Image processing apparatus, information processing system, image processing method, and program

Abstract

To provide an image processing apparatus, an information processing system, an image processing method, and a program capable of suppressing occurrence of downtime and improving accuracy of color matching without requiring colorimetric work using an external colorimeter.SOLUTION: The image forming apparatus includes a first acquisition unit configured to acquire a first device-dependent color value of a patch corresponding to a first gradation value read by a reading device from a first chart printed and output based on the first gradation value of a target mixed color in a first state, a second acquisition unit configured to acquire a second device-dependent color value of the patch corresponding to the first gradation value read by the reading device from a second chart printed and output based on the first gradation value in a second state, and a correction unit configured to correct the first gradation value so that a color difference between the second color value and the first color value is equal to or more than a threshold value set as a device-dependent color difference corresponding to an allowable device-independent color difference with respect to the target mixed color.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to an image processing apparatus, an information processing system, an image processing method, and a program. [Background technology]

[0002] Because the color state of printed materials output from an image forming device changes over time, regular color matching is required. This requires proper color matching of gray, a mixture of C (cyan), M (magenta), and Y (yellow). Color matching for time-dependent changes in mixed colors requires outputting patches of neighboring colors of the color being matched and acquiring their colorimetric values ​​in order to identify the direction of color matching. To ensure color matching accuracy, it is desirable to acquire neighboring colors as close as possible to the actual color matching process. However, acquiring neighboring colors each time color matching is performed places a significant burden on the user, who must use a colorimeter to measure colors.

[0003] There are two factors that increase the workload of such color measurement work. First, the gamut of the output neighboring colors must include colors close to the color to be matched (target color), and to guarantee that these colors will be included regardless of any changes over time, it is necessary to output neighboring colors from a wide gamut. Second, to improve the accuracy of predicting color changes, it is necessary to increase the number of colors that can be output within the gamut, and the wider the gamut, the greater the number of colors required to achieve prediction accuracy.

[0004] To reduce the burden on users due to such color measurement, one possible method is to read color values ​​as RGB values ​​from patches using an inline sensor within the image forming device, instead of measuring colors using an external spectrophotometer, etc. The color values ​​read in this way are device-dependent RGB values, and if color matching processing is to be performed using Lab values ​​as in the case of color measurement using a colorimeter, it is necessary to have an internal conversion table for converting RGB values ​​to Lab values.

[0005] Furthermore, as a technology for maintaining print density during printing using RGB values ​​read by such a reading means, an image forming device has been disclosed that sets density correction values ​​based on the read data read by the reading means and the print data before density correction that corresponds to the read data (for example, Patent Document 1). Summary of the Invention [Problem to be solved by the invention]

[0006] However, in the conventional technology, in the method using an inline sensor as described above, it is known that the correspondence between RGB values ​​and Lab values ​​varies greatly depending on the paper characteristics, and a conversion table like the one described above needs to be created each time for each paper characteristic, which ultimately requires the user to measure the color of the patch using a colorimeter. Also, in the technology described in Patent Document 1, density correction is performed based on the difference between the target RGB value and the RGB value to be corrected, but it is not possible to determine whether the difference is large enough to require correction, so if the difference is not at a level that requires correction, downtime occurs due to the correction process, and even overcorrection may actually result in a deterioration in accuracy.

[0007] The present invention has been made in consideration of the above, and aims to provide an image processing device, an information processing system, an image processing method, and a program that can reduce downtime and improve color matching accuracy without requiring color measurement work using an external colorimeter. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems and achieve the object, the present invention provides an image processing device that performs color matching of gradation values ​​of the same target color obtained in two different states, the image processing device comprising: a first acquisition unit that, in a first state, acquires a device-dependent first color value of a patch corresponding to a first gradation value read by a reading device provided in the image forming device from a first chart printed out from the image forming device based on the first gradation value of the target color mixture; a second acquisition unit that, in a second state different from the first state, acquires a device-dependent second color value of a patch corresponding to the first gradation value read by the reading device from a second chart printed out from the image forming device based on the first gradation value; and a correction unit that corrects the first gradation value so that the color mixture using the first gradation value in the second state becomes the target color mixture when the color difference between the second color value and the first color value is equal to or greater than a threshold value set as a device-dependent color difference corresponding to a device-independent color difference that is allowable for the target color mixture. [Effects of the Invention]

[0009] According to the present invention, it is possible to reduce downtime and improve the accuracy of color matching without requiring color measurement work using an external colorimeter. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of the overall configuration of an information processing system according to the first embodiment. [Figure 2] FIG. 2 is a diagram illustrating the operation of generating a monochrome TRC by the image processing device according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating that the correspondence between Lab values ​​and RGB values ​​differs for each paper type. [Figure 4] FIG. 4 is a diagram showing an example of the distribution of RGB values ​​read from patches of the same gradation value for each of a plurality of paper types and Lab values ​​measured by an external colorimeter. [Figure 5]FIG. 5 is a diagram showing an example of the color difference between the RGB values ​​read from a patch of a certain gradation value and a patch having a predetermined Lab color difference or less, calculated for a plurality of paper types. [Figure 6] FIG. 6 is a diagram illustrating an example of a hardware configuration of the image processing apparatus according to the first embodiment. [Figure 7] FIG. 7 is a diagram showing an outline of the structure of the image forming apparatus according to the first embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of a hardware configuration of the image forming apparatus according to the first embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of the functional block configuration of the image processing apparatus according to the first embodiment. [Figure 10] FIG. 10 is a flowchart showing an example of the flow of the target gray and neighboring gray acquisition process of the image processing apparatus according to the first embodiment. [Figure 11] FIG. 11 is a diagram showing an example of a target / neighborhood chart in the first embodiment. [Figure 12] FIG. 12 is a diagram showing an example of the tone values ​​and color values ​​of the target gray, as well as the color difference tolerance and correction threshold. [Figure 13] FIG. 13 is a diagram showing an example of the tone values ​​and color values ​​of neighboring grays. [Figure 14] FIG. 14 is a diagram illustrating the operation of the neighborhood color change prediction model. [Figure 15] FIG. 15 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the first embodiment. [Figure 16] FIG. 16 is a diagram showing an example of an updated gray chart in the first embodiment. [Figure 17] FIG. 17 is a flowchart showing an example of the flow of the corrected gray gradation value calculation process of the image processing apparatus according to the first embodiment. [Figure 18] FIG. 18 is a diagram illustrating the calculation process of the corrected gray gradation value calculation process of the image processing apparatus according to the first embodiment. [Figure 19]FIG. 19 is a diagram showing an example of the target gray tone values ​​and corrected gray tone values. [Figure 20] FIG. 20 is a diagram illustrating an example of the functional block configuration of the image processing apparatus according to the second embodiment. [Figure 21] FIG. 21 is a flowchart showing an example of the flow of the target gray / neighboring gray acquisition process of the image processing apparatus according to the second embodiment. [Figure 22] FIG. 22 is a diagram showing an example of a target / neighborhood chart in the second embodiment. [Figure 23] FIG. 23 is a diagram showing an example of the tone values ​​and color values ​​of the neighboring gray and paper white. [Figure 24] FIG. 24 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of an image processing device, an information processing system, an image processing method, and a program according to the present invention will be described in detail with reference to the drawings. Furthermore, the present invention is not limited to the following embodiments, and the components in the following embodiments include those that would be easily conceived by a person skilled in the art, those that are substantially the same, and those that are within the scope of what is called equivalents. Furthermore, various omissions, substitutions, modifications, and combinations of the components can be made without departing from the spirit of the following embodiments.

[0012] [First embodiment] (Overall configuration of information processing system) 1 is a diagram showing an example of the overall configuration of an information processing system according to the first embodiment. The overall configuration of an information processing system 100 according to this embodiment will be described with reference to FIG.

[0013] As shown in Fig. 1, the information processing system 100 includes an image processing device 1, an image forming device 2, and a user PC (Personal Computer) 3. Each device is capable of data communication via a network N. The network N is a network configured by a LAN (Local Area Network) or the like. The network N may be a wired network or a wireless network.

[0014] The image processing device 1 is a device that performs color matching (correction processing) on ​​gray, which is a mixed color of C (cyan), M (magenta), and Y (yellow), for printed matter output by the image forming device 2. The image processing device 1 may be, for example, an information processing device such as a normal PC, or may be a DFE (Digital Front End) when the image forming device 2 is a commercial printing press or the like. Furthermore, although the mixed color is described as a three-color gray of C, M, and Y as described above, it is not limited to this and may be a secondary color or another mixed color.

[0015] The image forming device 2 is a device that prints out based on image data that is output from a user PC 3 and has been subjected to image processing by the image processing device 1. The image forming device 2 is, for example, an electrophotographic printer or an MFP (Multifunction Peripheral). As shown in FIG. 1, the image forming device 2 is equipped with a reading device 5 that is an inline sensor.

[0016] The reading device 5 is an inline sensor provided in the image forming device 2 for reading the chart printed out by the image forming device 2. The reading device 5 transmits, to the image processing device 1, RGB color values ​​(RGB values) in the RGB color space obtained by reading the chart.

[0017] The user PC 3 is an information processing device that transmits image data to be printed to the image processing device 1.

[0018] (About the generation of monochrome TRC) 2 is a diagram illustrating the operation of generating a monochromatic TRC by the image processing device 1 according to the first embodiment. The operation of generating a monochromatic TRC by the image processing device 1 according to the present embodiment will be described with reference to FIG.

[0019] Conventionally, in image processing using electrophotography, correction of the gradation values ​​of each of the single colors C, M, Y, and K (so-called monochromatic calibration) has been performed. FIG. 2 shows the flow up to the generation of a monochromatic TRC (Tone Reproduction Curve) in monochromatic calibration. The generation of a monochromatic TRC is performed when a new chart is created and when an update is made. When a new chart is created (an example of a first state), the image forming device 2 prints a chart including patches for each of the single colors C, M, and Y, in which the gradation values ​​are modulated in stages from 0 to 100%. The image processing device 1 then creates density targets for each input gradation value from the measured densities of the patches, as shown in FIG. 2(a), and generates a monochromatic TRC for converting the input gradation values ​​to output gradation values ​​that match the targets, as shown in FIG. 2(b).

[0020] However, when printing is actually performed using the image forming device 2 using the target gradation values ​​shown in FIG. 2(a), as time passes or environmental changes occur, the density measured will not fall on the target curve but will instead be measured on the "measured density" curve shown in FIG. 2(a), resulting in deviation from the target curve. For example, as shown in FIG. 2(a), according to the target curve, ideally, the density measured when a patch of a certain monochromatic color is printed at a gradation value of 30% should be 0.2. However, in reality, the density will not be measured as 0.2 unless the patch is printed at a gradation value of 50%. In this case, the image processing device 1 generates a monochromatic TRC that converts the specified gradation value of 30% (input gradation value) to a gradation value of 50% (output gradation value), as shown in FIG. 2(b). As a result, the specified gradation value of 30[%] (input gradation value) is converted to a gradation value of 50[%] by the monochromatic TRC, and by printing a patch using this gradation value of 50[%], it becomes possible to measure the density of the patch as 0.2. Even when printing general image data thereafter using the image forming device 2, monochromatic color management is performed by applying this monochromatic TRC.

[0021] Similarly, during update (an example of the second state), the image forming device 2 prints a chart, and the image processing device 1 updates the single-color TRC so that the density measured for the patch matches the target created during new creation. As a result, the density targets for each single color created during new creation are reproduced during update.

[0022] However, while single-color calibration guarantees color reproducibility for single colors over time or against environmental changes, single-color calibration does not guarantee color reproducibility for mixed colors obtained by superimposing single colors over time or against environmental changes. Therefore, in addition to single-color calibration, correction of mixed colors combining C, M, Y, and K must also be performed. Gray, a tertiary color of C, M, and Y, is a commonly used color, and even a slight change in gray appears significantly different to the human eye. Therefore, gray correction specifically for gray may be performed. This embodiment describes gray correction, but it can also be applied to secondary colors or mixed colors including K.

[0023] (Gray correction process overview) Fig. 3 is a diagram illustrating that the correspondence between Lab values ​​and RGB values ​​differs for each paper type. Fig. 4 is a diagram illustrating an example of the distribution of RGB values ​​read for patches of the same gradation value for multiple paper types, and Lab values ​​measured by an external colorimeter. Fig. 5 is a diagram illustrating an example of the color difference of RGB values ​​read for a patch of a certain gradation value and a patch having a predetermined Lab color difference or less, calculated for multiple paper types. An overview of the gray correction process will be described with reference to Figs. 3 to 5.

[0024] While monochrome calibration can be achieved with simple correction because the target color is composed of only one toner color, gray correction requires simultaneous correction of all three toner colors because the target color is composed of three toner colors. Therefore, a common technique involves printing patches of grays with slightly different combinations of C, M, and Y relative to the target gray using an image forming device, measuring the color values ​​of the patches, predicting the color changes that will occur when C, M, and Y are changed, and identifying a color that matches the target color. In this case, the color gamut formed by the printed neighboring grays must include colors that change in hue due to changes in the target color over time or environmental changes. To ensure that the changed color of the target color is accurately included regardless of any changes over time or environmental changes, it is necessary to print neighboring grays from a wide color gamut. Furthermore, since it is impractical to print all neighboring grays within the color gamut, neighboring grays that were not printed are predicted by interpolation from the measured color values ​​of the printed neighboring grays. However, because prediction errors due to interpolation affect correction accuracy, it is necessary to have several different combinations of C, M, and Y that make up the nearby gray to be printed in order to increase correction accuracy to a certain extent, but the wider the color gamut of the nearby gray to be printed, the more different combinations of C, M, and Y must be increased in order to maintain sufficient prediction accuracy.As a result, it is necessary to print many nearby gray patches.

[0025] Considering that color changes over time occur when C, M, and Y are changed, it is desirable to print patches of nearby gray at the same time as making corrections; however, if not only the gray to be corrected but also its nearby grays are printed each time a correction is made, and if the user measures the colors using an external colorimeter, they will have to measure a large number of patches, which places a heavy burden on the user.

[0026] To address these issues, a new technology has emerged in recent years: an inline sensor capable of reading images is installed inside the image forming device, along the transport path of the printed material, and correction is performed using the read values. Using such an image forming device eliminates the need for users to perform color measurements. However, because using a spectral sensor equivalent to an external colorimeter would be expensive, RGB sensors capable of reading RGB values ​​are often used as inline sensors. Because the RGB values ​​read by an RGB sensor are device-dependent and not accurate color values, color correction using an RGB sensor typically involves converting them to device-independent values ​​such as Lab values ​​before processing. When performing such conversion, a conversion table pre-installed in the device can be used.

[0027] As shown in Figure 3(a), if the RGB values ​​of a patch of a certain color read on a certain sheet of paper A are (R1, G1, B1), the corresponding Lab value is always (L1A, a1A, b1A), and if the RGB values ​​of a patch of another color are (R2, G2, B2), the corresponding Lab value is always (L2A, a2A, b2A). Also, as shown in Figure 3(b), if there is a patch read on a different sheet of paper B with the same RGB values ​​(R1, G1, B1), the corresponding Lab value is always (L1B, a1B, b1B), and the Lab value of a patch read with RGB values ​​(R2, G2, B2) is always (L2B, a2B, b2B).

[0028] Figure 4 also shows the distribution of RGB values ​​(Figure 4(a)) read by an inline sensor for gray patches of the same gradation value for six paper types, and the distribution of Lab values ​​measured by an external spectrophotometer (Figure 4(b)). As shown in Figure 4, for example, Paper B and Paper F have nearly identical RGB values, but the corresponding Lab values ​​differ between the papers. Even if similar RGB values ​​are read by an inline sensor, the corresponding Lab values ​​differ for different paper types. In such cases, it is difficult to ensure consistent conversion accuracy for all paper types using a pre-installed conversion table. Therefore, to maintain high conversion accuracy, it is necessary to measure the same patch for each paper type using an external spectrophotometer and then read it using an inline sensor. Then, a conversion table for each paper type must be created from the RGB and Lab values. Ultimately, even when using an inline sensor, users must measure a large number of patches.

[0029] Here, as described below, in the image forming apparatus 2 according to this embodiment, if the color difference between the target gray and the gray to be corrected is equal to or less than a predetermined threshold, correction is not performed. This color difference is the focus of attention. As described above, the Lab values ​​corresponding to RGB values ​​differ depending on the paper type. However, for the same paper type, a given RGB value always corresponds to the same Lab value one-to-one. The determination of whether correction should be performed is based on whether the color difference between the Lab values ​​of the target gray and the gray to be corrected (Lab color difference) is equal to or greater than a threshold. This Lab color difference is fixed regardless of paper type or aging. Furthermore, as described above, the correspondence between RGB values ​​and Lab values ​​for the same paper type is always constant, so the color difference between the RGB values ​​corresponding to the Lab color difference (RGB color difference) for the same paper type is always constant. In other words, by storing fixed values ​​for the RGB color difference corresponding to the Lab color difference threshold for each paper type, the determination of whether correction should be performed can be made simply by dealing with the RGB values. FIG. 5 shows the difference between the read RGB values ​​(R,G,B) for a patch with a certain gradation value (C,M,Y) and a patch with a gradation value (C',M',Y') whose Lab color difference is below a predetermined threshold, calculated for each of six paper types. As shown in FIG. 5, while the Lab color difference is within a range below a predetermined threshold for these six paper types, the range of RGB color difference is also roughly the same regardless of paper type. Conventionally, because Lab color values ​​are required for correction execution determination, a user must measure the color of patches using an external colorimeter for each paper type in order to create a conversion table. However, this embodiment describes details of an information processing system 100 that can improve color matching accuracy without requiring color measurement using an external colorimeter.

[0030] (Hardware configuration of image processing device) 6 is a diagram showing an example of the hardware configuration of the image processing device according to the first embodiment. The hardware configuration of the image processing device 1 according to this embodiment will be described with reference to FIG.

[0031] As shown in FIG. 6, the image processing device 1 includes a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, an auxiliary storage device 505, a media drive 507, a display 508, a network I / F 509, a keyboard 511, a mouse 512, and a DVD (Digital Versatile Disc) drive 514.

[0032] The CPU 501 is a computing device that controls the overall operation of the image processing device 1. The ROM 502 is a non-volatile storage device that stores programs such as an IPL (Initial Program Loader) that is initially executed by the CPU 501. The RAM 503 is a volatile storage device that is used as a work area for the CPU 501.

[0033] The auxiliary storage device 505 is a non-volatile storage device that stores various data such as programs, etc. The auxiliary storage device 505 is, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0034] The media drive 507 is a device that controls reading and writing of data from and to a recording medium 506 such as a flash memory.

[0035] The display 508 is a liquid crystal display (LCD) or an organic electroluminescence (EL) display that displays various types of information such as a cursor, menu, window, text, or image.

[0036] The network I / F 509 is an interface for performing data communication using the network N. The network I / F 509 is, for example, a network interface card (NIC) that enables communication using a transmission control protocol (TCP) / internet protocol (IP). The network I / F 509 may also be a communication interface having a wireless communication function based on a standard such as Wi-Fi (registered trademark).

[0037] The keyboard 511 is an example of an input device having multiple keys for inputting characters, numbers, various instructions, etc. The mouse 512 is a type of input device for selecting and executing various instructions, selecting a processing target, moving a cursor, etc.

[0038] The DVD drive 514 is a device that controls reading and writing of various data from and to a DVD 513, which is an example of a removable storage medium. The DVD 513 is, for example, a DVD-RW (Digital Versatile Disk Rewritable), a DVD-R (Digital Versatile Disk Recordable), a CD-RW (Compact Disc Rewritable), or a CD-R (Compact Disc Recordable).

[0039] The above-mentioned CPU 501, ROM 502, RAM 503, auxiliary storage device 505, media drive 507, display 508, network I / F 509, keyboard 511, mouse 512 and DVD drive 514 are connected to each other so that they can communicate with each other via bus lines 510 such as an address bus and a data bus.

[0040] 6 is an example, and it is not necessary to include all the components, and other components may be included. The hardware configuration of the user PC 3 also conforms to the configuration shown in FIG.

[0041] (Outline of the structure of an image forming device) 7 is a diagram showing an outline of the structure of the image forming apparatus according to the first embodiment. With reference to FIG. 7, an outline of the structure of the image forming apparatus 2 according to this embodiment will be described.

[0042] 7 is a printing device such as a tandem-type MFP, etc. As shown in FIG. 7, the image forming device 2 includes a paper feed tray 300, a conveying roller 301, an intermediate transfer belt 302, photosensitive drums 303C, 303M, 303Y, and 303K, a transfer roller 304, a fixing roller 305, a controller 600, and a reading device 5.

[0043] The paper feed tray 300 is a tray that stores recording media such as paper to be fed.

[0044] The transport rollers 301 are a pair of rollers that transport a recording medium fed from a paper feed tray 300 along a transport path to a transfer roller 304 .

[0045] Intermediate transfer belt 302 is an endless belt on which intermediate transfer images are formed by photosensitive drums 303C, 303M, 303Y, and 303K. Intermediate transfer belt 302 rotates clockwise as viewed on the paper in Fig. 7, and toner images of each color are formed on photosensitive drums 303K, 303C, 303M, and 303Y in this order.

[0046] Photoconductor drum 303C is a photoconductor drum that forms a cyan toner image on intermediate transfer belt 302. Photoconductor drum 303M is a photoconductor drum that forms a magenta toner image on intermediate transfer belt 302. Photoconductor drum 303Y is a photoconductor drum that forms a yellow toner image on intermediate transfer belt 302. Photoconductor drum 303K is a photoconductor drum that forms a black toner image on intermediate transfer belt 302. To form an intermediate transfer image on intermediate transfer belt 302, photoconductor drums 303K, 303C, 303M, and 303Y are arranged in this order from upstream in the rotation direction of intermediate transfer belt 302. As a result, toner images of each color are formed on the surface of intermediate transfer belt 302, and a full-color image is formed as an intermediate transfer image. When referring to any one of photoconductor drums 303C, 303M, 303Y, and 303K or when referring to them collectively, they will be simply referred to as "photoconductor drum 303." Also, photoconductor drum 303 is configured to use CMYK colors as process colors, but CMY colors may also be used as process colors, or R (red), B (blue), and G (green) may be used as process colors instead of CMY colors.

[0047] The transfer roller 304 is a roller that transfers the intermediate transfer image (full-color image) formed on the intermediate transfer belt 302 onto the recording medium conveyed by the conveying roller 301. By the function of this transfer roller 304, a full-color image is formed (printed) on the recording medium.

[0048] The fixing roller 305 is a roller for fixing a full-color image onto a recording medium on which the image has been formed.

[0049] The reading device 5 is an in-line sensor that reads paper and obtains RGB values, and is installed on the paper transport path after the fixing roller 305. The RGB values ​​read by the reading device 5 are sent to the image processing device 1 via the controller 600.

[0050] The controller 600 is a control device that controls the overall operation of the image forming apparatus 2. The hardware configuration of the controller 600 will be described later with reference to FIG.

[0051] (Hardware configuration of image forming device) 8 is a diagram showing an example of the hardware configuration of the image forming apparatus according to the first embodiment. The hardware configuration of the image forming apparatus 2 will be described with reference to FIG.

[0052] As shown in FIG. 8, the image forming apparatus 2 is configured such that a controller 600, an operation display unit 610, an FCU (Facsimile Control Unit) 620, a plotter 631, and a scanner 632 are connected via a PCI (Peripheral Component Interface) bus.

[0053] The controller 600 is a device that controls the entire image forming apparatus 2 , and controls drawing, communication, and input from the operation display unit 610 .

[0054] The operation display unit 610 is, for example, a touch panel, and is a device that receives input to the controller 600 (input function) and displays (display function) the status of the image forming apparatus 2. The operation display unit 610 is directly connected to an ASIC (Application Specific Integrated Circuit) 606, which will be described later.

[0055] The FCU 620 is a device that implements a fax function, and is connected to the ASIC 606 via, for example, a PCI bus.

[0056] The plotter 631 is a device that realizes a printing function. The plotter 631 is connected to the ASIC 606 via, for example, a PCI bus. The plotter 631 corresponds to the intermediate transfer belt 302, the photosensitive drum 303, and the transfer roller 304 shown in FIG.

[0057] The scanner 632 is a function that realizes a scanner function, and is connected to the ASIC 606 by, for example, a PCI bus.

[0058] As shown in FIG. 8, the controller 600 includes a CPU 601, a system memory (MEM-P) 602, a north bridge (NB) 603, a south bridge (SB) 604a, a network I / F 604b, a USB I / F 604c, a Centronics I / F 604d, a sensor I / F 604e, an ASIC 606, a local memory (MEM-C) 607, and an auxiliary storage device 608.

[0059] The CPU 601 is a computing device that performs overall control of the image forming apparatus 2. The CPU 601 is connected to a chipset consisting of a system memory 602, a north bridge 603, and a south bridge 604a, and is connected to other devices via this chipset.

[0060] The system memory 602 is a memory used as a memory for storing programs and data, a memory for expanding programs and data, a memory for printer drawing, etc., and includes ROM and RAM. Of these, the ROM is a read-only memory used as a memory for storing programs and data, and the RAM is a writable and readable memory used as a memory for expanding programs and data, and a memory for printer drawing, etc.

[0061] The north bridge 603 is a bridge for connecting the CPU 601 with the system memory 602, south bridge 604a, and AGP (Accelerated Graphics Port) bus 605, and has a memory controller that controls reading and writing to the system memory 602, a PCI master, and an AGP target.

[0062] The southbridge 604a is a bridge for connecting the northbridge 603 with PCI devices and peripheral devices. The southbridge 604a is connected to the northbridge 603 via a PCI bus, and the network I / F 604b, USB I / F 604c, Centronics I / F 604d, sensor I / F 604e, etc. are connected to the PCI bus. The above-mentioned reading device 5 is connected to, for example, the sensor I / F 604e.

[0063] The AGP bus 605 is a bus interface for a graphics accelerator card proposed to accelerate graphics processing. The AGP bus 605 is a bus that speeds up the graphics accelerator card by directly accessing the system memory 502 at high throughput.

[0064] The ASIC 606 is an integrated circuit (IC) for image processing applications that has hardware elements for image processing and acts as a bridge connecting the AGP bus 605, PCI bus, auxiliary storage device 608, and local memory 607. The ASIC 606 is composed of a PCI target and AGP master, an arbiter (ARB) that forms the core of the ASIC 606, a memory controller that controls the local memory 607, multiple direct memory access controllers (DMACs) that perform image data rotation and the like using hardware logic, and a PCI unit that transfers data via the PCI bus between the ASIC 606 and the plotter 631 and scanner 632. For example, the FCU 620, plotter 631, and scanner 632 are connected to the ASIC 606 via the PCI bus.

[0065] The local memory 607 is a memory used as an image buffer for copying and a code buffer.

[0066] The auxiliary storage device 608 is a storage device such as an HDD, SSD, SD (Secure Digital) card, or flash memory, and is a storage for storing image data, programs, font data, forms, and the like.

[0067] 8 is an example, and does not necessarily include all of the components, and may include other components. For example, the image forming apparatus 2 may include an ADF (Automatic Document Feeder) and the like.

[0068] (Configuration and operation of functional blocks of image processing device) 9 is a diagram showing an example of the configuration of functional blocks of the image processing device according to the first embodiment. The configuration and operation of the functional blocks of the image processing device 1 according to this embodiment will be described with reference to FIG.

[0069] As shown in FIG. 9, the image processing device 1 has a read value acquisition unit 11, a prediction model creation unit 12 (an example of a fourth acquisition unit), a target color acquisition unit 13 (an example of a first acquisition unit), a memory unit 14, a TRC generation unit 15 (an example of a correction unit), a chart generation unit 16, an image input unit 17, an image processing unit 18, an image output unit 19, a correction target color acquisition unit 20 (an example of a second acquisition unit), a tolerance setting unit 21 (a setting unit), a threshold calculation unit 22 (a calculation unit), and a display control unit 23.

[0070] The read value acquisition unit 11 is a functional unit that acquires, via the network I / F 509, color values ​​(RGB values) read by the reading device 5 for a chart printed by the image forming apparatus 2. The read value acquisition unit 11 is realized, for example, by a program being executed by the CPU 501 shown in FIG.

[0071] The prediction model creation unit 12 is a functional unit that creates a neighborhood color change prediction model for predicting color values ​​from color gradation values ​​(CMY values) based on the color values ​​acquired by the read value acquisition unit 11. The prediction model creation unit 12 creates, for example, a model for converting color values ​​using a matrix or polynomial as the neighborhood color change prediction model. Note that the prediction model creation unit 12 may also create a neighborhood color change prediction model as a learning model through a learning process based on supervised learning or the like. The prediction model creation unit 12 creates a neighborhood color change prediction model for each gradation value of a target gray, which will be described later, and stores the model in the storage unit 14. The prediction model creation unit 12 is realized, for example, by a program executed by the CPU 501 shown in FIG. 6.

[0072] The target color acquisition unit 13 is a functional unit that acquires color values ​​of the target color of each CMY single color (target single color) and the target color of gray, which is a mixed color (target gray, target mixed color), from the color values ​​acquired by the read value acquisition unit 11. The target color acquisition unit 13 stores the acquired color values ​​of the target single color and target gray in the memory unit 14. The target color acquisition unit 13 is realized, for example, by a program being executed by the CPU 501 shown in FIG.

[0073] The storage unit 14 is a functional unit that stores the neighborhood color change prediction model, various TRCs, etc. The storage unit 14 is realized by the RAM 503 or the auxiliary storage device 505 shown in FIG.

[0074] The correction target color acquisition unit 20 is a functional unit that acquires the color values ​​of patches corresponding to updated gray, which will be described later, from the color values ​​acquired from the chart by the read value acquisition unit 11. The correction target color acquisition unit 20 is realized, for example, by the CPU 501 shown in FIG.

[0075] The TRC generation unit 15 is a functional unit that generates a monochrome TRC based on the color values ​​of the target monochrome color acquired by the target color acquisition unit 13 and the gradation values ​​of the target monochrome color stored in the memory unit 14. The TRC generation unit 15 also generates a monochrome TRC with gray correction based on the gradation values ​​of the target gray stored in the memory unit 14, the color values ​​of the updated gray acquired by the correction target color acquisition unit 20, and a neighborhood color change prediction model. Details of the monochrome TRC and the monochrome TRC with gray correction will be described later. The monochrome TRC is a one-dimensional conversion curve that converts input gradation values ​​into output gradation values, as will be described later. The updated gray indicates a gray that has changed from the target gray due to changes over time or environmental changes. The TRC generation unit 15 is implemented, for example, by a program executed by the CPU 501 shown in FIG. 6.

[0076] The chart generation unit 16 is a functional unit that acquires the color value of the target gray, creates a neighborhood color change prediction model, and generates a chart image required for gray correction processing. The chart generation unit 16 is realized, for example, by the CPU 501 shown in FIG. 6 executing a program.

[0077] The image input unit 17 is a functional unit that inputs image data transmitted from the user PC 3 via the network I / F 509. The image input unit 17 is realized, for example, by the CPU 501 shown in FIG.

[0078] The image processing unit 18 is a functional unit that uses TRC (monochrome TRC, monochrome TRC with gray correction) to convert the image data input by the image input unit 17 and the gradation values ​​in the chart image generated by the chart generation unit 16 into gradation values ​​in a format for printing out by the image forming apparatus 2. The image processing unit 18 is realized, for example, by the CPU 501 shown in FIG. 6 executing a program.

[0079] The image output unit 19 is a functional unit that outputs the data that has been image-processed by the image processing unit 18 to the image forming apparatus 2 via the network I / F 509 for printing. The image output unit 19 is realized, for example, by the CPU 501 shown in FIG. 6 executing a program.

[0080] The tolerance setting unit 21 is a functional unit that sets a value (color difference tolerance) that is allowed as the color difference between the updated gray and Lab value and the target gray and Lab value in response to operational inputs via the keyboard 511 and mouse 512. In other words, the color difference tolerance that is set is a value for each paper type. The tolerance setting unit 21 stores the set color difference tolerance in the memory unit 14. The tolerance setting unit 21 is realized, for example, by the CPU 501 shown in FIG. 6 executing a program.

[0081] The threshold calculation unit 22 is a functional unit that calculates a threshold value (hereinafter, sometimes referred to as a modified threshold value) for determining whether correction should be performed, using a conversion formula or conversion table for converting Lab values ​​to RGB values ​​from the color difference tolerance value set by the tolerance setting unit 21. In other words, the calculated modified threshold value is a value for each paper type. The threshold calculation unit 22 stores the calculated modified threshold value in the memory unit 14. The threshold calculation unit 22 is realized, for example, by the CPU 501 shown in FIG. 6 executing a program.

[0082] The display control unit 23 is a functional unit that controls the display operation of the display 508. The display control unit 23 causes the display 508 to display, for example, the monochrome TRC generated by the TRC generation unit 15 and the correction content generated in the process of generating the monochrome TRC with gray correction. The display control unit 23 is realized, for example, by the CPU 501 shown in FIG. 6 executing a program.

[0083] Note that the read value acquisition unit 11, prediction model creation unit 12, target color acquisition unit 13, correction target color acquisition unit 20, TRC generation unit 15, chart generation unit 16, image input unit 17, image processing unit 18, image output unit 19, tolerance setting unit 21, threshold calculation unit 22, and display control unit 23 of the image processing device 1 shown in Fig. 9 are not limited to being realized by a program executed by the CPU 501 shown in Fig. 6. For example, they may be realized by hardware such as an integrated circuit, or by a combination of software and hardware.

[0084] Furthermore, the functional units of the image processing device 1 shown in Fig. 9 are conceptual representations of functions, and are not limited to such configurations. For example, the multiple functional units illustrated as independent functional units in the image processing device 1 shown in Fig. 9 may be configured as a single functional unit. On the other hand, the function of a single functional unit in the image processing device 1 shown in Fig. 9 may be divided into multiple units and configured as multiple functional units. Furthermore, the functional units of the image processing device 1 do not need to be configured as distinct software modules as shown in Fig. 9, and it is sufficient that the functions of the functional units as a whole are realized by executing a program in the image processing device 1.

[0085] (Image processing device target gray and neighboring gray acquisition process) FIG. 10 is a flowchart showing an example of the flow of target gray and neighboring gray acquisition processing in the image processing device according to the first embodiment. FIG. 11 is a diagram showing an example of a target and neighboring chart in the first embodiment. FIG. 12 is a diagram showing an example of the gradation values ​​and color values ​​of the target gray, as well as color difference tolerances and correction thresholds. FIG. 13 is a diagram showing an example of the gradation values ​​and color values ​​of neighboring gray. FIG. 14 is a diagram explaining the operation of a neighboring color change prediction model. The target gray and neighboring gray acquisition processing in the image processing device 1 according to this embodiment will be explained with reference to FIGS. 10 to 14.

[0086] <Step S11> First, the TRC generation unit 15 of the image processing device 1 generates (newly creates) a monochromatic TRC by the monochromatic calibration described above. This new monochromatic TRC creation corresponds to the "first state" of the present invention. Then, the TRC generation unit 15 stores the generated monochromatic TRC in the storage unit 14. Note that this embodiment is based on the premise that the gradation values ​​have been subjected to gradation correction using the monochromatic TRC. Therefore, the implementation format of the monochromatic calibration is not particularly limited. Then, the process proceeds to step S12.

[0087] <Step S12> Next, the chart generating unit 16 of the image processing device 1 generates a target / neighborhood chart 60 (an example of a first chart) as shown in FIG.

[0088] As shown in FIG. 11, the target / nearby chart 60 includes a patch group 61 and a patch group 62. The patch group 61 is a group of multiple patches 61a of target gray that have been printed out using arbitrary non-zero C=M=Y gradation values ​​and a gradation value of K=0. The gradation values ​​of these patches 61a correspond to the "first gradation value" of the present invention. The patch group 62 is a collection of multiple nearby gray patches 62a that have been printed out using gradation values ​​(second gradation values) obtained by arbitrarily modulating the gradation values ​​of one or more of C, M, and Y within a predetermined color gamut range, relative to the gradation values ​​of each patch 61a in the patch group 61. For example, if a certain patch 61a is a patch with C=M=Y=10[%], the patch group 62a corresponding to that patch 61a is a group of nearby gray patches in which gradation values ​​are assigned within the range of ±3[%] for C, ±4[%] for M, and ±5[%] for Y, relative to C=M=Y=10[%]. Therefore, although the colors of the patches included in each patch group 62a are shown with the same pattern in Fig. 11, in detail they are patches with different gradation values ​​within the above-mentioned ranges.

[0089] The placement of each patch on the target / neighborhood chart 60 is not limited, and it may be placed in a location where the color values ​​of the target gray and neighboring gray can be measured with high accuracy.

[0090] Next, the image processing unit 18 of the image processing device 1 performs color conversion on the target and proximity chart 60 generated by the chart generation unit 16 using the single-color TRC generated by the TRC generation unit 15. The image output unit 19 of the image processing device 1 causes the image forming device 2 to print out the target and proximity chart 60 after color conversion. The reading device 5 then performs a reading process on the printed out target and proximity chart 60. The read value acquisition unit 11 of the image processing device 1 then acquires the color values ​​(RGB values) of each patch of the target and proximity chart 60 read by the reading device 5. Then, the process proceeds to step S13.

[0091] <Step S13> The target color acquisition unit 13 of the image processing device 1 acquires the color value (first color value) of one target gray (hereinafter referred to as the target target gray) from the color values ​​of each patch of the target and neighborhood chart 60 acquired by the read value acquisition unit 11, and stores this in the memory unit 14 as a color value corresponding to the gradation value of the target target gray. Here, Fig. 12 shows an example of the color value and gradation value of each target gray acquired by the read value acquisition unit 11. Then, the process proceeds to step S14.

[0092] <Step S14> Next, the prediction model creation unit 12 of the image processing device 1 acquires the color value (fourth color value) of the neighborhood gray of the patch group 62a corresponding to the target target gray from the color values ​​of each patch of the target / neighborhood chart 60 acquired by the read value acquisition unit 11, and stores this in the memory unit 14. Here, Fig. 13 shows an example of the gradation value of the target target gray, and the gradation value and color value of the neighborhood gray corresponding to the target target gray. Then, the process proceeds to step S15.

[0093] <Step S15> Then, the prediction model creation unit 12 creates a neighborhood color change prediction model corresponding to the target gray based on the correspondence between the gradation value and color value of the target gray and the correspondence between the gradation value and color value of multiple neighborhood grays corresponding to the target gray, and stores the model in the memory unit 14.

[0094] Here, the operation of the neighborhood color change prediction model is shown in Figure 14. The neighborhood color change prediction model is a model that takes gradation values ​​C, M, and Y as input and outputs color values ​​(RGB values) that are predicted to be reproduced by the image forming device 2. As a function to realize the neighborhood color change prediction model, a general function for a color change prediction model can be used, such as a multiple regression equation, a neural network, or interpolation using a direct lookup table.

[0095] Then, the process proceeds to step S16.

[0096] <Step S16> The prediction model creation unit 12 determines whether or not neighborhood color change prediction models corresponding to all target grays have been created. If neighborhood color change prediction models corresponding to all target grays have been created (step S16: Yes), the process proceeds to step S18, and if not (step S16: No), the process proceeds to step S17.

[0097] <Step S17> The target color acquisition unit 13 determines one different target gray from the color values ​​of each patch of the target / nearby chart 60 acquired by the read value acquisition unit 11 as the target target gray, acquires the color value of the target target gray, and stores it in the memory unit 14 as the color value corresponding to the gradation value of the target target gray. Then, the process returns to step S14.

[0098] <Step S18> The tolerance setting unit 21 sets a value (color difference tolerance) that is permissible as the color difference between the updated gray and the Lab value, and the target gray and the Lab value, in response to user input via the keyboard 511 and the mouse 512. The tolerance setting unit 21 stores the set color difference tolerance in the storage unit 14. Here, Fig. 12 shows an example of the color difference tolerance that has been set corresponding to the gradation value and color value of each target gray. Then, the process proceeds to step S19.

[0099] <Step S19> The threshold calculation unit 22 then calculates a correction threshold for determining whether correction is necessary, using a conversion formula or conversion table for converting Lab values ​​to RGB values ​​from the color difference tolerance set by the tolerance setting unit 21. The threshold calculation unit 22 stores the calculated correction threshold in the memory unit 14. FIG. 12 shows an example of a correction threshold calculated for each target gray gradation value, color value, and color difference tolerance. The conversion formula and conversion table may be unique, or the conversion table or the terms of the conversion formula may be switched using paper information such as the paper type (e.g., coated paper or uncoated paper) or paper thickness of the target / neighborhood chart 60. While only one color difference tolerance and one correction threshold are set and calculated in the above example, they may also be set and calculated individually for each target gray. The target gray / neighborhood gray acquisition process then ends.

[0100] (Gray correction processing in image processing devices) FIG. 15 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the first embodiment. FIG. 16 is a diagram showing an example of an updated gray chart in the first embodiment. FIG. 17 is a flowchart showing an example of the flow of corrected gray gradation value calculation processing in the image processing device according to the first embodiment. FIG. 18 is a diagram illustrating the calculation process of corrected gray gradation value calculation processing in the image processing device according to the first embodiment. FIG. 19 is a diagram showing an example of target gray gradation values ​​and corrected gradation values. The flow of gray correction processing in the image processing device 1 according to this embodiment will be described with reference to FIGS. 15 to 19.

[0101] <Step S21> After a sufficient amount of time has passed since the above-described process for creating a new monochromatic TRC, or the environment has changed, and it is assumed that the colors printed on the recording medium have changed, the following process is executed in response to a user operation. First, the TRC generation unit 15 of the image processing device 1 regenerates a monochromatic TRC using the above-described monochromatic calibration. Then, the TRC generation unit 15 stores the generated monochromatic TRC in the storage unit 14 and updates it. This update of the monochromatic TRC corresponds to the "second state" of the present invention. Then, the process proceeds to step S22.

[0102] <Step S22> Next, the chart generating unit 16 of the image processing device 1 generates an updated gray chart 70 (an example of a second chart) as shown in FIG.

[0103] 16, the updated gray chart 70 includes a patch group 71. The patch group 71 is a group of multiple patches 71a of updated gray that are printed out using the same gradation values ​​as the patches 61a of the patch group 61 of the target / proximity chart 60 shown in FIG.

[0104] The placement of each patch on the updated gray chart 70 is not limited, and it is sufficient that the patches are placed in a location where the color value of the updated gray can be measured with high accuracy.

[0105] Next, the image processing unit 18 of the image processing device 1 performs color conversion on the updated gray chart 70 generated by the chart generation unit 16 using the single-color TRC generated by the TRC generation unit 15. The image output unit 19 of the image processing device 1 causes the image forming device 2 to print out the updated gray chart 70 after color conversion. The reading device 5 then performs a reading process on the printed out updated gray chart 70. Then, the read value acquisition unit 11 of the image processing device 1 acquires the color values ​​of each patch 71a of the updated gray chart 70 read by the reading device 5. Then, the process proceeds to step S23.

[0106] <Step S23> The correction target color acquisition unit 20 of the image processing device 1 acquires the color value (second color value) of one updated gray (hereinafter referred to as target updated gray) from the color values ​​of the patches 71a of the updated gray chart 70 acquired by the read value acquisition unit 11. Then, the process proceeds to step S24.

[0107] <Step S24> The TRC generation unit 15 of the image processing device 1 reads out the target gray corresponding to the target updated gray, that is, the color values ​​and gradation values ​​of the target gray that are the same as the gradation values ​​of the target updated gray, from the storage unit 14. Then, the process proceeds to step S25.

[0108] <Step S25> The TRC generation unit 15 calculates the color difference (RGB color difference) between the color value of the target updated gray acquired by the correction target color acquisition unit 20 and the color value of the target updated gray corresponding to the target updated gray read from the storage unit 14. Then, the process proceeds to step S26.

[0109] <Step S26> The TRC generation unit 15 determines whether the calculated color difference (RGB color difference) is equal to or greater than the modified threshold calculated by the threshold calculation unit 22. If the color difference is equal to or greater than the modified threshold (step S26: Yes), the process proceeds to step S27, and if the color difference is less than the modified threshold (step S26: No), the process proceeds to step S28.

[0110] <Step S27> 17, the TRC generation unit 15 calculates the corrected gradation value of the target gray corresponding to the target updated gray, and stores the calculated corrected gradation value of the target gray in the storage unit 14. The processing of steps S271 to S279 will be described below.

[0111] <<Step S271>> The TRC generation unit 15 reads out the neighborhood color change prediction model corresponding to the target gray corresponding to the target updated gray from the storage unit 14. Then, the process proceeds to step S272.

[0112] <<Step S272>> Next, the TRC generating unit 15 selects one arbitrary gradation value (Cα, Mα, Yα) within the gradation range of the acquired neighborhood color change prediction model, and then proceeds to step S273.

[0113] <<Step S273>> As shown in Fig. 18, the TRC generation unit 15 calculates model predicted color values ​​(R1, G1, B1) (first predicted color values), which are color values ​​predicted from the gradation values ​​(Ct, Mt, Yt) of the target updated gray using the acquired neighborhood color change prediction model. Furthermore, as shown in Fig. 18, the TRC generation unit 15 calculates model predicted color values ​​(R2, G2, B2) (second predicted color values), which are color values ​​predicted from the gradation values ​​(Cα, Mα, Yα), using the acquired neighborhood color change prediction model from the selected gradation values ​​(Cα, Mα, Yα). Then, the process proceeds to step S274.

[0114] <<Step S274>> Next, the TRC generator 15 calculates the difference values ​​(ΔR12, ΔG12, ΔB12) = (R2 - R1, G2 - G1, B2 - B1) between the model predicted color values ​​(R1, G1, B1) and the model predicted color values ​​(R2, G2, B2), as shown in Fig. 18. Then, the process proceeds to step S275.

[0115] <<Step S275>> 18, the TRC generation unit 15 calculates a predicted color value (Rm+ΔR12, Gm+ΔG12, Bm+ΔB12) by adding the calculated difference values ​​(ΔR12, ΔB12, ΔB12) to the color values ​​(Rm, Gm, Bm) of the target updated gray acquired by the correction target color acquisition unit 20. Then, the process proceeds to step S276.

[0116] <<Step S276>> 18, the TRC generation unit 15 calculates the color difference (RGB color difference) between the calculated predicted color values ​​(Rm+ΔR12, Gm+ΔG12, Bm+ΔB12) and the color values ​​of the target gray (Rt, Gt, Bt) read from the storage unit 14. Then, the process proceeds to step S277.

[0117] <<Step S277>> The TRC generation unit 15 determines whether the calculated color difference (RGB color difference) is equal to or less than the modified threshold calculated by the threshold calculation unit 22. If the color difference is equal to or less than the modified threshold (step S277: Yes), the process proceeds to step S278, and if the color difference exceeds the modified threshold (step S277: No), the process proceeds to step S279.

[0118] <<Step S278>> The TRC generation unit 15 calculates the selected gradation values ​​(Cα, Mα, Yα) as corrected gradation values ​​of the target gray corresponding to the target updated gray, and stores the calculated gradation values ​​of the corrected gray corresponding to the target gray in the storage unit 14. In other words, the TRC generation unit 15 corrects the gradation values ​​so that the target updated gray based on the gradation values ​​of the target gray in the "second state" becomes the target gray. More specifically, the TRC generator 15 calculates the corrected value for the gradation values ​​(Ct, Mt, Yt) by adding the difference between the model predicted color values ​​(R1, G1, B1) predicted from the gradation values ​​of the target gray and the model predicted color values ​​(R2, G2, B2) predicted from the selected gradation values ​​(Cα, Mα, Yα) to the color values ​​of the target updated gray (Rm, Gm, Bm) such that the difference between this value and the color values ​​of the target gray (Rt, Gt, Bt) is equal to or less than the correction threshold.Then, the process proceeds to step S29.

[0119] <<Step S279>> The TRC generation unit 15 selects one gradation value (Cα, Mα, Yα) that is different from the already selected values ​​within the gradation range of the acquired neighborhood color change prediction model, and then returns to step S273.

[0120] <Step S28> The TRC generation unit 15 calculates the tone value of the target updated gray (i.e., the tone value of the corresponding target gray) as a corrected tone value corresponding to the target gray, and stores it in the storage unit 14. In other words, the TRC generation unit 15 does not correct the tone value of the target gray. Then, the process proceeds to step S29.

[0121] <Step S29> The TRC generation unit 15 determines whether or not the calculation of the color difference between the color value of the corresponding target gray and the determination using the correction threshold (processing of steps S24 to S28) for all updated grays has been completed. If the calculation has been completed (step S29: Yes), the display control unit 23 of the image processing device 1 displays, for example, the corrected gradation value of the target gray calculated by the TRC generation unit 15 on the display 508. Here, FIG. 19 shows an example of the gradation value of the target gray and the corrected gradation value. In the example shown in FIG. 19, for the target gray with a gradation value of C=M=Y=20[%], it is determined in step S26 that the color difference is less than the correction threshold, and therefore no correction is required for the gradation value of the target gray, and the corrected gradation value is also C=M=Y=20[%]. Then, the gray correction process ends. On the other hand, if the calculation has not been completed (step S29: No), the process proceeds to step S30.

[0122] <Step S30> The correction target color acquisition unit 20 acquires, as a new target updated gray color value, one updated gray color value different from the already acquired color values ​​of the patches 71a of the updated gray chart 70 acquired by the read value acquisition unit 11. Then, the process returns to step S24.

[0123] When the gray correction process is completed, the TRC generation unit 15 generates a monochrome TRC (monochrome TRC with gray correction) that reflects the corrected gradation values ​​calculated in steps S27 and S28 for the monochrome TRC generated in step S21, and updates the monochrome TRC already stored in the storage unit 14. Thereafter, the image processing unit 18 uses the monochrome TRC (monochrome TRC with gray correction) to execute a calibration process, thereby converting the input gradation values ​​into output gradation values.

[0124] As described above, in the image processing device 1 according to this embodiment, the target color acquisition unit 13 acquires, at the time of new creation (first state), the device-dependent first color value of the patch corresponding to the gradation value read by the reading device 5 provided in the image forming device 2 from the target neighborhood chart 60 printed out from the image forming device 2 based on the gradation value of the target gray, and the correction target color acquisition unit 20 acquires, at the time of update (second state), the device-dependent second color value of the patch corresponding to the gradation value read by the reading device 5 from the updated gray chart 70 printed out from the image forming device 2 based on the gradation value of the target gray, and if the color difference between the second color value and the first color value is equal to or greater than the correction threshold set as the device-dependent color difference corresponding to the device-independent color difference (color difference tolerance) that is allowable for the target gray, the TRC generation unit 15 corrects the gradation value so that the updated gray based on the gradation value in the second state becomes the target gray. This eliminates the need for color measurement using an external colorimeter, reducing downtime and improving color matching accuracy. Furthermore, because the correction threshold is used to determine whether correction should be performed, it is possible to prevent overcorrection from adversely affecting accuracy.

[0125] Furthermore, in the image processing device 1 according to this embodiment, a device-independent color difference tolerance that is allowable for target gray is set for each paper type in response to an operation on the input device, and the threshold calculation unit 22 calculates a device-dependent correction threshold from the color difference tolerance set by the tolerance setting unit 21. This makes it possible to simply set a color difference tolerance (and thus a correction threshold) for each paper type, eliminating the need for color measurement using an external colorimeter.

[0126] [Second embodiment] The information processing system 100 according to the second embodiment will be described, focusing on differences from the information processing system 100 according to the first embodiment. In the first embodiment, the operation of determining whether correction should be performed using a correction threshold (threshold for RGB color differences) calculated based on a color difference tolerance set for each paper type was described. In the first embodiment, this would not be a problem if the user could set an optimal color difference tolerance for each paper type. However, setting an appropriate color difference tolerance may be difficult if the user is unable to determine the appropriate color difference tolerance. Furthermore, since the color difference tolerance varies depending on the paper type, it is necessary to store a correction threshold for each paper type. This is not desirable from an operational standpoint, in terms of the storage capacity of the storage unit 14. Therefore, it would be ideal to be able to perform a determination using a single threshold regardless of the paper type. Therefore, in the second embodiment, the operation of acquiring the RGB value of the paper white of the paper and using it to normalize the read RGB value of each gray patch is described. This solves the problem that the RGB color differences corresponding to a certain Lab color difference differ depending on the paper type, making it possible to determine whether correction should be performed using a single threshold for any paper type.

[0127] (Configuration and operation of functional blocks of image processing device) Fig. 20 is a diagram showing an example of the configuration of functional blocks of an image processing device according to the second embodiment. The configuration and operation of the functional blocks of an image processing device 1a according to this embodiment will be described with reference to Fig. 20.

[0128] 20, the image processing device 1a includes a read value acquisition unit 11, a prediction model creation unit 12 (an example of a fourth acquisition unit), a target color acquisition unit 13 (an example of a first acquisition unit), a paper whiteness acquisition unit 24 (an example of a third acquisition unit), a memory unit 14, a TRC generation unit 15 (an example of a correction unit), a chart generation unit 16, an image input unit 17, an image processing unit 18, an image output unit 19, a correction target color acquisition unit 20 (an example of a second acquisition unit), and a display control unit 23. The operations of the read value acquisition unit 11, the prediction model creation unit 12, the target color acquisition unit 13, the memory unit 14, the TRC generation unit 15, the chart generation unit 16, the image input unit 17, the image processing unit 18, the image output unit 19, the correction target color acquisition unit 20, and the display control unit 23 are the same as those described in the first embodiment.

[0129] The paper white acquisition unit 24 is a functional unit that acquires color values ​​of a predetermined paper white reading area (color values ​​of paper white) from color values ​​acquired for a target line / neighborhood chart 60a (described later) by the read value acquisition unit 11. The paper white acquisition unit 24 stores the acquired color values ​​of paper white in the memory unit 14. The paper white acquisition unit 24 is realized, for example, by a program being executed by the CPU 501 shown in FIG.

[0130] Note that the read value acquisition unit 11, prediction model creation unit 12, target color acquisition unit 13, correction target color acquisition unit 20, TRC generation unit 15, chart generation unit 16, image input unit 17, image processing unit 18, image output unit 19, display control unit 23, and paper white acquisition unit 24 of the image processing device 1a shown in Fig. 20 are not limited to being realized by a program executed by the CPU 501 shown in Fig. 6. For example, they may be realized by hardware such as an integrated circuit, or by a combination of software and hardware.

[0131] Furthermore, the functional units of the image processing device 1a shown in FIG. 20 are conceptual representations of functions, and are not limited to such configurations. For example, the multiple functional units illustrated as independent functional units in the image processing device 1a shown in FIG. 20 may be configured as a single functional unit. On the other hand, the function of a single functional unit in the image processing device 1a shown in FIG. 20 may be divided into multiple units and configured as multiple functional units. Furthermore, the functional units of the image processing device 1a do not need to be configured as distinct software modules as shown in FIG. 20, and it is sufficient that the functions of the functional units as a whole are realized by executing a program in the image processing device 1a.

[0132] (Image processing device target gray and neighboring gray acquisition process) Fig. 21 is a flowchart showing an example of the flow of target gray and neighboring gray acquisition processing in an image processing device according to the second embodiment. Fig. 22 is a diagram showing an example of a target and neighboring chart in the second embodiment. Fig. 23 is a diagram showing an example of gradation values ​​and color values ​​of neighboring gray and paper white. The target gray and neighboring gray acquisition processing in the image processing device 1a according to this embodiment will be described with reference to Figs. 21 to 23.

[0133] <Step S41> First, the TRC generation unit 15 of the image processing device 1a generates (newly creates) a monochromatic TRC by the monochromatic calibration described above. This newly created monochromatic TRC corresponds to the "first state" of the present invention. Then, the TRC generation unit 15 stores the generated monochromatic TRC in the storage unit 14. Note that this embodiment is based on the premise that the gradation values ​​have been subjected to gradation correction by the monochromatic TRC. Therefore, the implementation format of the monochromatic calibration is not particularly limited. Then, the process proceeds to step S42.

[0134] <Step S42> Next, the chart generating unit 16 of the image processing device 1a generates a target / neighborhood chart 60a (an example of a first chart) as shown in FIG.

[0135] 22, the target and vicinity chart 60a includes a patch group 61, a patch group 62, and a paper white reading area 63. The patch group 61 and the patch group 62 are as described in the first embodiment above.

[0136] The paper white reading area 63 is an area of ​​paper white (i.e., gradation values ​​(C, M, Y, K) = (0, 0, 0, 0)) where no patches are printed and which is the same size as each of the patches that make up the patch group 61 and the patch group 62. Note that the position and size of the paper white reading area 63 are not limited, and it may be set in an area where the RGB values ​​can be read with high accuracy. Alternatively, multiple paper white reading areas may be set, and their average values ​​may be used as the RGB values ​​of paper white.

[0137] The placement of each patch on the target / neighborhood chart 60a is not limited, and it may be placed in a location where the color values ​​of the target gray and neighboring gray can be measured with high accuracy.

[0138] Next, the image processing unit 18 of the image processing device 1a performs color conversion on the target and proximity chart 60a generated by the chart generation unit 16 using the monochrome TRC generated by the TRC generation unit 15. The image output unit 19 of the image processing device 1 causes the image forming device 2 to print out the color-converted target and proximity chart 60a. The reading device 5 performs a reading process on the printed out target and proximity chart 60. Then, the read value acquisition unit 11 of the image processing device 1a acquires the color values ​​(RGB values) of each patch of the target and proximity chart 60a and the paper white reading area 63 read by the reading device 5. Then, the process proceeds to step S43.

[0139] <Steps S43 to S47> The processing of steps S43 to S47 is the same as the processing of steps S13 to S17 shown in Fig. 10. Here, Fig. 23 shows an example of the gradation values ​​of the target target gray and paper white, and the color values ​​of the target target gray and paper white. Also, in step S46, if neighborhood color change prediction models corresponding to all target grays have been created (step S46: Yes), the process proceeds to step S48, and if not (step S46: No), the process proceeds to step S47.

[0140] <Step S48> The paper white acquisition unit 24 of the image processing device 1a acquires the color value (paper white color value) (third color value) of the paper white reading area 63 from the color values ​​acquired for the target and neighboring chart 60a by the read value acquisition unit 11. Then, the paper white acquisition unit 24 stores the acquired paper white color value in the memory unit 14. Then, the target gray and neighboring gray acquisition process ends.

[0141] (Gray correction processing in image processing devices) 24 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the second embodiment. The flow of gray correction processing in the image processing device 1a according to this embodiment will be described with reference to FIG.

[0142] <Steps S51 to S53> The processes in steps S51 to S53 are the same as the processes in steps S21 to S23 shown in Fig. 15. Then, the process proceeds to step S54.

[0143] <Step S54> The TRC generating unit 15 of the image processing device 1a reads out the paper white color value read for the target / proximity chart 60a from the storage unit 14. Then, the process proceeds to step S55.

[0144] <Step S55> The TRC generation unit 15 reads the target gray corresponding to the target updated gray, that is, the color values ​​and gradation values ​​of the target gray that are the same as the gradation values ​​of the target updated gray, from the storage unit 14. Then, the process proceeds to step S56.

[0145] <Step S56> The TRC generation unit 15 performs normalization processing on the color value of the target updated gray and the color value of the target gray corresponding to the target updated gray, using the color value of paper white as a reference. Specifically, the TRC generation unit 15 normalizes the color value of the target updated gray and the color value of the target gray corresponding to the target updated gray (RGB gray ) to the paper white color value (RGB white ) is used as the basis for normalization, and the normalized color value (RGB norm ) is obtained.

[0146] RGB norm =255×RGB gray / RGB white ···(1)

[0147] As a result, the color values ​​of the target updated gray and the target gray are normalized to values ​​between 0 and 255 such that the color value of paper white has a maximum value of 255. Then, the process proceeds to step S57.

[0148] <Step S57> The TRC generation unit 15 calculates the color difference (hereinafter sometimes referred to as the normalized color difference) between the color value of the normalized target updated gray and the color value of the normalized target gray corresponding to the target updated gray, and then proceeds to step S58.

[0149] <Step S58> The TRC generation unit 15 determines whether the calculated normalized color difference is equal to or greater than a predetermined threshold (modification threshold) set in advance. As described above, by normalizing the color values ​​of the updated gray and target gray using the color value (RGB value) of paper white, it becomes possible to standardize the RGB color difference corresponding to the Lab color difference as a normalized color difference for all paper types, thereby making it possible to determine whether correction should be performed using a single threshold (modification threshold).

[0150] Although normalization is performed using the color value (RGB value) of paper white, it is sufficient if normalization can be performed using a common standard for all paper types, and instead of the color value of paper white, normalization can also be performed using patches with specific gradation values ​​printed using, for example, the target / nearby chart 60a and the updated gray chart 70.

[0151] Furthermore, the paper white reading area 63 is set in the target / neighborhood chart 60a, and the updated gray is also normalized using the color value of the paper white in the paper white reading area 63 on the target / neighborhood chart 60a, but this is not limitative. For example, a paper white area equivalent to the paper white reading area 63 may also be set in the updated gray chart 70, and the updated gray may be normalized using the color value of the paper white in that paper white area on the updated gray chart 70.

[0152] In addition, although a threshold value (correction threshold value) for the normalized RGB color difference corresponding to the Lab color difference is set in advance, it is also possible for the user to set one color difference tolerance value for the Lab value to be applied to all paper types, and to use this color difference tolerance value to calculate and use the correction threshold value for the normalized color difference.

[0153] If the normalized color difference is equal to or greater than the modification threshold (step S58: Yes), the process proceeds to step S59, and if the normalized color difference is less than the modification threshold (step S58: No), the process proceeds to step S60.

[0154] <Steps S59 to S62> The processes in steps S59 to S62 are the same as the processes in steps S27 to S30 shown in Fig. 15. Then, the gray correction process ends.

[0155] As described above, in the image processing device 1a according to this embodiment, the paper-white acquisition unit 24 acquires the device-dependent color value of the paper white of the chart in the first or second state, and the TRC generation unit 15 normalizes the first and second color values ​​based on the paper-white color value, and performs correction if the color difference between the normalized second color value and the normalized first color value is equal to or greater than a preset correction threshold. This makes it possible to determine whether correction should be performed for any paper type using a single threshold, improving user convenience.

[0156] In the above-described embodiments, a neighborhood color change prediction model is created in the target gray / neighborhood gray acquisition process, but the present invention is not limited to this, and during gray correction processing, the updated gray chart 70 may be printed out to include the patch group 62, and a neighborhood color change prediction model may be created. Also, after performing correction processing once during gray correction processing, it may be determined by the above-described method whether or not to perform further correction processing, and then the updated gray chart 70 including the patch group 62 for creating a neighborhood color change prediction model may be output and correction may be performed.

[0157] Furthermore, each function of each of the above-described embodiments can be realized by one or more processing circuits. Here, the term "processing circuit" includes a processor programmed to execute each function by software, such as a processor implemented by an electronic circuit, as well as devices such as an ASIC, a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), and conventional circuit modules designed to execute each of the above-described functions.

[0158] The programs executed by the image processing devices 1 and 1a of the above-described embodiments may be provided by being pre-installed in a ROM or the like. The programs executed by the image processing devices 1 and 1a of the above-described embodiments may be provided as a computer program product by being recorded in an installable or executable file format on a computer-readable recording medium such as a CD-ROM (Compact Disc Read Only Memory), a flexible disk (FD), a CD-R (Compact Disk-Recordable), or a DVD (Digital Versatile Disk). The programs executed by the image processing devices 1 and 1a of the above-described embodiments may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. The programs executed by the image processing devices 1 and 1a of the above-described embodiments may be provided or distributed via a network such as the Internet.

[0159] Furthermore, the programs executed by the image processing devices 1 and 1a of the above-described embodiments are modularly structured to include the above-described functional units, and in actual hardware, the CPU (processor) reads the programs from the ROM and executes them, thereby loading the above-described functional units onto the main memory device and generating the functional units on the main memory device.

[0160] The aspects of the present invention are as follows. <1> An image processing device that performs color matching of tone values ​​of the same target color acquired in two different states, a first acquisition unit that acquires, in a first state, from a first chart printed out from an image forming apparatus based on a first gradation value of a target color mixture, a device-dependent first color value of a patch corresponding to the first gradation value read by a reading device provided in the image forming apparatus; a second acquisition unit that, in a second state different from the first state, acquires, from a second chart printed out from the image forming device based on the first gradation value, a device-dependent second color value of a patch corresponding to the first gradation value read by the reading device; a correction unit that corrects the first gradation value so that color mixing by the first gradation value in the second state becomes the target color mixing when the color difference between the second color value and the first color value is equal to or greater than a threshold value that is set as a device-dependent color difference that corresponds to a device-independent color difference that is allowable for the target color mixing; The image processing device is provided with: <2> The threshold value is set for each paper type. <1> 2 is an image processing device according to the first embodiment. <3> a setting unit that sets a device-independent color difference tolerance that is allowable for the target color mixture for each paper type in response to an operation on an input device; a calculation unit that calculates the device-dependent threshold value from the color difference tolerance value set by the setting unit; The said further comprising <2> 2 is an image processing device according to the first embodiment. <4> a third acquisition unit that acquires a device-dependent third color value of paper white of the chart in the first state or the second state; The correction unit normalizing the first color value and the second color value with respect to the third color value; When the color difference between the normalized second color value and the normalized first color value is equal to or greater than the preset threshold value, the correction is performed. <1> 2 is an image processing device according to the first embodiment. <5> The second state is a state resulting from a change over time or an environmental change from the first state. <1> ~ <4> 10. The image processing device according to claim 9, wherein: <6> a fourth acquisition unit that, in the first state or the second state, acquires fourth color values ​​of patches corresponding to the second gradation values ​​read by the reading device from a chart printed out from the image forming device based on a plurality of second gradation values ​​within a predetermined color gamut range including the first gradation value, the correction unit sets the second gradation value as the corrected value for the first gradation value such that a difference between a value obtained by adding a difference between a first predicted color value predicted from the first gradation value and a second predicted color value predicted from the second gradation value to the second color value and the first color value is equal to or less than the threshold value. <1> ~ <5> 10. The image processing device according to claim 9, wherein: <7> the device-dependent color values ​​are RGB values, The device-independent color values ​​are Lab values. <1> ~ <6> 10. The image processing device according to claim 9, wherein: <8> The target color mixture is gray obtained by mixing cyan, magenta, and yellow. <1> ~ <7> 10. The image processing device according to claim 9, wherein: <9> the image forming apparatus; The aforementioned <1> ~ <8> an image processing device according to any one of the preceding claims; It is an information processing system including: <10> An image processing method of an image processing device that performs color matching of gradation values ​​of the same target color acquired in two different states, a first acquisition step of acquiring, in a first state, from a first chart printed out from an image forming apparatus based on a first gradation value of a target color mixture, a device-dependent first color value of a patch corresponding to the first gradation value read by a reading device provided in the image forming apparatus; a second acquisition step of acquiring, in a second state different from the first state, device-dependent second color values ​​of patches corresponding to the first gradation values ​​read by the reading device from a second chart printed out from the image forming device based on the first gradation values; a correction step of correcting the first gradation value so that color mixture by the first gradation value in the second state becomes the target color mixture when the color difference between the second color value and the first color value is equal to or greater than a threshold value set as a device-dependent color difference corresponding to a device-independent color difference allowable for the target color mixture; An image processing method comprising: <11> A computer that performs color matching of the same target color gradation values ​​obtained under two different conditions. a first acquisition step of acquiring, in a first state, from a first chart printed out from an image forming apparatus based on a first gradation value of a target color mixture, a device-dependent first color value of a patch corresponding to the first gradation value read by a reading device provided in the image forming apparatus; a second acquisition step of acquiring, in a second state different from the first state, device-dependent second color values ​​of patches corresponding to the first gradation values ​​read by the reading device from a second chart printed out from the image forming device based on the first gradation values; a correction step of correcting the first gradation value so that color mixture by the first gradation value in the second state becomes the target color mixture when the color difference between the second color value and the first color value is equal to or greater than a threshold value set as a device-dependent color difference corresponding to a device-independent color difference allowable for the target color mixture; A program to execute. [Explanation of symbols]

[0161] 1, 1a Image processing device 2. Image forming device 3 User PC 5 Reading device 11 Reading acquisition unit 12 Prediction Model Creation Department 13 Target color acquisition section 14 Storage section 15 TRC generation section 16 Chart Generation Unit 17 Image input unit 18 Image processing section 19 Image output unit 20 Correction target color acquisition section 21 Tolerance setting section 22 Threshold calculation unit 23 Display control unit 24 Paper blank acquisition section 60, 60a Target and Nearby Chart 61 patches 61a patch 62, 62a patch group 63 Paper white reading area 70 Updated Gray Chart 71 Patches 71a patch 100 Information Processing Systems 300 Paper Tray 301 Conveyor roller 302 Intermediate transfer belt 303C, 303K, 303M, 303Y photoconductor drum 304 Transfer roller 305 Fuser roller 501 CPU 502 ROM 503 RAM 505 Auxiliary storage 506 Recording Media 507 Media Drive 508 Display 509 Network I / F 510 Bus Line 511 keyboard 512 Mouse 513 DVD 514 DVD drive 600 Controller 601 CPU 602 System Memory (MEM-P) 603 Northbridge (NB) 604a Southbridge (SB) 604b Network I / F 604c USB I / F 604d Centronics I / F 604e Sensor I / F 605 AGP 606 ASIC 607 Local Memory (MEM-C) 608 Auxiliary storage 610 Operation display section 620 FCU 631 Plotter 632 Scanner N Network [Prior art documents] [Patent documents]

[0162] [Patent Document 1] Japanese Patent Application Publication No. 2017-219753

Claims

1. An image processing device that performs color matching of tone values ​​of the same target color acquired in two different states, a first acquisition unit that acquires, in a first state, from a first chart printed out from an image forming apparatus based on a first gradation value of a target color mixture, a device-dependent first color value of a patch corresponding to the first gradation value read by a reading device provided in the image forming apparatus; a second acquisition unit that acquires, in a second state different from the first state, device-dependent second color values ​​of patches corresponding to the first gradation values ​​read by the reading device from a second chart printed out from the image forming device based on the first gradation values; a correction unit that corrects the first gradation value so that color mixing by the first gradation value in the second state becomes the target color mixing when the color difference between the second color value and the first color value is equal to or greater than a threshold value that is set as a device-dependent color difference that corresponds to a device-independent color difference that is allowable for the target color mixing; An image processing device comprising:

2. The image processing apparatus according to claim 1 , wherein the threshold value is set for each paper type.

3. a setting unit that sets a device-independent color difference tolerance that is allowable for the target color mixture for each paper type in response to an operation on an input device; a calculation unit that calculates the device-dependent threshold value from the color difference tolerance value set by the setting unit; The image processing device according to claim 2 , further comprising:

4. a third acquisition unit that acquires a device-dependent third color value of paper white of the chart in the first state or the second state, The correction unit normalizing the first color value and the second color value with respect to the third color value; The image processing device according to claim 1 , wherein the correction is performed when a color difference between the normalized second color value and the normalized first color value is equal to or greater than the preset threshold value.

5. 4. The image processing device according to claim 1, wherein the second state is a state resulting from a change from the first state over time or due to an environmental change.

6. a fourth acquisition unit that acquires, in the first state or the second state, fourth color values ​​of patches corresponding to the second gradation values ​​read by the reading device from a chart printed out from the image forming device based on a plurality of second gradation values ​​within a predetermined color gamut range including the first gradation value, The image processing device according to any one of claims 1 to 3, wherein the correction unit adds the difference between a first predicted color value predicted from the first gradation value and a second predicted color value predicted from the second gradation value to the second color value, and sets the second gradation value such that the difference between the second color value and the first color value is equal to or less than the threshold value as the corrected value for the first gradation value.

7. the device-dependent color values ​​are RGB values, 4. The image processing device according to claim 1, wherein the device-independent color values ​​are Lab values.

8. 4. The image processing device according to claim 1, wherein the target color mixture is gray obtained by mixing cyan, magenta, and yellow.

9. the image forming apparatus; The image processing device according to any one of claims 1 to 3; An information processing system including:

10. An image processing method for an image processing device that performs color matching of gradation values ​​of the same target color acquired in two different states, comprising: a first acquisition step of acquiring, in a first state, from a first chart printed out from an image forming apparatus based on a first gradation value of a target color mixture, a device-dependent first color value of a patch corresponding to the first gradation value read by a reading device provided in the image forming apparatus; a second acquisition step of acquiring, in a second state different from the first state, device-dependent second color values ​​of patches corresponding to the first gradation values ​​read by the reading device from a second chart printed out from the image forming device based on the first gradation values; a correction step of correcting the first gradation value so that color mixing by the first gradation value in the second state becomes the target color mixing when the color difference between the second color value and the first color value is equal to or greater than a threshold value set as a device-dependent color difference corresponding to a device-independent color difference that is allowable for the target color mixing; An image processing method comprising:

11. A computer that performs color matching of the same target color gradation values ​​obtained in two different states, a first acquisition step of acquiring, in a first state, from a first chart printed out from an image forming apparatus based on a first gradation value of a target color mixture, a device-dependent first color value of a patch corresponding to the first gradation value read by a reading device provided in the image forming apparatus; a second acquisition step of acquiring, in a second state different from the first state, device-dependent second color values ​​of patches corresponding to the first gradation values ​​read by the reading device from a second chart printed out from the image forming device based on the first gradation values; a correction step of correcting the first gradation value so that color mixing by the first gradation value in the second state becomes the target color mixing when the color difference between the second color value and the first color value is equal to or greater than a threshold value set as a device-dependent color difference corresponding to a device-independent color difference that is allowable for the target color mixing; A program to execute.

Images