ink on a substrate such as printing paper with a dot percentage in increments of 10%) is referred to as a "first type patch," and the lower patch (patches in the row labeled with reference number 52) of the CxF chart (patches obtained by applying the target ink on black with a dot percentage in increments of 10%) is referred to as a "second type patch." Also, a patch that represents the color of the substrate itself (patch marked with symbol PA1 in FIG. 1) is called a "paper white patch," a patch where the target ink is applied solidly (at maximum ink density) on the substrate (patch marked with symbol PA2 in FIG. 1) is called a "solid patch," a patch where only black ink is applied solidly on the substrate (patch marked with symbol PA3 in FIG. 1) is called a "black solid patch," and a patch where the target ink is applied solidly on black ink on the substrate (patch marked with symbol PA4 in FIG. 1) is called a "double solid patch." Also, patches other than the paper white patch PA1 and solid patch PA2 among the first type patches 51 are called "first type halftone patches," and patches other than the black solid patch PA3 and double solid patch PA4 among the second type patches 52 are called "second type halftone patches."

[0041] In the following embodiment, spectral reflectance data is used as the data of the spectral values ​​constituting the spectral characteristic data. In this regard, the description will be made assuming a case where the spectral reflectance is obtained in 10 nm increments in the wavelength range of 380 nm to 730 nm (i.e., a case where one color is specified by 36 spectral reflectances). However, this is not limited to this, and the following embodiment can also be applied to a case where a number of spectral reflectances are obtained by dividing a wavelength range including the range of 400 nm to 700 nm by a unit wavelength range of an appropriate size.

[0042] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

[0043] <1. Overall configuration of the printing system> 2 is a diagram showing the overall configuration of a printing system according to one embodiment of the present invention. This printing system is made up of a print data generating device 100, a digital printing device 200 such as an inkjet printer, and a plurality of personal computers 300. These components are connected to each other so that they can communicate with each other via a communication line 8. The print data generating device 100 realizes a color data conversion device.

[0044] The print data generating device 100 generates print data by performing various processes on input data such as a PDF file. Hereinafter, a series of processes including RIP processing and color conversion processing executed by the print data generating device 100 will be referred to as the "print data generating process." The color conversion process includes the above-mentioned CxF color conversion process and ICC color conversion processing using an ICC profile. The digital printing device 200 prints based on the print data generated by the print data generating device 100. The personal computer 300 transmits and receives data to and from outside the printing system via, for example, a communication line 8.

[0045] In addition, the printing system may also include a colorimeter that measures colors, a platemaking device that creates printing plates based on data generated by the print data generating device 100, and an analog printing device that performs analog printing using the printing plates created by the platemaking device.

[0046] <2. Configuration of the print data generating device> <2.1 Hardware configuration> 3 is a hardware configuration diagram of the print data generating device 100 in this embodiment. The print data generating device 100 is realized by a computer such as a personal computer, and has a CPU (processor) 11, a ROM 12, a RAM 13, an auxiliary storage device 14, an input operation unit 15 such as a keyboard, a display unit 16, an optical disk drive 17, and a network interface unit 18. The submitted data sent via the communication line 8 is input into the print data generating device 100 via the network interface unit 18. The print data generated by the print data generating device 100 based on the submitted data is sent to the digital printing device 200 via the communication line 8 through the network interface unit 18.

[0047] The auxiliary storage device 14 is provided with a spectral characteristics database 142 that holds CxF data for various colors (ink colors). In addition, a print data generation program 141 for executing a print data generation process is stored in the auxiliary storage device 14. The print data generation program 141 includes a color data conversion program P for executing a color conversion process. The print data generation program 141 is stored in a computer-readable recording medium (non-transient recording medium) such as a CD-ROM or DVD-ROM and provided. That is, the user purchases, for example, an optical disk (CD-ROM, DVD-ROM, etc.) 170 as a recording medium for the print data generation program 141, mounts it in the optical disk drive 17, reads out the print data generation program 141 from the optical disk 170, and installs it in the auxiliary storage device 14. Alternatively, the print data generation program 141 sent via the communication line 8 may be received by the network interface unit 18 and installed in the auxiliary storage device 14.

[0048] In this embodiment, the spectral characteristics database 142 is provided in the auxiliary storage device 14 in the print data generating device 100, but is not limited to this. The spectral characteristics database 142 may be provided in another device in this printing system, or in an external device connected via the communication line 8.

[0049] <2.2 Outline of functional configuration> Fig. 4 is a block diagram showing the functional configuration of a portion that functions as a color data conversion device among the functional configurations of the print data generating device 100. As shown in Fig. 4, the print data generating device 100 (the portion that functions as a color data conversion device) includes, as functional components, an input unit 41, a missing data identifying unit 42, a complementing unit 43, and a data converting unit 44. The missing data identifying unit 42 includes an insufficient patch identifying unit 421 and a printing order data checking unit 422. The complementing unit 43 includes a spectral characteristic complementing unit 431 and a printing order complementing unit 432. The data converting unit 44 includes a first converting unit 441 and a second converting unit 442.

[0050] In this embodiment, the submission unit 41 receives a PDF file (PDF data) as submission data DIN. The submission data DIN may include, in addition to image data to be printed including color data, CxF data (color chart data) regarding colors used in the image data, and printing order data indicating the printing order of a plurality of colors (ink colors) when analog printing based on the image data is performed.

[0051] The insufficient patch identifying unit 421 identifies patches for which spectral characteristic data is not available for spot colors used in image data (patches for which spectral characteristic data is not included in the input data DIN) as insufficient patches by analyzing the input data DIN. The printing order data checking unit 422 checks whether printing order data is included in the input data DIN by analyzing the input data DIN. The result RE of the analysis of the input data DIN by the insufficient patch identifying unit 421 and the printing order data checking unit 422 is provided to the complementing unit 43.

[0052] The spectral characteristic complementing unit 431 complements the spectral characteristic data of the insufficient patch identified by the insufficient patch identifying unit 421. The printing order complementing unit 432 complements the printing order data when the printing order data checking unit 422 determines that the printing order data is not included in the submitted data DIN. Here, the spectral characteristic data complemented by the spectral characteristic complementing unit 431 and the printing order data complemented by the printing order complementing unit 432 are collectively referred to as "complementary data".

[0053] The first conversion unit 441 converts the color data included in the image data constituting the input data DIN into spectral characteristic data based on the CxF data and printing order data including the complementary data DH. This conversion is performed for each combination of color values ​​for multiple colors including at least one spot color. The second conversion unit 442 converts the spectral characteristic data obtained by the processing of the first conversion unit 441 into color data (CMYK value data) DP for the digital printing device 200.

[0054] <3. Print data generation process> 5, a procedure of the print data generation process executed by the print data generating device 100 will be described. After the print data generation process starts, a PDF file including image data to be printed is first provided to the print data generating device 100 as submission data DIN (step S10).

[0055] Next, a process called "interpretation" is performed to analyze the PDF file in order to generate print data for the digital printing device 200 (step S20). For example, the positions of objects (text, line drawings, images, etc.) are identified. Note that the process of step S20 is a conventional process.

[0056] Next, the PDF file is further analyzed to identify missing data (step S30). The "missing data" here refers to the above-mentioned essential data that is not included in the PDF file to be analyzed. In this embodiment, the spectral characteristic data of the 22 patches constituting the CxF chart (see FIG. 1) and the above-mentioned printing order data are essential data, and it is assumed that the PDF file 60 includes printing order data 61 and CxF data 62 as shown in FIG. 6. Therefore, this step S30 includes step S32 of checking whether the printing order data 61 is included in the PDF file 60 or not, and step S34 of identifying a patch for which the PDF file 60 does not include spectral characteristic data for a spot color used in the image data as an insufficient patch, as shown in FIG. 7. The CxF data 62 has already been described in XML, which is one of the markup languages.

[0057] Incidentally, in order to perform the CxF color conversion process with high accuracy in step S60 described later, printing order data that allows the printing order of multiple colors to be understood for all parts in which colors overlap in the image data is required. For convenience, such printing order data is referred to as "complete printing order data" here. In this embodiment, it is assumed that complete printing order data is prepared before the CxF color conversion process is performed (input data DIN including complete printing order data is provided to the print data generating device 100, or complete printing order data is generated by complementing the printing order data by the printing order complementing unit 432). However, when high color prediction accuracy is not required for the CxF color conversion process (when a decrease in color prediction accuracy is allowed), complete printing order data does not need to be prepared, and the CxF color conversion process may be performed using arbitrarily created printing order data.

[0058] After step S30 is completed, it is determined whether or not all of the spectral characteristic data of the 22 patches constituting the CxF chart is included in the PDF file 60 (step S40). If the result of the determination is that all of the spectral characteristic data of the 22 patches is included in the PDF file 60, the process proceeds to step S50; otherwise, the process proceeds to step S45. In step S45, a process of complementing the spectral characteristic data of the insufficient patch is performed. Note that the detailed procedure of step S45 will be described later.

[0059] In step S50, it is determined whether or not the complete printing order data is included in the PDF file 60. If the result of the determination is that the complete printing order data is included in the PDF file 60, the process proceeds to step S60, and if not, the process proceeds to step S55. In step S55, a process of complementing the printing order data is performed. The detailed procedure of step S55 will be described later.

[0060] In step S60, a color conversion process is performed. In this step S60, for each combination of color values ​​for a plurality of colors constituting the image data included in the PDF file 60, color value data based on the PDF data as the input data DIN is converted into color value data (CMYK value data) for the digital printing device 200. As described above, this color conversion process includes a CxF color conversion process and an ICC color conversion process. The CxF color conversion process is performed in a state where missing data is complemented by the complement data DH.

[0061] Next, using the result of the color conversion process in step S60, a process called "rendering" is performed on the image data included in the PDF file 60 (step S70), thereby generating multi-value bitmap data.

[0062] Finally, a process called "screening" is applied to the multi-value bitmap data generated in step S70 (step S80). This generates binary bitmap data as halftone dot data. More specifically, for example, a 1-bit TIFF file is generated for each color of CMYK.

[0063] The print data generation process ends when binary bitmap data is generated for each color in step S80.

[0064] As described above, in this embodiment, the printing data generating device 100 performs a process using a computer to convert color data related to spot colors contained in the submitted data into color data for the printing device (digital printing device 200) to be used for printing.

[0065] In this embodiment, step S10 realizes the submission step, step S32 realizes the printing order data confirmation step, step S34 realizes the insufficient patch identification step, step S45 realizes the spectral characteristic complementation step, and step S55 realizes the printing order complementation step.

[0066] <4. Print order data supplement> The process of step S55 in FIG. 5 (processing to complement printing order data) will be described in detail with reference to the flowchart shown in FIG. 8. However, the procedure shown here is merely an example, and the present invention is not limited to this. Note that the multiple colors (ink colors) used in the image data included in the submitted data DIN (PDF file 60) are referred to here as "used colors." Used colors include not only spot colors but also process colors. The data obtained by arranging the data of the used colors according to the printing order becomes the printing order data.

[0067] First, the colors used are classified into 10 brightness levels based on their brightness, with the maximum brightness being 100 and the minimum brightness being 0 (step S551). Brightness corresponds to the magnitude of the L value in the CIELAB color space. For example, a color used with a brightness of 3 is classified as "brightness level 1," a color used with a brightness of 63 is classified as "brightness level 7," and a color used with a brightness of 95 is classified as "brightness level 10." The colors used (data of the colors used) are then sorted so that the higher the brightness level, the earlier they are printed.

[0068] After step S551 is completed, the first loop of processing is performed for each lightness level, as shown in Fig. 8. However, the first loop of processing need only be performed for lightness levels to which two or more used colors have been assigned by the classification in step S551. In other words, when two or more used colors are classified into the same lightness level in step S551, the first loop of processing is performed for that lightness level.

[0069] In step S552, the colors used are classified into 10 saturation levels based on their saturation, with the maximum saturation being 100 and the minimum saturation being 0. Saturation corresponds to the distance (Δab) from the coordinates (a,b)=(0,0) in the CIELAB color space (the coordinates corresponding to achromatic colors). For example, a color used with a saturation of 8 is classified as "saturation level 1," a color used with a saturation of 35 is classified as "saturation level 4," and a color used with a saturation of 92 is classified as "saturation level 10." The colors used are then sorted so that the higher the saturation level, the earlier it is printed.

[0070] After step S552 is completed, the second loop is performed for each saturation level, as shown in Fig. 8. However, the second loop need only be performed for saturation levels to which two or more used colors have been assigned by the classification in step S552. In other words, when two or more used colors are classified into the same saturation level in step S552, the second loop is performed for that saturation level.

[0071] In step S553, each used color is assigned to one of five hues (Y hue, O hue, M hue, G hue, and C hue). In this step S553, the distances (Δab) of the above five hues from the origin in the CIELAB color space are calculated for the used color, and the used color is assigned to the hue with the smallest distance (Δab). The coordinates of the origin of the Y hue are (a,b)=(100,0), the coordinates of the origin of the O hue are (a,b)=(75,75), the coordinates of the origin of the M hue are (a,b)=(0,100), the coordinates of the origin of the G hue are (a,b)=(0,-100), and the coordinates of the origin of the C hue are (a,b)=(-100,0).

[0072] In step S554, the colors to be used are sorted according to a predetermined rule based on the result of the process in step S553. For example, in the case of flexographic printing, the colors to be used are sorted so that the printing order is "Y, O, M, G, C."

[0073] After step S554 is completed, a third loop of processing is performed for each hue, as shown in Fig. 8. However, the third loop of processing need only be performed for hues to which two or more usable colors have been assigned by the processing in step S553.

[0074] In step S555, the colors are sorted based on the distance between each color and the origin of the hue. Specifically, the colors are sorted so that the smaller the distance (Δab) found in step S553, the earlier the colors are printed.

[0075] Furthermore, if there are two or more used colors whose printing order cannot be determined by the processing of step S555, the used colors are sorted based on the distance between each used color and the origin of the hue that is to be printed first among the two adjacent hues (step S556). For example, assuming flexographic printing, if the printing order of two used colors assigned to O hue has not been determined, the two hues adjacent to O hue are Y hue and M hue, and the printing order is "Y, O, M, G, C" as described above, so the distance from the origin of Y hue is found for each used color. The used colors are then sorted so that the shorter the distance, the earlier the printing order.

[0076] As described above, data indicating the order of colors to be used at the time when the first to third loop processes are all completed is used as printing order data in the CxF color conversion process.

[0077] Focusing on the processing of steps S551 and S552, the printing order data is complemented so that a used color classified into a lightness level corresponding to a higher lightness is printed before a used color (ink color) classified into a lightness level corresponding to a lower lightness, and a used color classified into a saturation level corresponding to a higher saturation is printed before a used color classified into a saturation level corresponding to a lower saturation with respect to two or more used colors classified into the same lightness level. Also, when two or more used colors are classified into the same saturation level in step S552, the printing order data is complemented so that the printing order of the two or more used colors is an order according to a predetermined rule based on the hue.

[0078] In this embodiment, step S551 realizes a lightness classification step, and step S552 realizes a saturation classification step.

[0079] <5. How to obtain spectral characteristic data> Before describing the detailed procedure of step S45 in FIG. 5, a number of specific methods for the process of obtaining the spectral characteristic data of an insufficient patch (spectral characteristic calculation process) will be described. Note that the procedure for determining which method to actually adopt from among the multiple specific methods will be described later. In addition, the spectral characteristic data of an insufficient patch may be obtained using a method other than the multiple specific methods described here. In other words, the present invention is not particularly limited in terms of the specific method of obtaining the spectral characteristic data of an insufficient patch. Hereinafter, the special color (special color for which an insufficient patch is identified) that is the processing target for obtaining the spectral characteristic data is referred to as the "prediction target color," and the patch (insufficient patch) that is the processing target for obtaining the spectral characteristic data is referred to as the "prediction target patch."

[0080] <5.1 First method for calculating spectral characteristics> First, a method that uses the "relationship between the spectral reflectance of solid patch PA2 and the spectral reflectance of the prediction target patch" for a color similar to the prediction target color will be described as a first spectral characteristic calculation method. Note that here, an example will be given in which the prediction target patch is a first type halftone patch. However, this first spectral characteristic calculation method can also be applied when the prediction target patch is a second type halftone patch.

[0081] Fig. 9 is a flowchart showing the procedure of the spectral characteristic calculation process by the first spectral characteristic calculation method. Note that before this spectral characteristic calculation process is executed, the spectral characteristic data of all the first type patches 51 for an appropriate number of colors (hereinafter referred to as "sample colors") must be stored in the spectral characteristic database 142. Each sample color may be a spot color or a process color. In addition, the spectral characteristic data of the solid patch PA2 for the prediction target color must be included in the submitted data. The flow shown in Fig. 9 will be described below.

[0082] First, a color close to the prediction target color is selected as a reference color from among a plurality of sample colors based on the spectral characteristic data (step S210). For example, if 32 sample colors are prepared, one color close to the prediction target color is selected as a reference color from among the 32 sample colors.

[0083] Next, for the reference color (sample color selected in step S210), a relational equation is obtained that expresses the relationship between the spectral reflectance of the solid patch PA2 as the reference patch and the spectral reflectance of the prediction target patch (step S220). This relational equation is obtained for each prediction target patch. If the number of prediction target patches is nine, nine relational equations are obtained by the process of step S220.

[0084] Finally, the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color is applied to the relational equations obtained in step S220 to obtain the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch for the prediction target color (step S230). If the number of prediction target patches is nine, the spectral characteristic data of the solid patch PA2 for the prediction target color is applied to nine relational equations. As a result, the spectral characteristic data of each of the nine prediction target patches for the prediction target color is obtained.

[0085] Note that step S210 realizes a reference color selection step, step S220 realizes a relational expression calculation step, and step S230 realizes a spectral characteristic calculation step.

[0086] <5.1.1 Selection of reference color> An example of the process of selecting a reference color (the process of step S210 in FIG. 9) will be described. In this embodiment, the spectral characteristic data of each patch constituting the CxF chart is composed of 36 pieces of spectral reflectance data at 10 nm intervals in the wavelength range of 380 nm to 730 nm. Therefore, regarding the prediction target color and the sample color, the spectral characteristic data of the solid patch PA2 is composed of 36 pieces of spectral reflectance data at 10 nm intervals in the wavelength range of 380 nm to 730 nm. Therefore, based on the 36 pieces of spectral reflectance data of the prediction target color and each sample color, the square error of the spectral reflectance of the solid patch PA2 between the prediction target color and each sample color is calculated. Then, the sample color with the smallest square error is selected as the reference color.

[0087] <5.1.2 Calculation of the relational expression> Next, the process of finding the relational expression (the process of step S220 in FIG. 9) will be described in detail. For the reference color, the spectral characteristic data of all the first-type patches 51 including the paper white patch PA1 and the solid patch PA2 is obtained. That is, data corresponding to the curve (curve representing the spectral reflectance) shown in part A of FIG. 10 is obtained for all the first-type patches 51 (in FIG. 10, the horizontal axis is the wavelength (unit: nm) and the vertical axis is the spectral reflectance). Note that part A of FIG. 10 shows only the curves corresponding to four patches among the first-type patches 51 (the same applies to part B of FIG. 10). The curve marked with the symbol 53 is the curve for the paper white patch PA1, and the curve marked with the symbol 54 is the curve for the solid patch PA2. Normalization is performed on such data by setting the spectral reflectance of the paper white patch PA1 to 1. As a result, data corresponding to the curve (curve representing the spectral reflectance) shown in part B of FIG. 10 (however, the paper white patch PA1, which was the standard for normalization, is a straight line) is obtained.

[0088] Here, one patch (hereinafter referred to as a "target patch") is focused on as a prediction target patch. With respect to the graph shown in part B of FIG. 10, it is assumed that the curves for the solid patch PA2 and the target patch are as shown in FIG. 11 in the vicinity of a wavelength of 480 nm. In this case, the spectral reflectance of the solid patch PA2 is 0.15, and the spectral reflectance of the target patch is 0.52. In this embodiment, such data combining the spectral reflectance of the solid patch PA2 and the spectral reflectance of the target patch is treated as "combined data". As described above, the spectral characteristic data is composed of 36 pieces of spectral reflectance data, so that 36 pieces of combined data of the spectral reflectance of the solid patch PA2 (spectral reflectance after normalization) and the spectral reflectance of the target patch (spectral reflectance after normalization) are obtained. Each combined data is represented as one plot on a graph (hereinafter referred to as a "relationship graph" for convenience) in which the horizontal axis represents the spectral reflectance of the solid patch PA2 and the vertical axis represents the spectral reflectance of the target patch, as shown in FIG. 12. For example, the combination data based on the data shown in Fig. 11 is represented on the relationship graph as the plot indicated by reference numeral 56 in Fig. 13. Thus, in this embodiment, 36 plots are represented on the relationship graph. Calculation of the relationship equation corresponds to finding a curve (for example, the curve indicated by reference numeral 55 in Fig. 12) that passes through positions as close as possible to the positions of these 36 plots.

[0089] In the example shown in part B of FIG. 10, the spectral reflectance is at its minimum value near a wavelength of 560 nm, and the same spectral reflectance appears at wavelengths greater than 560 nm and smaller than 560 nm. Therefore, if the wavelengths are plotted on the relationship graph in order from the highest wavelength, the locus will turn around. However, as can be seen from FIG. 12, the relationship between the spectral reflectance of the solid patch PA2 and the spectral reflectance of the target patch (prediction target patch) does not change before and after the turnaround. From the above, it is considered that the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch for the prediction target color can be accurately obtained from the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color by utilizing the "relationship between the spectral reflectance of the solid patch PA and the spectral reflectance of the prediction target patch" for the reference color.

[0090] In view of the above, in step S220 of FIG. 9, a relational equation (an approximation equation for obtaining an approximate value of the spectral reflectance of the target patch from the spectral reflectance of the solid patch PA2) expressing the relationship between the spectral reflectance of the solid patch PA2 and the spectral reflectance of the target patch is obtained based on the 36 combination data as described above. The relational equation is obtained by a known method. For example, the relational equation can be obtained by solving simultaneous equations obtained from the 36 combination data by the Gaussian elimination method or the Gauss-Jordan method. In this manner, a relational equation corresponding to each prediction target patch is obtained.

[0091] Incidentally, as the relational expression, for example, a quintic expression is adopted. As an example, a quintic expression such as the following expression (1) is adopted as the relational expression. In addition, in the following expression (1), y is the spectral reflectance of the prediction target patch, and x is the spectral reflectance of the solid patch PA2.

number

[0092] <5.1.3 Calculation of spectral reflectance> Next, the process of calculating the spectral reflectance (the process of step S230 in FIG. 9) will be described in detail. At the start of the process of step S230, a quintic equation such as the above equation (1) is obtained as a relational expression for each prediction target patch. As described above, the spectral characteristic data is composed of 36 pieces of spectral reflectance data. Therefore, in step S230, 36 pieces of spectral reflectance data that are the spectral characteristic data of the solid patch PA2 for the prediction target color are substituted one by one into the corresponding relational expression (a relational expression that represents the relationship between the spectral reflectance of the solid patch PA2 and the spectral reflectance of the corresponding prediction target patch) for each prediction target patch, thereby obtaining 36 pieces of spectral reflectance data that are the spectral characteristic data of the corresponding prediction target patch for the prediction target color.

[0093] In this embodiment, as described above, when calculating the relational equation, normalization is performed so that the spectral reflectance of the paper-white patch PA1 is 1. Therefore, the 36 spectral reflectances obtained from the relational equation are subjected to inverse normalization (processing for returning normalized data to unnormalized data) based on the actual spectral reflectance of the paper-white patch PA1.

[0094] <5.2 Second method for calculating spectral characteristics> Next, a method for calculating the spectral characteristics data of the prediction target patch by a relational expression similar to the above-mentioned first spectral characteristics calculation method using the spectral characteristics data of the first type halftone patch when the spectral characteristics data of at least one first type halftone patch in addition to the paper white patch PA1 and the solid patch PA2 is obtained for the prediction target color will be described as a second spectral characteristics calculation method. Here again, the case where the prediction target patch is a first type halftone patch is taken as an example, but this second spectral characteristics calculation method can also be applied when the prediction target patch is a second type halftone patch. In the following, a halftone patch for which spectral characteristics data is obtained for the prediction target color is referred to as a "patch with characteristics acquired."

[0095] 14 is a flowchart showing the procedure of the spectral characteristic calculation process by the second spectral characteristic calculation method. As with the first spectral characteristic calculation method, before this spectral characteristic calculation process is executed, the spectral characteristic data of the sample colors must be stored in the spectral characteristic database 142. In addition, the spectral characteristic data of the paper white patch PA1, the solid patch PA2, and at least one first type halftone patch for the prediction target color must be included in the input data DIN.

[0096] First, for each of the multiple sample colors, a relational equation (hereinafter referred to as a "first relational equation") expressing the relationship between the spectral reflectance of the solid patch PA2 and the spectral reflectance of the patch whose characteristics have been acquired is found (step S310). Note that this first relational equation is found in a similar procedure to that used to find the relational equation in the first spectral characteristic calculation method described above. In step S310, for each sample color, first relational equations are found in a number equal to the number of patches whose characteristics have been acquired. If the number of patches whose characteristics have been acquired is two, two first relational equations are found for each sample color.

[0097] Next, the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color is applied to the first relational expression for each of the sample colors to obtain predicted values ​​of the spectral characteristic data (36 pieces of spectral reflectance data) of the characteristic-acquired patch for the prediction target color (step S320). If 32 colors are prepared as sample colors, 32 predicted values ​​(each predicted value is composed of 36 pieces of spectral reflectance data) are obtained for each characteristic-acquired patch in step S320.

[0098] Then, for each of the sample colors, a difference between the predicted value obtained in step S320 and the actual measurement value (obtained from the input data) of the spectral characteristic data of the patch whose characteristics have been acquired for the prediction target color is obtained (step S330). In this embodiment, the square error between the predicted value obtained in step S320 and the actual measurement value of the spectral characteristic data of the patch whose characteristics have been acquired for the prediction target color is obtained as the difference value.

[0099] Incidentally, when the number of characteristic-acquired patches is one, only one squared error is obtained for each sample color, and the squared error can be used as the difference value. On the other hand, when the number of characteristic-acquired patches is two or more, a squared error is obtained for each characteristic-acquired patch for each sample color. Then, for example, the average value of these squared errors is used as the difference value. If the number of characteristic-acquired patches is three, three squared errors are obtained, and the average value of the three squared errors is used as the difference value. Note that instead of a simple average value of the squared errors, a weighted average value of the squared errors can also be used as the difference value.

[0100] After step S330 is completed, the sample color for which the minimum difference value is obtained in step S330 is selected as the reference color (step S340). That is, the sample color for which the spectral characteristic data of the characteristic-acquired patch for the prediction target color can be predicted most accurately using the first relational expression is selected as the reference color.

[0101] Next, a second relational expression is calculated to obtain 36 pieces of spectral reflectance data that are the spectral characteristic data of the prediction target patch for the prediction target color (step S350). Here, it is assumed that one patch with a dot percentage of 50% is the patch whose characteristics have been acquired. When obtaining the relational expression in the first spectral characteristic calculation method, normalization was performed by setting the spectral reflectance of the paper white patch PA1 to 1. That is, the relational expression in the first spectral characteristic calculation method was obtained by using the spectral reflectance of the solid patch PA2 as a first reference and the spectral reflectance of the paper white patch PA1 as a second reference. In contrast, in the second spectral characteristic calculation method, the spectral reflectance of the patch whose characteristics have been acquired is included in the reference when obtaining the second relational expression.

[0102] In this example, the second relational expressions corresponding to the four prediction target patches with a dot percentage of 60% or more and less than 90% have different criteria for creating the expressions, and the second relational expressions corresponding to the four prediction target patches with a dot percentage of 10% or more and less than 40% have different criteria for creating the expressions. Specifically, the second relational expressions corresponding to the four prediction target patches with a dot percentage of 10% or more and less than 40% are obtained by using the spectral reflectance of the characteristic acquired patch (dot percentage: 50%) as a first criterion and the spectral reflectance of the paper white patch PA1 as a second criterion (see part A of FIG. 15). Also, the second relational expressions corresponding to the four prediction target patches with a dot percentage of 60% or more and less than 90% are obtained by using the spectral reflectance of the solid patch PA2 as a first criterion and the spectral reflectance of the characteristic acquired patch (dot percentage: 50%) as a second criterion (see part B of FIG. 15). With reference to FIG. 15, the lines (curved or straight) labeled G(z) (z is a value between 0 and 100 in increments of 10) represent the second relation corresponding to patches with a dot percentage of z %.

[0103] The second relational equation is obtained by the same procedure as the relational equation in the first spectral characteristic calculation method after the criteria are determined as described above. That is, in step S350, for the reference color, a relational equation expressing the relationship between the spectral characteristic data of the solid patch PA2, which is the patch with the maximum ink density, and the spectral characteristic data of an insufficient patch, whose ink density is greater than that of the characteristic-acquired patch, and a relational equation expressing the relationship between the spectral characteristic data of the characteristic-acquired patch and the spectral characteristic data of an insufficient patch, whose ink density is less than that of the characteristic-acquired patch, are obtained as the second relational equation.

[0104] Finally, the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch for the prediction target color is obtained using the second relational expression (step S360). In the above example, the spectral characteristic data (36 pieces of spectral reflectance data) of the four prediction target patches with a dot percentage of 10% or more and less than 40% are obtained by applying the spectral characteristic data (36 pieces of spectral reflectance data) of the patch whose characteristics have been acquired for the prediction target color to the corresponding second relational expression, and the spectral characteristic data (36 pieces of spectral reflectance data) of the four prediction target patches with a dot percentage of 60% or more and less than 90% are obtained by applying the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color to the corresponding second relational expression. Thus, in step S360, for the color to be predicted, the spectral characteristic data of an insufficient patch whose ink density is greater than that of the patch whose characteristics have been acquired is obtained by applying the spectral characteristic data of solid patch PA2, which is the patch with the greatest ink density, to the corresponding second relational equation, and the spectral characteristic data of an insufficient patch whose ink density is less than that of the patch whose characteristics have been acquired is obtained by applying the spectral characteristic data of the patch whose characteristics have been acquired to the corresponding second relational equation.

[0105] Note that step S310 realizes a first relational equation calculation step, step S320 realizes a prediction step, step S330 realizes a difference value calculation step, step S340 realizes a reference color selection step, step S350 realizes a second relational equation calculation step, and step S360 realizes a spectral characteristic calculation step.

[0106] <5.3 Third method for calculating spectral characteristics> Next, a method for calculating the spectral characteristic data of the second type patch 52 by using the spectral characteristic data of the solid patch PA2 will be described as a third spectral characteristic calculation method.

[0107] 16 is a flowchart showing the procedure of the spectral characteristic calculation process by the third spectral characteristic calculation method. First, the spectral characteristic data of the black solid patch PA3 on the target base material is acquired from the spectral characteristic data of any of the sample colors stored in the spectral characteristic database 142 (step S410).

[0108] Next, in the same manner as in step S210 (see FIG. 9) in the first spectral characteristic calculation method, a color close to the prediction target color is selected as a reference color from among the multiple sample colors (step S420).

[0109] Next, for the reference color (the sample color selected in step S420), a relational expression expressing the relationship between the spectral reflectance of the solid patch PA2 and the spectral reflectance of the double solid patch PA4 is calculated (step S430). This relational expression is calculated in the same manner as the relational expression in the first spectral characteristic calculation method.

[0110] Next, the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color is applied to the relational equation obtained in step S430 to obtain the spectral characteristic data (36 pieces of spectral reflectance data) of the double solid patch PA4 for the prediction target color (step S440).

[0111] Finally, using the spectral characteristic data of the black solid patch PA3 acquired in step S410 and the spectral characteristic data of the double solid patch PA4 obtained in step S440, the spectral characteristic data of a second type of halftone patch for the prediction target color (36 spectral reflectance data) is calculated using a procedure similar to the first spectral characteristic calculation method described above (step S450).

[0112] <5.4 Fourth method for calculating spectral characteristics> Next, a method of obtaining the spectral characteristic data of the second type patch 52 using machine learning will be described as a fourth spectral characteristic calculation method. In this method, a color prediction model is constructed to obtain the spectral characteristic data of the prediction target patch from the spectral characteristic data of the solid patch PA2, with the second type patch 52 as the prediction target patch. Then, the color prediction model is used to obtain the spectral characteristic data of the prediction target patch for the prediction target color.

[0113] The color prediction model is realized by a neural network that performs machine learning. The processing related to the color prediction model is roughly divided into processing in a learning stage and processing in a prediction (inference) stage. In the learning stage, teacher data (training data) is provided to the neural network, and machine learning using the teacher data is performed in the neural network. Spectral reflectance data is provided to the neural network as teacher data. Here, one piece of teacher data is composed of 36 pieces of spectral reflectance data that are the spectral characteristic data of the solid patch PA and 36 pieces of spectral reflectance data that are the spectral characteristic data of one second type patch 52. In the prediction stage, the trained neural network is provided with the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color. As a result, the neural network outputs the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch for the prediction target color.

[0114] FIG. 17 is a diagram showing an example of the structure of a neural network 63 used in this embodiment. This neural network 63 is composed of an input layer, a hidden layer (intermediate layer), and an output layer. The input layer is composed of 36 units (neurons) that receive 36 spectral reflectances 65(1) to 65(36). The hidden layer is also composed of 36 units. However, the number of units in the hidden layer is not limited to 36. In addition, although the number of layers in the hidden layer is one in the example shown in FIG. 17, the number of layers in the hidden layer may be two or more. The output layer is composed of 36 units that output 36 spectral reflectances 66(1) to 66(36).

[0115] The connections between the input layer and hidden layer and between the hidden layer and output layer are full connections. The activation functions of the hidden layer and the output layer are sigmoid functions. However, functions other than the sigmoid function may be used as the activation functions.

[0116] During learning using this neural network 63, the spectral reflectances 65(1) to 65(36) are provided to the input layer. As a result, forward propagation processing is performed within the neural network 63, and the sum of squared errors between the spectral reflectances 66(1) to 66(36) output from the output layer and the spectral reflectances 67(1) to 67(36) that are the correct answer data is calculated (see FIG. 18). Then, the parameters (weighting coefficients, bias) of the neural network 63 are updated by using a gradient descent method based on the results obtained by the backpropagation processing of the errors. By repeating the learning in the above manner, the above parameters are optimized. As for the learning method, batch learning in which all the training data is provided to the neural network 63 together may be adopted, mini-batch learning in which the training data is divided into a plurality of groups and training data is provided to the neural network 63 for each group may be adopted, or online learning in which training data is provided to the neural network 63 one by one may be adopted.

[0117] When making a prediction (inference) using this neural network 63, the spectral reflectances 65(1) to 65(36) of the solid patch PA2 for the prediction target color are provided to the input layer. Then, forward propagation processing is performed within the neural network 63, and the spectral reflectances 66(1) to 66(36) are output from the output layer. These 36 spectral reflectances 66(1) to 66(36) are treated as the spectral characteristic data of the prediction target patch for the prediction target color.

[0118] 17 is prepared for each patch to be predicted. When the spectral characteristic data of all the second type patches 52 are obtained by this method, eleven neural networks 63 are prepared. Then, learning and prediction (inference) are performed for each patch to be predicted using the corresponding neural network 63.

[0119] When this method is adopted, the above-mentioned color prediction model must be constructed before operation begins. During operation, the neural network 63 that realizes the color prediction model is given the spectral characteristic data (36 pieces of spectral reflectance data) of the solid patch PA2 for the prediction target color. This allows the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch for the prediction target color to be obtained.

[0120] <5.5 Fifth spectral characteristic calculation method> Next, a method using spline interpolation will be described as a fifth spectral characteristic calculation method. This fifth spectral characteristic calculation method is applicable when the prediction target patch is a first type patch 51, and for the prediction target color, the input data must include spectral characteristic data of a paper white patch P1, a solid patch PA2, and at least one first type halftone patch.

[0121] In this method, spline interpolation (e.g., cubic spline interpolation) is performed based on the spectral characteristic data (36 pieces of spectral reflectance data) of the paper white patch P1, the solid patch PA2, and one first type halftone patch for the prediction target color. This allows the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch (first type halftone patch) for the prediction target color to be obtained. In this way, even if spectral characteristic data for various colors is not stored, it is possible to relatively accurately complement the spectral characteristic data of the prediction target patch for the prediction target color without requiring user operation.

[0122] Note that spline interpolation can also be performed based on the spectral characteristic data of three patches other than the combination of "paper white patch P1, solid patch PA2, and one first type halftone patch." That is, for any three patches for which spectral characteristic data is obtained for the prediction target color, when the patch with the highest ink density is defined as the "first patch," the patch with the lowest ink density is defined as the "third patch," and the patch with an ink density lower than the first patch and higher than the third patch is defined as the "second patch," it is also possible to obtain the spectral characteristic data of a patch (insufficient patch) corresponding to an ink density between the ink density of the first patch and the ink density of the third patch by performing spline interpolation using the spectral characteristic data of the first patch, the spectral characteristic data of the second patch, and the spectral characteristic data of the third patch.

[0123] <5.6 Sixth spectral characteristic calculation method> Next, a method using linear interpolation will be described as a sixth spectral characteristic calculation method. This sixth spectral characteristic calculation method is applicable when the patch to be predicted is the first type patch 51, and the input data must include the spectral characteristic data of the paper white patch P1 and the solid patch PA2 for the color to be predicted. If this method is adopted, there is a risk that the color obtained by overprinting multiple ink colors, including spot colors, will not be reproduced with sufficient accuracy by the digital printing device 200.

[0124] In this method, linear interpolation is performed based on the spectral characteristic data (36 pieces of spectral reflectance data) of the paper white patch P1 and the solid patch PA2 for the prediction target color. This allows the spectral characteristic data (36 pieces of spectral reflectance data) of the prediction target patch (first type halftone patch) for the prediction target color to be obtained. As with the fifth spectral characteristic calculation method, even if the spectral characteristic data for various colors is not stored, it is possible to complement the spectral characteristic data of the prediction target patch for the prediction target color without requiring user operation.

[0125] <6. Complementing Spectral Characteristics Data> Next, the step of complementing the spectral characteristic data (step S45 in FIG. 5) will be described in detail. In this step, first, the detection status of insufficient patches for each spot color is classified to determine a method for complementing the spectral characteristic data for each spot color. Then, the spectral characteristic data for each spot color is complemented by a method according to the classification result.

[0126] <6.1 Classification based on the detection status of insufficient patches> In this embodiment, in order to determine a method for complementing the spectral characteristic data for each spot color, the detection status of an insufficient patch for each spot color is classified into six cases (cases 1 to 6). The classification procedure will be described below with reference to the flowchart shown in Fig. 19. For convenience, the situation currently being focused on as the detection status of an insufficient patch is referred to as the "focused situation".

[0127] First, it is determined whether or not the spectral characteristic data of all the first type patches 51 have been obtained (step S451). As a result, if the spectral characteristic data of all the first type patches 51 have been obtained, the situation of interest is classified as the first case, and if not, the process proceeds to step S452.

[0128] In step S452, it is determined whether the spectral characteristic data of the solid patch PA2 has been obtained. If the spectral characteristic data of the solid patch PA2 has been obtained, the process proceeds to step S453. If not, it is determined that the CxF color conversion process cannot be performed on the color data including the corresponding spot color.

[0129] In step S453, it is determined whether or not spectral characteristic data of the sample color exists in the spectral characteristic database 142. As a result, if the spectral characteristic data of the sample color exists, the process proceeds to step S454, and if not, the process proceeds to step S456. Note that even if the spectral characteristic data of the sample color exists in the spectral characteristic database 142, the process may proceed to step S456 if there is no sample color having characteristics similar to the color to be processed.

[0130] In step S454, it is determined whether or not the spectral characteristic data of the paper-white patch PA1 has been obtained. As a result, if the spectral characteristic data of the paper-white patch PA1 has been obtained, the process proceeds to step S455, and if not, the situation of interest is classified as the fourth case.

[0131] In step S455, it is determined whether or not the spectral characteristic data of at least one halftone patch of the first type has been obtained. As a result, if the spectral characteristic data of at least one halftone patch of the first type has been obtained, the situation of interest is classified into the second case, and if not, the situation of interest is classified into the third case.

[0132] In step S456, it is determined whether the spectral characteristic data of the paper-white patch PA1 has been obtained. If the spectral characteristic data of the paper-white patch PA1 has been obtained, the process proceeds to step S457. If not, it is determined that the CxF color conversion process cannot be performed on the color data including the corresponding spot color.

[0133] In step S457, it is determined whether or not the spectral characteristic data of at least one halftone patch of the first type has been obtained. As a result, if the spectral characteristic data of at least one halftone patch of the first type has been obtained, the situation of interest is classified into the fifth case, and if not, the situation of interest is classified into the sixth case.

[0134] <6.2 Complementary methods for spectral characteristics data for each case> Next, a method for complementing the spectral characteristic data for each of the above-mentioned first to sixth cases will be described.

[0135] <6.2.1 First Case> In the first case, the spectral characteristic data for the second type patch 52 is obtained by the third spectral characteristic calculation method or the fourth spectral characteristic calculation method.

[0136] <6.2.2 Second Case> In the second case, the spectral characteristic data for the first type halftone patches excluding the characteristic-acquired patches is calculated by the second spectral characteristic calculation method, and the spectral characteristic data for the second type patch 52 is calculated by the third or fourth spectral characteristic calculation method.

[0137] <6.2.3 Case 3> In the third case, the spectral characteristic data for the first type halftone patch is calculated by the first spectral characteristic calculation method, and the spectral characteristic data for the second type patch 52 is calculated by the third or fourth spectral characteristic calculation method.

[0138] <6.2.4 Fourth Case> In the fourth case, first, spectral characteristic data of the same substrate as the substrate used for printing (i.e., spectral characteristic data of the paper white patch PA1) is obtained from the spectral characteristic database 142. Then, the spectral characteristic data of the first type halftone patch is calculated by the first spectral characteristic calculation method. Moreover, the spectral characteristic data of the second type patch 52 is calculated by the third spectral characteristic calculation method or the fourth spectral characteristic calculation method. Note that if the spectral characteristic data of the same substrate as the substrate used for printing does not exist in the spectral characteristic database 142, it is also possible to obtain the spectral characteristic data of a substrate having characteristics similar to the substrate used for printing from the spectral characteristic database 142, although the accuracy will decrease.

[0139] In this embodiment, the minimum density patch data acquisition step is realized by the operation of acquiring the spectral characteristic data of the paper-white patch PA1 from the spectral characteristic database 142 as described above.

[0140] <6.2.5 Fifth Case> In the fifth case, the spectral characteristic data for the first type halftone patches excluding the patches whose characteristics have been acquired is calculated by the fifth spectral characteristic calculation method. Also, the spectral characteristic data for the second type patches 52 is calculated by the fourth spectral characteristic calculation method. If the fourth spectral characteristic calculation method cannot be adopted, sufficient color prediction accuracy cannot be obtained, but dummy value data can be used as the spectral characteristic data for the second type patches 52.

[0141] <6.2.6 Case 6> In the sixth case, the spectral characteristic data for the first type halftone patch is calculated by the sixth spectral characteristic calculation method. The spectral characteristic data for the second type patch 52 is calculated by the fourth spectral characteristic calculation method. If the fourth spectral characteristic calculation method cannot be adopted, sufficient color prediction accuracy cannot be obtained, but dummy value data can be used as the spectral characteristic data for the second type patch 52.

[0142] <7. Color conversion processing> The color conversion process in step S60 in FIG. 5 that is relevant to the present invention will be described below.

[0143] <7.1 About the distinction between CxF color conversion processing and ICC color conversion processing> As described above, the color conversion process includes the CxF color conversion process and the ICC color conversion process. Here, we will explain which of the CxF color conversion process and the ICC color conversion process is performed on the pixel data constituting the image data included in the submitted data DIN.

[0144] For pixel data consisting only of process color value data, an ICC color conversion process is performed regardless of whether CxF data and complete printing order data for all process colors used is available.

[0145] For pixel data that is composed of color value data for process colors and color value data for spot colors, if CxF data and complete printing order data for all process colors and spot colors used is available, CxF color conversion processing is performed, otherwise ICC color conversion processing is performed.

[0146] For pixel data that is composed only of spot color value data, if CxF data and complete printing order data for all spot colors used are available, CxF color conversion processing is performed; otherwise, ICC color conversion processing is performed.

[0147] <7.2 CxF color conversion processing> The procedure of the CxF color conversion process included in the color conversion process of step S60 in Fig. 5 will be described with reference to the flowchart shown in Fig. 20. Here, attention is paid to one combination of color values ​​for multiple colors including at least one spot color (hereinafter referred to as a "color value group of interest").

[0148] After the CxF color conversion process starts, first, the data of the target color value group (multiple color value data based on the overlap of multiple colors) is converted into spectral characteristic data (36 pieces of spectral reflectance data) using the CxF data and the printing order data (step S610). In this regard, if missing data is detected in the process of step S30 in FIG. 5, the process of step S610 is performed in a state in which the missing data is complemented by the complement data DH. Note that, as a specific method for converting the data of the target color value group into spectral characteristic data, a known method such as that disclosed in JP 2020-017902 A can be adopted.

[0149] Next, the spectral characteristic data is converted into XYZ data (data of tristimulus values ​​X, Y, and Z) (step S620). The tristimulus values ​​X, Y, and Z are calculated by a known method from the spectral distribution of the light source, the spectral reflectance, and color matching functions corresponding to the tristimulus values ​​X, Y, and Z, respectively. More specifically, the tristimulus values ​​X, Y, and Z are calculated by the following formula (2).

number

[0150] Next, the XYZ data (data of tristimulus values ​​X, Y, and Z) is converted into Lab data (data in the CIELAB color space) by a known method (step S630).

[0151] Finally, the XYZ data is converted to CMYK data (data representing the color values ​​of process colors) using an ICC profile for output to the digital printing device 200 (step S640).

[0152] In this embodiment, the first conversion step is realized by step S610, and the second conversion step is realized by steps S620, S630, and S640.

[0153] <8. Effects> According to this embodiment, after the PDF file 60 including image data is provided to the print data generating device 100 as input data, the print data generating device 100 analyzes the PDF file 60 and identifies missing data (missing data) among the data (essential data) required for the execution of the CxF color conversion process. Then, if the printing order data is missing, the printing order data is supplemented without user operation, and if the spectral characteristic data of the patches constituting the CxF chart is missing, the spectral characteristic data is supplemented without user operation. As described above, according to this embodiment, regarding the CxF color conversion process, the determination of the presence or absence of essential data and the supplement of missing data are automatically performed without user operation. This makes the printing process of image data including spot colors by the digital printing device 200 more efficient. In addition, it becomes possible to execute digital printing of image data including spot colors so that even a user who has little knowledge of analog printing can obtain a printed product with high accuracy.

[0154] <9. Variations> A modification of the above embodiment will now be described.

[0155] <9.1 First modified example> In the above embodiment, it is assumed that the required data (data required to execute the CxF color conversion process) is provided to the print data generating device 100 as data in a PDF file, but this is not limited to the above. As in this modified example, a configuration may be adopted in which the required data is provided to the print data generating device 100 in the form of a file other than the PDF file.

[0156] First, a case will be described in which printing order data is provided to the printing data generating device 100 in a file other than the PDF file (hereinafter, for convenience, referred to as a "first external file"). In this case, the following process is performed in step S30 of FIG. 5. By analyzing the PDF file, a process is performed to identify the above-mentioned insufficient patches regarding the spot colors used in the image data. In addition, by analyzing the external file, a process is performed to check whether or not the external file contains complete printing order data described according to a predetermined rule. In other words, the printing data generating device 100 as a computer analyzes the first external file to determine whether or not the input data contains complete printing order data.

[0157] Next, a case will be described in which the CxF data is provided to the print data generating device 100 in a file other than the PDF file (hereinafter, for convenience, referred to as the "second external file"). It is assumed that the PDF file contains complete printing order data. In this case, the following process is performed in step S30 of FIG. 5. The second external file is analyzed to identify the above-mentioned insufficient patches regarding the spot colors used in the image data. Also, the PDF file is analyzed to determine whether the complete printing order data is included in the input data.

[0158] It is also possible to adopt a configuration in which both the printing order data and the CxF data are provided to the print data generating device 100 in a file separate from the PDF file.

[0159] <9.2 Second modified example> In the above embodiment, the spectral characteristic data of the insufficient patch is complemented by using any one of the first to sixth spectral characteristic calculation methods, but the present invention is not limited to this. If the spectral characteristic data of the insufficient patch is stored in the spectral characteristic database 142, the spectral characteristic data of the insufficient patch may be complemented from the spectral characteristic database 142.

[0160] In this modification, with regard to the procedure of classifying the detection status of insufficient patches for each spot color in order to determine a method of complementing the spectral characteristic data, step S450 is provided before step S451 as shown in Fig. 21. In step S450, if available spectral characteristic data is included in the spectral characteristic database 142, the spectral characteristic data is acquired.

[0161] <9.3 Third modified example> In the above embodiment, the spectral reflectance data is used as the data of the spectral values ​​constituting the spectral characteristic data. However, the present invention is not limited to this. For example, the data of the spectral values ​​constituting the spectral characteristic data may be data of the spectral absorptance (a value obtained by subtracting the spectral reflectance from 1) or data of the spectral absorption coefficient.

[0162] <9.4 Fourth Variation> In the above embodiment, a check is performed to see whether the input data DIN includes complete printing order data, and if the input data DIN does not include complete printing order data, a process of complementing the printing order data is performed. However, the present invention is not limited to this. For example, if an operation is adopted in which the input data DIN always includes complete printing order data, the process of checking whether the input data DIN includes complete printing order data or the process of complementing the printing order data becomes unnecessary. That is, among the components shown in FIG. 4, the printing order data confirmation unit 422 and the printing order complement unit 432 become unnecessary, and among the steps shown in FIG. 5, step S50 and step S55 become unnecessary.

[0163] <10.Other> The present invention is not limited to the above embodiment (including the modified example), and can be modified in various ways without departing from the spirit of the present invention. For example, in the above embodiment, a PDF file (PDF data) is provided as input data to the print data generating device 100, but the present invention can also be applied to a configuration in which data in a format other than PDF is provided as input data to the print data generating device 100.

[0164] <11. Notes> From the above disclosure, a color data conversion device having the following configuration is also conceivable.

[0165] A color data conversion device that converts color data related to spot colors included in input data into color data for a printing device used for printing, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, A processor (CPU) and Memory for storing programs Equipped with A color data conversion device, characterized in that, when the program stored in the memory is executed by the processor, the program causes the processor to execute the following operations (A), (B), (C), (D), and (E): (A) Image data of a print target including color data and spectral characteristic data related to spot colors used in the image data are received as the input data. (B) By analyzing the input data, patches for which spectral characteristic data is not available for spot colors used in the image data are identified as insufficient patches. (C) Complementing the spectral characteristic data of the insufficient patch. (D) Based on the color chart data including the complemented spectral characteristic data, color data related to spot colors and included in the image data is converted into spectral characteristic data. (E) The spectral characteristic data obtained by the conversion is converted into color data for the printing device. [Explanation of symbols]

[0166] 41…Submission section 42…Missing data identification section 43…Complementary section 44...Data conversion section 60...PDF file 61...Printing order data 62…CxF data 100...print data generating device 141...Print data generation program 142...Spectral characteristics database 200...Digital printing equipment 421...Insufficient patch identification section 422…Printing order data confirmation section 431…Spectral characteristic complement section 432…Printing order completion section 441…First conversion unit 442…Second conversion unit P...Color data conversion program PA1…Paper white patch PA2…Solid patch PA3…Black solid patch PA4…Double solid patch

Claims

1. A color data conversion method for converting color data relating to spot colors included in input data into color data for a printing device used for printing, using a computer, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, the method comprising: an input step of providing, as input data, image data of a printing target including color data and spectral characteristic data relating to spot colors used in the image data to the computer; an insufficient patch identifying step in which the computer analyzes the input data to identify, as an insufficient patch, a patch for which spectral characteristic data is not obtained regarding a spot color used in the image data; A spectral characteristic complementation step in which the computer complements the spectral characteristic data of the insufficient patch; a first conversion step in which the computer converts color data related to spot colors and included in the image data into spectral characteristic data based on the color chart data including the spectral characteristic data complemented in the spectral characteristic complement step; a second conversion step in which the computer converts the spectral characteristic data obtained in the first conversion step into color data for the printing device; A color data conversion method comprising:

2. a printing order data confirmation step in which the computer determines whether or not the input data includes printing order data that indicates a printing order of a plurality of ink colors when analog printing is performed based on the image data; a printing order supplementing step in which the computer supplements the printing order data when it is determined in the printing order data confirmation step that the printing order data is not included in the input data; Further comprising:

2. The color data conversion method according to claim 1, wherein in the first conversion step, the computer converts the color data included in the image data into spectral characteristic data, taking into account the printing order data complemented in the printing order complement step.

3. The printing order complementing step includes: a brightness classification step in which the computer classifies the ink colors used in the analog printing into a plurality of brightness levels according to their brightness; a saturation classification step in which, when two or more ink colors are classified into the same lightness level in the lightness classification step, the computer classifies the two or more ink colors into a plurality of saturation levels according to their saturation; Including, 3. The color data conversion method according to claim 2, characterized in that in the printing order complementing step, the computer complements the printing order data so that ink colors classified into a lightness level corresponding to a higher lightness are printed earlier than ink colors classified into a lightness level corresponding to a lower lightness, and that ink colors classified into a saturation level corresponding to a higher saturation are printed earlier than ink colors classified into a saturation level corresponding to a lower saturation with respect to two or more ink colors classified into the same lightness level.

4. 4. The color data conversion method according to claim 3, wherein when two or more ink colors are classified into the same saturation level in the saturation classification step, the computer complements the printing order data so that the printing order of the two or more ink colors is an order based on hue that follows a predetermined rule.

5. In the submission step, the printing order data is provided to the computer as a first external file separate from a file constituting the image data, A color data conversion method described in any one of claims 2 to 4, characterized in that in the printing order data confirmation step, the computer determines whether the printing order data is included in the submitted data by analyzing the first external file.

6. In the input step, spectral characteristic data for the spot color used in the image data is provided to the computer as a second external file separate from a file constituting the image data, 6. The color data conversion method according to claim 1, wherein in the insufficient patch identifying step, the computer identifies the insufficient patch by analyzing the second external file.

7. A color data conversion method according to any one of claims 1 to 6, characterized in that in the spectral characteristic complementation step, the spectral characteristic data of the insufficient patch is obtained from a spectral characteristic database that holds the spectral characteristic data of the multiple patches for multiple spot colors.

8. The spectral characteristic complementing step includes: a reference color selection step of selecting, as a reference color, a color close to the prediction target color from among a plurality of sample colors for which the spectral characteristic data of the plurality of patches is obtained, the special color for which the insufficient patch is identified as a prediction target color; a relational expression calculation step of determining, for the reference color, a relational expression representing a relationship between the spectral characteristic data of the reference patch and the spectral characteristic data of the insufficient patch, using a patch having a maximum ink density among the plurality of patches as a reference patch; a spectral characteristic calculation step of calculating the spectral characteristic data of the insufficient patch for the prediction target color by applying the spectral characteristic data of the reference patch for the prediction target color to the relational expression; 8. The method of claim 1, further comprising:

9. 9. The color data conversion method according to claim 8, wherein the spectral characteristic complementing step further includes a minimum density patch data acquisition step of acquiring spectral characteristic data of a patch having a minimum ink density for the prediction target color from a spectral characteristic database that holds the spectral characteristic data of the plurality of patches for a plurality of spot colors.

10. The spectral characteristic complementing step includes: a first relational equation calculation step of determining a first relational equation expressing a relationship between the spectral characteristic data of a patch having a maximum ink density and the spectral characteristic data of the characteristic-acquired patch for each of a plurality of sample colors for which the spectral characteristic data of the plurality of patches has been obtained, with the special color for which the insufficient patch has been identified as a prediction target color and the patch for which the spectral characteristic data of the prediction target color has been obtained as a characteristic-acquired patch; a prediction step of calculating a predicted value of the spectral characteristics data of the patch whose characteristics have been acquired for the prediction target color by applying the spectral characteristics data of the patch having the maximum ink density for the prediction target color to a corresponding first relational expression for each of the plurality of sample colors; a difference value calculation step of calculating a difference value between the predicted value calculated in the prediction step and the value of the spectral characteristic data of the patch whose characteristics have been acquired for the prediction target color for each of the plurality of sample colors; a reference color selection step of selecting, as a reference color, the sample color that has the smallest difference value in the difference value calculation step from among the plurality of sample colors; a second relational equation calculation step of calculating, as second relational equations, a relational equation expressing a relationship between the spectral characteristics data of a patch having a maximum ink density and the spectral characteristics data of an insufficient patch having an ink density greater than that of the characteristic-acquired patch, and a relational equation expressing a relationship between the spectral characteristics data of the characteristic-acquired patch and the spectral characteristics data of an insufficient patch having an ink density less than that of the characteristic-acquired patch; a spectral characteristic calculation step of calculating, for the prediction target color, spectral characteristic data of an insufficient patch having an ink density greater than that of the characteristic-acquired patch by applying the spectral characteristic data of the patch having the maximum ink density to a corresponding second relational expression, and calculating, for the insufficient patch having an ink density less than that of the characteristic-acquired patch by applying the spectral characteristic data of the characteristic-acquired patch to a corresponding second relational expression; 8. The method of claim 1, further comprising:

11. 8. The color data conversion method according to claim 1, wherein in the spectral characteristic complementation step, the special color for which the insufficient patch is identified is set as a prediction target color, and spectral characteristic data of an insufficient patch corresponding to an ink density between the ink density of the first patch and the ink density of the third patch is obtained by spline interpolation using spectral characteristic data of a first patch having the highest ink density among three patches for which spectral characteristic data has been obtained for the prediction target color, spectral characteristic data of a third patch having the lowest ink density among the three patches, and spectral characteristic data of a second patch having an ink density lower than that of the first patch and higher than that of the third patch.

12. 8. The color data conversion method according to claim 1, wherein in the spectral characteristic complementation step, the special color for which the insufficient patch is identified is set as a prediction target color, and the spectral characteristic data of the insufficient patch for the prediction target color is obtained by linear interpolation using the spectral characteristic data of a patch with the maximum ink density for the prediction target color and the spectral characteristic data of a patch with the minimum ink density for the prediction target color.

13. the plurality of patches include eleven first type patches obtained by applying a spot color ink on a base material at eleven ink densities ranging from a minimum density to a maximum density, and eleven second type patches obtained by applying a spot color ink on a black base material at eleven ink densities ranging from the minimum density to the maximum density; 8. The color data conversion method according to claim 1, wherein in the spectral characteristic complementation step, the special color identified as the insufficient patch is set as the prediction target color, the patch identified as the insufficient patch among the eleven second type patches is set as the prediction target patch, and the spectral characteristic data of the reference patch for the prediction target color is provided as input data to a trained neural network that uses as input data the spectral characteristic data of a reference patch that is the patch with the greatest ink density among the eleven first type patches and as output data the spectral characteristic data of the prediction target patch, thereby determining the spectral characteristic data of the prediction target patch for the prediction target color.

14. A color data conversion device that converts color data related to spot colors included in input data into color data for a printing device used for printing, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, an input unit that receives, as input data, image data of a print target including color data and spectral characteristic data related to spot colors used in the image data; an insufficient patch identifying unit that identifies a patch for which spectral characteristic data is not obtained regarding a spot color used in the image data as an insufficient patch by analyzing the input data; a spectral characteristic complementing unit that complements the spectral characteristic data of the insufficient patch; a first conversion unit that converts color data related to spot colors and included in the image data into spectral characteristic data based on the color chart data including the spectral characteristic data complemented by the spectral characteristic complement unit; a second conversion unit that converts the spectral characteristic data obtained by the first conversion unit into color data for the printing device; A color data conversion device comprising:

15. A color data conversion program for converting color data relating to spot colors included in input data into color data for a printing device used for printing, based on color chart data including spectral characteristic data of a plurality of patches corresponding to a plurality of levels of ink density, On the computer, an insufficient patch identifying step of identifying a patch for which spectral characteristic data is not obtained for a spot color used in the image data by analyzing the input data, which is composed of image data of a print target including color data and spectral characteristic data for a spot color used in the image data, as an insufficient patch; a spectral characteristic complementation step of complementing the spectral characteristic data of the insufficient patch; a first conversion step of converting color data related to spot colors and included in the image data into spectral characteristic data based on the color chart data including the spectral characteristic data complemented in the spectral characteristic complement step; a second conversion step of converting the spectral characteristic data obtained in the first conversion step into color data for the printing device; A color data conversion program for executing the above.

Images