Image processing device, 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 that can prevent an increase in load of colorimetric processing every time correction is performed, and can accurately perform correction of color mixture.SOLUTION: An image processing apparatus comprises: a first acquisition unit that acquires first colorimetric values from a chart that is printed out in a first state based on first gradation values; a second acquisition unit that acquires second colorimetric values from a chart that is printed out from an image forming apparatus in the first state for a second gradation value near part of the first gradation values, and from a chart that is printed out in a second state for a second gradation value near the rest of the first gradation values; a third acquisition unit that acquires a third colorimetric value in the second state; a first calculation unit that adds up the difference between the first colorimetric value and the second colorimetric value and the third colorimetric value to calculate a predicted colorimetric value; and a correction unit that, when the color difference between the predicted colorimetric value and the first colorimetric value is equal to or less than a threshold, corrects the first gradation values with the second gradation value as a correction value.SELECTED DRAWING: Figure 4

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] The color state of printed materials output from an image forming device changes over time, so color matching must be performed periodically. To do this, it is necessary to properly match gray, which is a mixture of C (cyan), M (magenta), and Y (yellow). When matching colors that change over time in mixed colors, it is necessary to output colors adjacent to the color being matched and obtain their color measurement values ​​in order to identify the direction of color matching. In this case, to ensure the accuracy of color matching, it is desirable to obtain the adjacent colors as close as possible to the time immediately before performing color matching.

[0003] As a color matching technique, in order to automatically and quickly perform gray balance adjustment, a technique has been disclosed in which color mixing characteristics are measured by outputting grays adjacent to the target gray during correction, and the result is used to correct the target gray to a target value (for example, Patent Document 1).

[0004] In addition, in order to accommodate fluctuations in gradation characteristics, a technology has been disclosed in which a predictive model created during proofreading is used to correct colorimetric values ​​obtained from an image during a print job to target values ​​at the time of proofreading (for example, Patent Document 2). Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 has the problem of requiring the user to measure the color of nearby grays each time correction is performed, which places a heavy burden on the user. Also, with the technology described in Patent Document 2, if the time of calibration is considered the past state and the time of print job processing is considered the current state, the colorimetric values ​​of the current state are adjusted to the target values ​​for the past state, which causes a problem of not being able to perform accurate correction if the color mixing characteristics change significantly between the time of calibration and the time of print job processing.

[0006] 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 suppress the load of color measurement processing from increasing with each correction and perform color mixing correction with high accuracy. [Means for solving the problem]

[0007] 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 colors of the same target gradation value obtained in two different states, the image processing device including: a first acquisition unit that, in a first state, acquires first colorimetric values ​​of target color mixture corresponding to each of the first gradation values ​​from a chart printed out from the image forming device based on one or more first gradation values; a second acquisition unit that acquires second colorimetric values ​​of neighboring color mixture from a chart printed out from the image forming device in the first state for second gradation values ​​neighboring at least some of the first gradation values, and from a chart printed out from the image forming device in a second state that is chronologically later than the first state for second gradation values ​​neighboring the remaining first gradation values ​​among the first gradation values; and a third acquisition unit that, in the second state, acquires third colorimetric values ​​of pre-correction color mixture corresponding to each of the first gradation values ​​from a chart printed out from the image forming device based on each of the first gradation values. a generating unit that generates a prediction model corresponding to each of the first gradation values ​​for predicting a colorimetric value from a gradation value based on the second gradation values ​​near some of the first gradation values ​​and the second gradation values ​​near the remaining first gradation values; a first predicted value predicted from the first gradation value using the prediction model corresponding to the first gradation value; and a second predicted value predicted from the second gradation value corresponding to the first gradation value using the prediction model. The difference between The aforementioned The present invention is characterized in that it comprises a first calculation unit that calculates a predicted colorimetric value of the target color mixture by adding the third colorimetric value corresponding to the first colorimetric value; a first determination unit that determines whether a color difference between the predicted colorimetric value and the first colorimetric value corresponding to the predicted colorimetric value is equal to or less than a first threshold value; a fourth acquisition unit that acquires the second gradation value of the second colorimetric value corresponding to the color difference as a correction value when the color difference is equal to or less than the first threshold value; and a correction unit that corrects the first gradation value using the correction value so that the colorimetric value of the pre-correction color mixture becomes the colorimetric value of the target color mixture. [Effects of the Invention]

[0008] According to the present invention, it is possible to suppress an increase in the load of color measurement processing each time correction is performed, and to perform color mixing correction with high accuracy. [Brief explanation of the drawings]

[0009] [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 an example of a hardware configuration of the image processing apparatus according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a hardware configuration of the image forming apparatus according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the functional block configuration of the image processing apparatus according to the first embodiment. [Figure 5] FIG. 5 is a diagram illustrating an outline of the gray correction process in the image processing device according to the first embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of the flow of image processing in the image processing apparatus according to the first embodiment. [Figure 7] FIG. 7 is a flowchart showing an example of the flow of target gray acquisition processing in the image processing apparatus according to the first embodiment. [Figure 8] FIG. 8 is a diagram showing an example of the gray correction chart in the first state of the first embodiment. [Figure 9] FIG. 9 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the first embodiment. [Figure 10] FIG. 10 is a diagram showing an example of the gray correction chart in the second state of the first embodiment. [Figure 11] FIG. 11 is a flowchart showing an example of the flow of the gray correction value calculation process of the image processing device according to the first embodiment. [Figure 12] FIG. 12 is a diagram illustrating a monochrome TRC for gray correction. [Figure 13]FIG. 13 is a diagram illustrating the process of calculating the gray correction value in the image processing device according to the first embodiment. [Figure 14] FIG. 14 is a diagram illustrating an example of the functional block configuration of the image processing apparatus according to the second embodiment. [Figure 15] FIG. 15 is a diagram illustrating an outline of the local variation difference calculation process in the image processing device according to the second embodiment. [Figure 16] FIG. 16 is a diagram showing an example of the gray correction chart in the first state of the second embodiment. [Figure 17] FIG. 17 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the second embodiment. [Figure 18] FIG. 18 is a diagram showing an example of the gray correction chart in the second state of the second embodiment. [Figure 19] FIG. 19 is a diagram illustrating an example of a prediction model update chart according to the second embodiment. [Figure 20] FIG. 20 is a flowchart showing an example of the flow of local variation difference calculation processing in the image processing apparatus according to the second embodiment. [Figure 21] FIG. 21 is a diagram illustrating a calculation process of a local variation difference in the image processing apparatus according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] 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.

[0011] [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.

[0012] As shown in Fig. 1, the information processing system 100 includes an image processing device 1, an image forming device 2, a user PC (Personal Computer) 3, and a colorimeter 4. Data communication between the devices is possible 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.

[0013] 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.

[0014] The image forming device 2 is a device that prints out based on image data that has been output from the user PC 3 and has been subjected to image processing by the image processing device 1.

[0015] The user PC 3 is an information processing device that transmits image data to be printed to the image processing device 1.

[0016] The colorimeter 4 is a device for measuring the color of a chart printed out by the image forming apparatus 2. The colorimeter 4 transmits colorimetric values, such as Lab values ​​in the L*a*b* color space, obtained by measuring the color of the chart to the image processing apparatus 1. The colorimeter 4 may be a device that automatically reads a printed version of a placed chart, or may be a device that reads a printed version of a chart by manual operation.

[0017] (Hardware configuration of image processing device) 2 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.

[0018] As shown in FIG. 2, 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.

[0019] 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.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 2 is an example, and it is not necessary to include all of the components, and other components may be included. The hardware configuration of the user PC 3 also conforms to the configuration shown in FIG.

[0028] (Hardware configuration of image forming device) 3 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.

[0029] As shown in FIG. 3, 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.

[0030] 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 .

[0031] The operation display unit 610 is, for example, a touch panel, and is a device that accepts input to the controller 600 (input function) and displays the status of the image forming device 2 (display function), and is directly connected to the ASIC (Application Specific Integrated Circuit) 606 described later.

[0032] The FCU 620 is a device that implements a fax function, and is connected to the ASIC 606 via, for example, a PCI bus.

[0033] The plotter 631 is a device that realizes a printing function and is connected to the ASIC 606 via, for example, a PCI bus. The scanner 632 is a device that realizes a scanner function and is connected to the ASIC 606 via, for example, a PCI bus.

[0034] The controller 600 has 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.

[0035] The CPU 601 is a computing device that performs overall control of the image forming apparatus 2, and is connected to a chip set consisting of a system memory 602, a north bridge 603, and a south bridge 604a, and is connected to other devices via this chip set.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] The local memory 607 is a memory used as an image buffer for copying and a code buffer.

[0042] The auxiliary storage device 608 is a storage device such as an HDD (Hard Disk Drive), SSD (Solid State Drive), SD (Secure Digital) card, or flash memory, and is a storage for storing image data, programs, font data, and forms, etc.

[0043] 3 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.

[0044] (Configuration and operation of functional blocks of image processing device) 4 is a diagram showing an example of the configuration of functional blocks of the image processing device 1 according to the first embodiment. The configuration and operation of the functional blocks of the image processing device 1 according to the present embodiment will be described with reference to FIG.

[0045] As shown in Figure 4, the image processing device 1 has a colorimetric value acquisition unit 11, a prediction model creation unit 12 (second acquisition unit, creation unit), a target acquisition unit 13 (first acquisition unit), a memory unit 14, a TRC (Tone Reproduction Curve) generation unit 15 (third acquisition unit, first calculation unit, first judgment unit, fourth acquisition unit), a chart image generation unit 16, an image input unit 17, an image processing unit 18 (correction unit), and an image output unit 19.

[0046] The colorimetric value acquisition unit 11 is a functional unit that acquires, via the network I / F 509, the colorimetric values ​​measured by the colorimeter 4 for a chart printed by the image forming apparatus 2. The colorimetric value acquisition unit 11 is realized, for example, by the CPU 501 shown in FIG.

[0047] The prediction model creation unit 12 is a functional unit that creates a prediction model for predicting colorimetric values ​​from the dot area percentage of a color based on the colorimetric values ​​acquired by the colorimetric value acquisition unit 11. For example, the prediction model creation unit 12 creates a 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 prediction model for each dot area percentage of a target gray, which will be described later, and stores the prediction model in the storage unit 14. Note that the dot area percentage may also be referred to as a device value. The prediction model creation unit 12 is realized, for example, by a program executed by the CPU 501 shown in FIG. 2.

[0048] The target acquisition unit 13 is a functional unit that acquires target single colors, which are target values ​​for each single color of CMY, and target gray, which is a target value for gray, which is a mixed color, from the colorimetric values ​​acquired by the colorimetric value acquisition unit 11. In this case, the target value is expressed, for example, as density or distance from paper white. The target acquisition unit 13 stores the acquired target single colors and target gray in the storage unit 14. The target acquisition unit 13 is realized, for example, by the CPU 501 shown in FIG. 2 executing a program.

[0049] The storage unit 14 is a functional unit that stores the prediction model, various TRCs, etc. The storage unit 14 is realized by the RAM 503 or the auxiliary storage device 505 shown in FIG.

[0050] The TRC generation unit 15 is a functional unit that generates a monochrome TRC based on the monochrome colorimetric values ​​acquired by the colorimetric value acquisition unit 11 and 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 pre-correction gray acquired by the colorimetric value acquisition unit 11 during correction, and the target gray and prediction model stored in the memory unit 14. Details of the monochrome TRC and the monochrome TRC with gray correction will be described later. Like the target gray, the pre-correction gray is expressed, for example, by density or distance from paper white. The TRC is a one-dimensional conversion curve that converts input gradations to output gradations. The TRC generation unit 15 is realized, for example, by a program executed by the CPU 501 shown in FIG. 2.

[0051] The chart image generation unit 16 is a functional unit that acquires target gray, creates a prediction model, and generates a chart image required for gray correction processing. The chart image generation unit 16 is realized, for example, by the CPU 501 shown in FIG. 2 executing a program.

[0052] Image input unit 17 is a functional unit that inputs image data transmitted from user PC 3 via network I / F 509. Image input unit 17 is realized, for example, by a program being executed by CPU 501 shown in FIG.

[0053] The image processing unit 18 is a functional unit that converts the image data input by the image input unit 17 and the input device values ​​in the chart image generated by the chart image generating unit 16 into device 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. 2 executing a program.

[0054] 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 based on the data. The image output unit 19 is realized, for example, by the CPU 501 shown in FIG. 2 executing a program.

[0055] Note that the colorimetric value acquisition unit 11, the prediction model creation unit 12, the target acquisition unit 13, the TRC generation unit 15, the chart image generation unit 16, the image input unit 17, the image processing unit 18, and the image output unit 19 of the image processing device 1 shown in Fig. 4 are not limited to being realized by the CPU 501 shown in Fig. 2 executing a program. For example, they may be realized by hardware such as an integrated circuit, or may be realized by a combination of software and hardware.

[0056] Furthermore, the functional units of the image processing device 1 shown in Fig. 4 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. 4 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. 4 may be divided into multiple units and configured as multiple functional units.

[0057] (Gray correction process overview) 5 is a diagram illustrating an outline of the gray correction process in the image processing device according to the first embodiment. With reference to FIG. 5, an outline of the gray correction process in the image processing device 1 according to this embodiment will be described.

[0058] In this embodiment, when target gray (an example of target color mixing) is acquired (hereinafter, this may be referred to as a first state), the image processing device 1 acquires target neighborhood gray (an example of neighborhood color mixing), which is gray that is neighboring the target gray. Then, using the acquired target neighborhood gray, the image processing device 1 creates a prediction model for predicting colorimetric values ​​such as Lab values ​​from the device values ​​of the gray.

[0059] As described above, the color state of the printed matter from the image forming apparatus 2 may change over time. For example, as shown in FIG. 5, the target gray to be corrected during correction (hereinafter, sometimes referred to as the second state) is referred to as pre-correction gray (an example of pre-correction color mixing). In this case, as shown in FIG. 5, the colorimetric values ​​(Lab values) of the pre-correction gray are (Lm, am, bm). Also, in this second state, the image processing apparatus 1 acquires the target neighborhood gray and creates a prediction model using the target neighborhood gray. The second state is a state that is later in time than the first state.

[0060] Then, when correcting the pre-correction gray corresponding to a specific target gray (second state), the image processing device 1 determines the colorimetric value predicted from the device value of the target gray using the prediction model as the predicted value (L1, a1, b1), and the colorimetric value predicted from the device value of a specific target-neighbor gray using the prediction model as the predicted value (L2, a2, b2). Here, low-gradation gray (e.g., device value of 10%) tends to have a large amount of change (deviation) over time between the first state and the second state, while high-gradation gray (e.g., device value of 20% or more) tends to have a small deviation between the first state and the second state. Therefore, when the target gray is high-gradation, the image processing device 1 assumes that the deviation is small and calculates the predicted values ​​(L1, a1, b1) and (L2, a2, b2) using the prediction model created in the first state. On the other hand, when the target gray is low gradation, the image processing device 1 assumes that the deviation is large and calculates the predicted values ​​(L1, a1, b1) and (L2, a2, b2) using the prediction model created in the second state, which is the time of correction. Therefore, in the first state, it is sufficient to create a prediction model corresponding to the high gradation of the target neighborhood gray, and in the second state, it is sufficient to create a prediction model corresponding to the low gradation of the target neighborhood gray.

[0061] Then, the image processing device 1 calculates a predicted Lab value (Lm+ΔL,am+Δa,bm+Δb) by adding the difference (ΔL,Δa,Δb)=(L2-L1,a2-a1,b2-b1) between the predicted value (L2,a2,b2) and the predicted value (L1,a1,b1) to the colorimetric value (Lm,am,bm) of the uncorrected gray. 1The image processing device 1 determines whether the color difference between the colorimetric value (Lt, at, bt) of the target gray in the current state and the predicted Lab value (Lm + ΔL, am + Δa, bm + Δb) is equal to or less than a predetermined threshold. For example, ΔE2000 may be used as the color difference. If the result of this determination is equal to or less than the threshold, the image processing device 1 acquires the device value (dot area ratio) of the specific target-neighbor gray in this case as a correction value for the specific target gray. That is, the correction is performed by using the prediction model to search for the target-neighbor gray that most closely matches the colorimetric value (Lt, at, bt) of the target gray when the difference (ΔL, Δa, Δb) between the predicted value (L2, a2, b2) of the target-neighbor gray calculated using the prediction model and the predicted value (L1, a1, b1) of the target gray is added to the colorimetric value (Lm, am, bm) of the pre-correction gray.

[0062] Hereinafter, each process including the gray correction process of the image processing device 1 will be described in detail with reference to FIGS.

[0063] (Image processing of image processing device) 6 is a flowchart showing an example of the flow of image processing in the image processing device 1 according to the first embodiment. The flow of image processing in the image processing device 1 according to the present embodiment will be described with reference to FIG.

[0064] <Step S11> As a profile conversion process, the image processing unit 18 converts the input device values ​​(Cin, Min, Yin, Kin) of the image data input by the image input unit 17 into intermediate device values ​​(C1, M1, Y1, K1) using an ICC (International Color Consortium) profile, and then proceeds to step S12.

[0065] <Step S12> Next, as a total amount restriction process, the image processing unit 18 replaces intermediate device values ​​that exceed the total amount that can be output by the image forming device 2 with intermediate device values ​​(C2, M2, Y2, K2) that are equal to or less than the total amount. Then, the process proceeds to step S13.

[0066] <Step S13> Next, as a calibration process, the image processing unit 18 converts the intermediate device values ​​(C2, M2, Y2, K2) into output device values ​​(Cout, Mout, Yout, Kout) using the monochrome TRC generated by the TRC generating unit 15. Then, the process proceeds to step S14.

[0067] <Step S14> Finally, as a screen process, the image processing unit 18 converts the image data made up of the output device values ​​(Cout, Mout, Yout, Kout) into a halftone dot format required for output by the image forming device 2. This completes the image processing of the image processing device 1.

[0068] (Target gray acquisition process for image processing devices) Fig. 7 is a flowchart showing an example of the flow of target gray acquisition processing in the image processing device according to the first embodiment. Fig. 8 is a diagram showing an example of a gray correction chart in the first state of the first embodiment. The flow of target gray acquisition processing in the image processing device 1 according to this embodiment will be described with reference to Figs. 7 and 8. The target gray acquisition processing in the image processing device 1 is processing that is executed in the first state described above.

[0069] <Step S21> First, the chart image generation unit 16 generates a single-color calibration chart. Image data of the single-color calibration chart is processed by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 does not perform profile conversion processing or calibration processing, that is, it outputs the data so that the input device values ​​(Cin, Min, Yin, Kin) and the output device values ​​(Cout, Mout, Yout, Kout) are the same. Then, the image forming device 2 outputs the single-color calibration chart, and colorimetric processing is performed by the colorimeter 4. Then, the process proceeds to step S22.

[0070] <Step S22> Next, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the single-color calibration chart from the colorimeter 4. Then, the target acquisition unit 13 acquires the target single color from the colorimetric values ​​and stores it in the memory unit 14. Then, the TRC generation unit 15 generates a single-color TRC to match the target single color stored in the memory unit 14 and stores it in the memory unit 14. Note that, because the results of the gray correction process described below are directly applied to the single-color TRC, the format of the target single-color value and the method of generating the single-color TRC are not limited to a specific format or method. Then, the process proceeds to step S23.

[0071] <Step S23> Next, the chart image generating unit 16 generates a gray correction chart 30 as shown in FIG. 8, for example.

[0072] As shown in FIG. 8, the gray correction chart 30 includes patches 31 to 34 arranged at the four corners and patch groups 36 to 38. Patch 31 is a target gray patch with a dot area ratio of C=M=Y=10% (an example of a first gradation value). Patch 32 is a target gray patch with a dot area ratio of C=M=Y=20% (an example of a first gradation value). Patch 33 is a target gray patch with a dot area ratio of C=M=Y=40% (an example of a first gradation value). Patch 34 is a target gray patch with a dot area ratio of C=M=Y=60% (an example of a first gradation value). Patch group 36 is a group of neighboring gray patches allocated with combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value) relative to the dot area ratio of patch 32. Patch group 37 is a group of nearby gray patches allocated with combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value) relative to the dot area ratio of patch 33. Patch group 38 is a group of nearby gray patches allocated with combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value) relative to the dot area ratio of patch 34. Note that patch groups 36 to 38 also include patches with the same dot area ratio as patches 32 to 34. Furthermore, patches 31 to 34 are not limited to being arranged at the four corners, but may be arranged in any location where the colorimetric value of the target gray can be obtained with high accuracy.

[0073] The image data of the gray correction chart 30 is subjected to image processing by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 performs only the calibration process without performing the profile conversion process. Then, the image forming device 2 outputs the gray correction chart 30, and the colorimeter 4 performs the color measurement process. Then, the process proceeds to step S24.

[0074] <Step S24> Next, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the gray correction chart 30 from the colorimeter 4. The prediction model creation unit 12 acquires, from the colorimetric values, each gray in the patch group 36 as a 20% target neighborhood gray, each gray in the patch group 37 as a 40% target neighborhood gray, and each gray in the patch group 38 as a 60% target neighborhood gray (examples of second colorimetric values). Then, the prediction model creation unit 12 creates a prediction model (hereinafter, sometimes referred to as a 20% prediction model) for predicting colorimetric values ​​from a dot area ratio near 20% using the 20% target neighborhood gray. Similarly, the prediction model creation unit 12 creates a 40% prediction model from the 40% target neighborhood gray, and a 60% prediction model from the 60% target neighborhood gray. That is, prediction models for high gradations, i.e., 20%, 40%, and 60%, are created from the target neighborhood gray acquired in the first state. Then, the process proceeds to step S25.

[0075] <Step S25> Furthermore, the target acquisition unit 13 acquires the colorimetric values ​​of patches 31 to 34 from the colorimetric values ​​acquired by the colorimetric value acquisition unit 11, calculates the average colorimetric values ​​(an example of a first colorimetric value) of each of the patches arranged in four locations, and acquires them as 10% target gray, 20% target gray, 40% target gray, and 60% target gray, respectively. This completes the target gray acquisition process of the image processing device 1.

[0076] (Gray correction processing in image processing devices) FIG. 9 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the first embodiment. FIG. 10 is a diagram showing an example of a gray correction chart in the second state of the first embodiment. FIG. 11 is a flowchart showing an example of the flow of gray correction value calculation processing in the image processing device according to the first embodiment. FIG. 12 is a diagram explaining a monochrome TRC for gray correction. FIG. 13 is a diagram explaining the calculation process of the gray correction value in the image processing device according to the first embodiment. The flow of gray correction processing in the image processing device 1 according to this embodiment will be described with reference to FIGS. 9 to 13. The gray correction processing in the image processing device 1 is processing executed in the second state described above.

[0077] <Step S31> First, the chart image generation unit 16 generates a single-color calibration chart. Image data of the single-color calibration chart is processed by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 does not perform profile conversion processing or calibration processing, that is, it outputs the data so that the input device values ​​(Cin, Min, Yin, Kin) and the output device values ​​(Cout, Mout, Yout, Kout) are the same. Then, the image forming device 2 outputs the single-color calibration chart, and colorimetric processing is performed by the colorimeter 4. Then, the process proceeds to step S32.

[0078] <Step S32> Next, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the single-color calibration chart from the colorimeter 4. Then, the target acquisition unit 13 acquires the target single color from the colorimetric values ​​and stores it in the storage unit 14. Then, the TRC generation unit 15 generates a single-color TRC to match the target single color stored in the storage unit 14, and updates the single-color TRC already stored in the storage unit 14. Then, the process proceeds to step S33.

[0079] <Step S33> Next, the chart image generating unit 16 generates a gray correction chart 40 as shown in FIG. 10, for example.

[0080] As shown in FIG. 10, the gray correction chart 40 includes patches 41 to 44 arranged at the four corners and a patch group 45. Patch 41 is a pre-correction gray patch with a dot area ratio of C=M=Y=10% (an example of a first gradation value). Patch 42 is a pre-correction gray patch with a dot area ratio of C=M=Y=20% (an example of a first gradation value). Patch 33 is a pre-correction gray patch with a dot area ratio of C=M=Y=40% (an example of a first gradation value). Patch 34 is a pre-correction gray patch with a dot area ratio of C=M=Y=60% (an example of a first gradation value). Patch group 45 is a group of neighboring gray patches allocated to the dot area ratio of patch 41 by combining ±5% for C and M and ±10% for Y (an example of a second gradation value). Note that patch group 45 also includes a patch with the same dot area ratio as patch 41. Furthermore, the patches 41 to 44 are not limited to being placed at the four corners, but may be placed at any location where colorimetric values ​​can be obtained with high accuracy.

[0081] The image data of the gray correction chart 40 is subjected to image processing by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 does not perform profile conversion processing, but only performs calibration processing using the monochromatic TRC in step S32. Then, the image forming device 2 outputs the gray correction chart 40, and colorimetric processing is performed by the colorimeter 4. Then, the process proceeds to step S34.

[0082] <Step S34> Next, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the gray correction chart 40 from the colorimeter 4. The prediction model creation unit 12 acquires each gray of the patch group 45 from the colorimetric values ​​as a 10% target neighborhood gray (an example of a second colorimetric value). Then, the prediction model creation unit 12 creates a prediction model (hereinafter sometimes referred to as a 10% prediction model) for predicting colorimetric values ​​from a dot area ratio near 10% from the 10% target neighborhood gray. In other words, the low gradation, i.e., the 10% prediction model, is created from the target neighborhood gray acquired in the second state. Then, the process proceeds to step S35.

[0083] <Step S35> The TRC generation unit 15 acquires the colorimetric values ​​of patches 41 to 44 from the colorimetric values ​​acquired by the colorimetric value acquisition unit 11, calculates the average value of the colorimetric values ​​of each of the patches arranged in four locations, and acquires them as 10% pre-correction gray, 20% pre-correction gray, 40% pre-correction gray, and 60% pre-correction gray (an example of a third colorimetric value), respectively.The TRC generation unit 15 then acquires a correction value for the dot area ratio of each pre-correction gray using a corresponding prediction model so that it matches the corresponding target gray.Details of the gray correction value calculation process, which is the calculation process of the correction value by the TRC generation unit 15, will be described below with reference to FIGS. 11 to 13.

[0084] <<Step S351>> First, the TRC generating unit 15 identifies the dot area ratio (C, M, Y) of one target gray for calculating a correction value, and then proceeds to step S352.

[0085] <<Step S352>> Next, the TRC generating unit 15 identifies one target nearby gray dot percentage (Cn, Mn, Yn) from the target nearby gray dot percentages corresponding to the identified target gray dot percentage, and then proceeds to step S353.

[0086] <<Step S353>> Next, the TRC generation unit 15 determines whether the identified dot area ratio (Cn, Mn, Yn) of the target neighborhood gray is 10%. If the dot area ratio of the target neighborhood gray is 10% (step S353: Yes), the TRC generation unit 15 proceeds to step S354. If the dot area ratio of the target neighborhood gray is other than 10% (here, 20%, 40%, or 60%) (step S353: No), the TRC generation unit 15 proceeds to step S355.

[0087] <<Step S354>> The TRC generation unit 15 calculates predicted values ​​(L1, a1, b1) from the dot area percentages (C, M, Y) of the identified target gray (here, 10% target gray) using the prediction model (here, 10% prediction model) created in the first state. The TRC generation unit 15 also calculates predicted values ​​(L2, a2, b2) from the dot area percentages (Cn, Mn, Yn) of the identified near-target gray (here, 10% target near-target gray) using the prediction model (here, 10% prediction model) created in the first state. Then, the process proceeds to step S356.

[0088] <<Step S355>> The TRC generation unit 15 calculates a predicted value (L1, a1, b1) from the dot area ratio (C, M, Y) of the identified target gray (here, 20%, 40%, or 60% target gray) using the prediction model created in the second state. The TRC generation unit 15 also calculates a predicted value (L2, a2, b2) from the dot area ratio (Cn, Mn, Yn) of the identified target neighborhood gray (here, 20%, 40%, or 60% target neighborhood gray) using the prediction model created in the second state. Then, the process proceeds to step S356.

[0089] <<Step S356>> 13, the TRC generation unit 15 calculates a predicted Lab value (Lm+ΔL,am+Δa,bm+Δb) (predicted colorimetric value) by adding the difference (ΔL,Δa,Δb)=(L2-L1,a2-a1,b2-b1) between the predicted value (L1,a1,b1) and the predicted value (L2,a2,b2) to the pre-correction gray (Lm,am,bm) corresponding to the dot area ratio of the identified target gray among the acquired pre-correction grays. Then, the process proceeds to step S357.

[0090] <<Step S357>> Then, the TRC generating unit 15 calculates the color difference between the calculated predicted Lab value and the colorimetric value (Lt, at, bt) of the target gray corresponding to the dot area ratio of the identified target gray, as shown in Fig. 13. Then, the process proceeds to step S358.

[0091] <<Step S358>> The TRC generator 15 determines whether the calculated color difference is equal to or less than a predetermined threshold (first threshold). If the color difference is equal to or less than the threshold (step S358: Yes), the process proceeds to step S360. If the color difference exceeds the threshold (step S358: No), the process proceeds to step S359.

[0092] <<Step S359>> The TRC generation unit 15 identifies a dot area ratio (Cn, Mn, Yn) of a target nearby gray other than the one already identified among the dot area ratios of the target nearby gray corresponding to the identified dot area ratio of the target gray, and then returns to step S353.

[0093] <<Step S360>> The TRC generating unit 15 obtains the dot area ratio of the identified target neighborhood gray as a correction value for the dot area ratio of the identified target gray that takes gray into consideration, and then proceeds to step S361.

[0094] <<Step S361>> The TRC generation unit 15 determines whether correction values ​​for the dot area ratios of all target grays have been acquired. If correction values ​​for the dot area ratios of all target grays have not been acquired (step S361: No), the process proceeds to step S362. If correction values ​​have been acquired (step S361: Yes), the gray correction value calculation process ends and the process proceeds to step S36 in FIG. 9.

[0095] <<Step S362>> The TRC generating unit 15 identifies a target gray dot percentage (C, M, Y) that is different from the already identified target gray dot percentages, and then returns to step S352.

[0096] <Step S36> Then, the TRC generation unit 15 generates a monochrome TRC (monochrome TRC with gray correction) that reflects the correction based on the calculated correction value (gray correction value conversion shown in FIG. 12) for the monochrome TRC obtained in step S32, and updates the monochrome TRC already stored in the storage unit 14. Thereafter, the image processing unit 18 performs a calibration process using the monochrome TRC (monochrome TRC with gray correction), thereby converting the input device values ​​into output device values ​​that have been gray corrected. This completes the gray correction process of the image processing device 1.

[0097] As described above, the configuration of the image processing device 1 according to this embodiment makes it possible to suppress an increase in the load of color measurement processing each time correction is performed, and to perform color mixing correction with high accuracy.

[0098] In the above description, a prediction model for a 10% target neighborhood gray is created during gray correction (second state), but a prediction model for a target neighborhood gray of another dot area ratio may also be created during gray correction (second state). For example, a prediction model for a 20% target neighborhood gray, which is closer to the low gradation level, may also be created during gray correction (second state).

[0099] [Second embodiment] The image processing device according to the second embodiment will be described, focusing on the differences from the image processing device 1 according to the first embodiment. In this embodiment, an operation will be described in which a prediction model created when target gray is acquired (first state) is updated during gray correction to a prediction model for target near-grays for which the local variation difference is equal to or greater than a predetermined threshold by evaluating the local variation difference between when target gray is acquired (first state) and when gray is corrected (second state). Note that the overall configuration of the information processing system 100 according to this embodiment, and the hardware configurations of the image processing device (image processing device 1a described below), image forming device 2, and user PC 3 are the same as those described in the first embodiment.

[0100] (Configuration and operation of functional blocks of image processing device) 14 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.

[0101] 14, the image processing device 1a includes a colorimetric value acquisition unit 11, a prediction model creation unit 12 (second acquisition unit, creation unit), a target acquisition unit 13 (first acquisition unit), a storage unit 14, a TRC generation unit 15 (third acquisition unit, first calculation unit, first determination unit, fourth acquisition unit), a chart image generation unit 16, an image input unit 17, an image processing unit 18 (correction unit), an image output unit 19, and a local variation difference determination unit 20 (second calculation unit, second determination unit). Note that the operations of the colorimetric value acquisition unit 11, the prediction model creation unit 12, the target acquisition unit 13, the storage unit 14, the TRC generation unit 15, the image input unit 17, the image processing unit 18, and the image output unit 19 are the same as those described in the first embodiment.

[0102] The chart image generating unit 16 generates a chart image required for obtaining target gray, creating a prediction model, determining local variation differences, and performing gray correction processing.

[0103] The local variation difference determination unit 20 is a functional unit that calculates a local variation difference from the colorimetric values ​​acquired by the colorimetric value acquisition unit 11 and the prediction model stored in the storage unit 14, and performs threshold determination on the local variation difference. The local variation difference determination unit 20 is realized, for example, by a program being executed by the CPU 501 shown in FIG.

[0104] Note that the colorimetric value acquisition unit 11, prediction model creation unit 12, target acquisition unit 13, TRC generation unit 15, chart image generation unit 16, image input unit 17, image processing unit 18, image output unit 19, and local variation difference determination unit 20 of the image processing device 1a shown in Fig. 14 are not limited to being realized by a program executed by the CPU 501 shown in Fig. 2. For example, they may be realized by hardware such as an integrated circuit, or by a combination of software and hardware.

[0105] Furthermore, the functional units of the image processing device 1a shown in Fig. 14 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. 14 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. 14 may be divided into multiple units and configured as multiple functional units.

[0106] (Outline of local variation difference calculation process) 15 is a diagram illustrating an overview of the local variation difference calculation process in the image processing device according to the second embodiment. The overview of the local variation difference calculation process in the image processing device 1a according to this embodiment will be described with reference to FIG.

[0107] If the color change (local variation) between the target gray gradation and the gradation of the target gray (near-target gray) is not large (local variation difference) between the time of target gray acquisition (first state) and the time of gray correction (second state), accurate correction is possible even if a prediction model is created and used when target gray acquisition (first state). However, in reality, as shown in FIG. 15 , local variation may differ between the time of target gray acquisition and the time of gray correction due to changes in the state of the image forming apparatus 2, so if the correction value calculated using the prediction model created when target gray acquisition (first state) is applied, the colorimetric value of the corrected gray will not match the predicted value of the corrected gray. As such, if the local variation difference between the time of target gray acquisition and the time of gray correction is large, correction accuracy will decrease. Therefore, in this embodiment, if the local variation difference is large, the prediction model created during gray correction (second state) is used; otherwise, the prediction model created during target gray acquisition (first state) is used.

[0108] (Target gray acquisition process for image processing devices) Fig. 16 is a diagram showing an example of a gray correction chart in the first state of the second embodiment. The flow of the target gray acquisition process of the image processing device 1a according to this embodiment conforms to the flow of the flowchart shown in Fig. 7 above, but the following explanation will focus on the differences.

[0109] In step S23, the chart image generating unit 16 generates, for example, the gray correction chart 50 shown in FIG.

[0110] As shown in FIG. 16, the gray correction chart 50 includes patches 51 to 54 arranged at the four corners and patch groups 55 to 58. Patch 51 is a target gray patch with a dot area ratio of C=M=Y=10% (an example of a first gradation value). Patch 52 is a target gray patch with a dot area ratio of C=M=Y=20% (an example of a first gradation value). Patch 53 is a target gray patch with a dot area ratio of C=M=Y=40% (an example of a first gradation value). Patch 54 is a target gray patch with a dot area ratio of C=M=Y=60% (an example of a first gradation value). Patch group 55 is a group of neighboring gray patches allocated with combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value) relative to the dot area ratio of patch 51. Patch group 56 is a group of nearby gray patches allocated with the dot area ratio of patch 52 at combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value). Patch group 57 is a group of nearby gray patches allocated with the dot area ratio of patch 53 at combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value). Patch group 58 is a group of nearby gray patches allocated with the dot area ratio of patch 54 at combinations of ±5% for C and M and ±10% for Y (an example of a second gradation value). Note that patch groups 55 to 58 also include patches with the same dot area ratio as patches 51 to 54. Furthermore, patches 51 to 54 are not limited to being arranged at the four corners, but may be arranged in any location where the colorimetric value of the target gray can be accurately obtained.

[0111] The image data of the gray correction chart 50 is subjected to image processing by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 performs only calibration processing without performing profile conversion processing. The image forming device 2 then outputs the gray correction chart 50, and colorimeter 4 performs color measurement processing.

[0112] In step S24, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the gray correction chart 50 from the colorimeter 4. Of the colorimetric values, the prediction model creation unit 12 acquires each gray in patch group 55 as a 10% target neighborhood gray, each gray in patch group 56 as a 20% target neighborhood gray, each gray in patch group 57 as a 40% target neighborhood gray, and each gray in patch group 58 as a 60% target neighborhood gray (examples of second colorimetric values). Then, the prediction model creation unit 12 creates a prediction model (10% prediction model) (an example of a first prediction model) for predicting colorimetric values ​​from the 10% target neighborhood gray based on a dot area ratio of about 10%. Similarly, the prediction model creation unit 12 creates a 20% prediction model (an example of a first prediction model) from the 20% target neighborhood gray, a 40% prediction model (an example of a first prediction model) from the 40% target neighborhood gray, and a 60% prediction model (an example of a first prediction model) from the 60% target neighborhood gray.

[0113] The other processes are the same as those described with reference to FIG.

[0114] (Gray correction processing in image processing devices) FIG. 17 is a flowchart showing an example of the flow of gray correction processing in the image processing device according to the second embodiment. FIG. 18 is a diagram showing an example of a gray correction chart in the second state of the second embodiment. FIG. 19 is a diagram showing an example of a prediction model update chart in the second embodiment. FIG. 20 is a flowchart showing an example of the flow of local variation difference calculation processing in the image processing device according to the second embodiment. FIG. 21 is a diagram illustrating the process of calculating local variation difference 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 FIGS. 17 to 21. The gray correction processing in the image processing device 1a is processing executed in the second state described above.

[0115] <Step S51> First, the chart image generation unit 16 generates a single-color calibration chart. Image data of the single-color calibration chart is processed by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 does not perform profile conversion processing or calibration processing, that is, it outputs the data so that the input device values ​​(Cin, Min, Yin, Kin) and the output device values ​​(Cout, Mout, Yout, Kout) are the same. Then, the image forming device 2 outputs the single-color calibration chart, and colorimetric processing is performed by the colorimeter 4. Then, the process proceeds to step S52.

[0116] <Step S52> Next, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the single-color calibration chart from the colorimeter 4. Then, the target acquisition unit 13 acquires the target single color from the colorimetric values ​​and stores it in the storage unit 14. Then, the TRC generation unit 15 generates a single-color TRC to match the target single color stored in the storage unit 14, and updates the single-color TRC already stored in the storage unit 14. Then, the process proceeds to step S53.

[0117] <Step S53> Next, the chart image generating unit 16 generates a gray correction chart 60 as shown in FIG. 18, for example.

[0118] As shown in FIG. 18, the gray correction chart 60 includes patches 61 to 64 arranged at the four corners and patch groups 65 to 68. Patch 61 is a pre-correction gray patch with a dot area ratio of C=M=Y=10% (an example of a first gradation value). Patch 62 is a pre-correction gray patch with a dot area ratio of C=M=Y=20% (an example of a first gradation value). Patch 63 is a pre-correction gray patch with a dot area ratio of C=M=Y=40% (an example of a first gradation value). Patch 64 is a pre-correction gray patch with a dot area ratio of C=M=Y=60% (an example of a first gradation value). Patch group 65 is a patch group of neighboring grays allocated to the dot area ratio of patch 61 by any combination of ±5% for C and M and ±10% for Y (an example of a second gradation value). Patch group 66 is a group of nearby gray patches allocated to the dot area ratio of patch 62 by any one of the following combinations (an example of a second gradation value): ±5% for C and M and ±10% for Y. Patch group 67 is a group of nearby gray patches allocated to the dot area ratio of patch 63 by any one of the following combinations (an example of a second gradation value): ±5% for C and M and ±10% for Y. Patch group 68 is a group of nearby gray patches allocated to the dot area ratio of patch 64 by any one of the following combinations (an example of a second gradation value): ±5% for C and M and ±10% for Y. The number of patches constituting each of patch groups 65 to 68 is smaller than the number of patches constituting each of patch groups 55 to 58 of the gray correction chart 50 shown in FIG. 16 above. Patch groups 65 to 68 also include patches with the same dot area ratio as patches 61 to 64. Furthermore, the patches 61 to 64 are not limited to being placed at the four corners, but may be placed at any location where colorimetric values ​​can be obtained with high accuracy.

[0119] The image data of the gray correction chart 60 is subjected to image processing by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 does not perform profile conversion processing, but only performs calibration processing using the monochromatic TRC in step S52. Then, the image forming device 2 outputs the gray correction chart 60, and color measurement processing is performed by the colorimeter 4. Then, the process proceeds to step S54.

[0120] <Step S54> The local variation difference determination unit 20 calculates the local variation difference from the colorimetric values ​​of patches 61 to 64 and patch groups 65 to 68 among the colorimetric values ​​acquired by the colorimetric value acquisition unit 11, and the prediction models of 10%, 20%, 40%, and 60% stored in the storage unit 14. Details of the local variation difference calculation process of this local variation difference determination unit 20 will be described below with reference to FIGS. 20 and 21.

[0121] <<Step S541>> First, the local variation difference determining unit 20 identifies the dot area ratio (C, M, Y) of one target gray for calculating the local variation difference, and then proceeds to step S542.

[0122] <<Step S542>> Next, the local variation difference determination unit 20 identifies one of the target near-gray dot percentages (Cn, Mn, Yn) acquired by the gray correction chart 60 in step S53 from the target near-gray dot percentages corresponding to the identified target gray dot percentage, and then proceeds to step S543.

[0123] <<Step S543>> The local variation difference determination unit 20 calculates a predicted value (L1, a1, b1) from the dot area ratios (C, M, Y) of the identified target gray using the prediction model created in the first state, as shown in Fig. 21. Also, the local variation difference determination unit 20 calculates a predicted value (L2, a2, b2) from the dot area ratios (Cn, Mn, Yn) of the identified target neighborhood gray using the prediction model created in the first state, as shown in Fig. 21. Then, the process proceeds to step S544.

[0124] <<Step S544>> 21, the local variation difference determination unit 20 acquires the colorimetric values ​​(Lt, at, bt) corresponding to the identified target gray and the colorimetric values ​​(Ln, an, bn) corresponding to the identified near-target gray from among the colorimetric values ​​acquired by the colorimetric value acquisition unit 11. Then, the process proceeds to step S545.

[0125] <<Step S545>> As shown in FIG. 21 , the local variation difference determination unit 20 determines the local variation difference √{(ΔLm-ΔL)} in the specified target vicinity gray from the difference (ΔL, Δa, Δb) = (L2-L1, a2-a1, b2-b1) (first difference) between the predicted value (L1, a1, b1) and the predicted value (L2, a2, b2) and the difference (ΔLm, Δam, Δbm) = (Ln-Lt, an-at, bn-bt) (second difference) between the colorimetric value (Lt, at, bt) and the colorimetric value (Ln, an, bn). 2 +(Δam-Δa) 2 +(Δbm-Δb) 2 Then, the process proceeds to step S546.

[0126] <<Step S546>> The local variation difference determination unit 20 determines whether or not all of the local variation differences for the target vicinity grays acquired by the gray correction chart 60 in step S53 that correspond to the identified target gray have been calculated. If the local variation differences for all of the target vicinity grays have not been calculated (step S546: No), the process proceeds to step S547, and if they have been calculated (step S546: Yes), the process proceeds to step S548.

[0127] <<Step S547>> The local variation difference determination unit 20 identifies a dot area ratio (Cn, Mn, Yn) of a target nearby gray that is different from the already identified dot area ratio of the target nearby gray that corresponds to the identified dot area ratio of the target gray, and then returns to step S543.

[0128] <<Step S548>> The local variation difference determination unit 20 averages all the local variation differences in the target neighborhood grays corresponding to the dot area ratio of the identified target gray, and acquires this as the local variation difference of the target gray.Then, the process proceeds to step S549.

[0129] <<Step S549>> The local variation difference determination unit 20 determines whether or not the local variation differences of all target grays have been acquired. If the local variation differences of all target grays have not been acquired (step S549: No), the process proceeds to step S550. If the local variation differences of all target grays have been acquired (step S549: Yes), the local variation difference calculation process ends and the process proceeds to step S55 in FIG. 17.

[0130] <<Step S550>> The local variation difference determining unit 20 identifies a target gray dot area ratio (C, M, Y) that is different from the already identified target gray dot area ratios among the identified target gray dot area ratios, and then returns to step S542.

[0131] <Step S55> The local variation difference determination unit 20 determines whether the calculated local variation difference of each target gray is equal to or greater than a predetermined threshold (second threshold). If at least one of the local variation differences of each target gray is equal to or greater than the threshold (step S55: Yes), the process proceeds to step S56. If all of the local variation differences of each target gray are less than the threshold (step S55: No), the process proceeds to step S58.

[0132] <Step S56> The chart image generation unit 16 generates a prediction model updating chart 70, such as that shown in Fig. 19, which includes a group of patches of neighboring grays corresponding to target grays for which the local variation difference has been determined to be equal to or greater than the threshold by the local variation difference determination unit 20. Here, the explanation will be given assuming that the local variation difference between the 10% target gray and the 60% target gray has been determined to be equal to or greater than the threshold by the local variation difference determination unit 20.

[0133] 19, the prediction model updating chart 70 includes patch groups 75 and 78. Patch group 75 is a group of neighboring gray patches allocated to a target gray dot area ratio of 10% by combining C and M by ±5% and Y by ±10% (an example of a second gradation value). Patch group 78 is a group of neighboring gray patches allocated to a target gray dot area ratio of 60% by combining C and M by ±5% and Y by ±10% (an example of a second gradation value).

[0134] The image data of the prediction model updating chart 70 is subjected to image processing by the image processing unit 18 and output from the image output unit 19 to the image forming device 2. In this case, the image processing unit 18 does not perform profile conversion processing, but only performs calibration processing using the monochromatic TRC in step S52. Then, the image forming device 2 outputs the prediction model updating chart 70, and colorimetric processing is performed by the colorimeter 4. Then, the process proceeds to step S57.

[0135] <Step S57> Next, the colorimetric value acquisition unit 11 acquires the colorimetric values ​​of the prediction model updating chart 70 from the colorimeter 4. The prediction model creation unit 12 acquires each gray in the patch group 75 from the colorimetric values ​​as a gray near the 10% target, and creates a prediction model (10% prediction model) (an example of a second prediction model) for predicting colorimetric values ​​from a dot area rate near the 10% target, based on the gray near the 10% target. The prediction model creation unit 12 also acquires each gray in the patch group 78 from the colorimetric values ​​as a gray near the 60% target, and creates a prediction model (60% prediction model) (an example of a second prediction model) for predicting colorimetric values ​​from a dot area rate near the 60% target, based on the gray near the 60% target. Then, the process proceeds to step S58.

[0136] <Step S58> The TRC generation unit 15 acquires the colorimetric values ​​of patches 61 to 64 from the colorimetric values ​​acquired by the colorimetric value acquisition unit 11 for the gray correction chart 60, calculates the average value of the colorimetric values ​​for each of the four patches, and acquires them as 10% pre-correction gray, 20% pre-correction gray, 40% pre-correction gray, and 60% pre-correction gray (an example of a third colorimetric value).The TRC generation unit 15 then acquires a correction value for the dot area ratio of each pre-correction gray using a corresponding prediction model so that it matches the corresponding target gray.In this case, the TRC generation unit 15 acquires correction values ​​for the dot area ratios of the 20% pre-correction gray and the 40% pre-correction gray using the prediction model created in the first state described above.Furthermore, the TRC generation unit 15 acquires correction values ​​for the dot area ratios of the 10% pre-correction gray and the 60% pre-correction gray using the prediction model created in step S57 described above (the prediction model created in the second state). The gray correction value calculation process, which is the calculation process of the correction value by the TRC generation unit 15, conforms to the processing flow in Fig. 11. Then, the process proceeds to step S59.

[0137] <Step S59> Then, the TRC generation unit 15 generates a monochrome TRC (monochrome TRC with gray correction) that reflects the correction based on the calculated correction value for the monochrome TRC obtained in step S52, and updates the monochrome TRC already stored in the storage unit 14. Thereafter, the image processing unit 18 performs a calibration process using the monochrome TRC (monochrome TRC with gray correction), thereby converting the input device values ​​into output device values ​​that have been gray-corrected. This completes the gray correction process of the image processing device 1a.

[0138] As described above, the configuration of the image processing device 1a according to this embodiment makes it possible to suppress an increase in the load of colorimetry processing each time correction is performed, and to perform color mixing correction with high accuracy. In other words, since there is no need to print a chart and perform colorimetry to create a prediction model unnecessarily during gray correction, it is possible to perform gray correction with high accuracy while reducing the burden of colorimetry on the user.

[0139] Each function of each embodiment described above 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, and devices such as an ASIC, a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), or a conventional circuit module designed to execute each function described above.

[0140] Furthermore, the programs executed by the image processing apparatuses 1 and 1a of the above-described embodiments may be provided in a state that they are pre-installed in a ROM or the like.

[0141] In addition, the programs executed by the image processing devices 1 and 1a of the above-described embodiments may be configured to be provided as a computer program product by being recorded in an installable or executable 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).

[0142] 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.

[0143] 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. [Explanation of symbols]

[0144] 1, 1a Image processing device 2. Image forming device 3 User PC 4 Colorimeter 11 Colorimetric value acquisition unit 12 Prediction Model Creation Department 13 Target Acquisition Section 14 Storage section 15 TRC generation section 16 Chart image generation unit 17 Image input unit 18 Image processing section 19 Image output unit 20 Local fluctuation difference judgment unit 30 Gray Correction Chart 31~34 patches 35-38 Patch Group 40 Gray Correction Chart 41~44 patches 45 patches 50 Gray Correction Chart Patches 51-54 55~58 Patch Group 60 Gray Correction Chart Patches 61-64 65-68 patch group 70 Forecast model update chart 75, 78 patch group 100 Information Processing Systems 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]

[0145] [Patent Document 1] Patent No. 4751853 [Patent Document 2] Japanese Patent Publication No. 2020-151967

Claims

1. An image processing device that performs color matching of colors of the same target gradation value acquired in two different states, a first acquisition unit that acquires, in a first state, first colorimetric values ​​of target color mixtures corresponding to one or more first gradation values ​​from a chart printed out from the image forming apparatus based on the one or more first gradation values; a second acquisition unit that acquires second colorimetric values ​​of nearby color mixture from a chart printed out from the image forming apparatus in the first state for second gradation values ​​that are near at least some of the first gradation values, and from a chart printed out from the image forming apparatus in a second state that is later in time than the first state for second gradation values ​​that are near the remaining first gradation values, a third acquisition unit that, in the second state, acquires third colorimetric values ​​of pre-correction mixed colors corresponding to each of the first gradation values ​​from a chart printed out from the image forming apparatus based on each of the first gradation values; a creation unit that creates a prediction model corresponding to each of the first gradation values ​​for predicting a colorimetric value from a gradation value based on the second gradation values ​​near the part of the first gradation values ​​and the second gradation values ​​near the remaining first gradation values; a first calculation unit that calculates a predicted colorimetric value of the target color mixture by adding a difference between a first predicted value predicted from the first gradation value using the prediction model corresponding to the first gradation value and a second predicted value predicted from the second gradation value corresponding to the first gradation value using the prediction model, and the third colorimetric value corresponding to the first colorimetric value; a first determination unit that determines whether a color difference between the predicted colorimetric value and the first colorimetric value corresponding to the predicted colorimetric value is equal to or less than a first threshold; a fourth acquisition unit that acquires, when the color difference is equal to or smaller than the first threshold value, the second gradation value of the second colorimetric value corresponding to the color difference as a correction value; a correction unit that corrects the first gradation value using the correction value so that the colorimetric value of the pre-correction color mixture becomes the colorimetric value of the target color mixture; An image processing device comprising:

2. The second acquisition unit acquiring the second colorimetric value for the second gradation value near the first gradation value on the high gradation side as the part of the first gradation value; The image processing device according to claim 1 , wherein the second colorimetric values ​​for the second gradation values ​​near the first gradation values ​​on the lower gradation side than the part of the first gradation values ​​are acquired as the remaining first gradation values.

3. the second acquisition unit acquires the second colorimetric values ​​from charts printed out from the image forming apparatus in the first state and the second state for all second gradation values ​​near the first gradation values; the creation unit creates a first prediction model corresponding to each of the first gradation values ​​based on the second colorimetric values ​​in the first state acquired by the second acquisition unit; a second calculation unit that calculates a local variation difference between a first difference between a colorimetric value predicted from the first gradation value using the corresponding first prediction model and a colorimetric value predicted from the second gradation value corresponding to the first gradation value using the first prediction model, and a second difference between the first colorimetric value corresponding to the first gradation value in the second state and the second colorimetric value corresponding to the second gradation value corresponding to the first gradation value; a second determination unit that determines whether the local variation difference is equal to or greater than a second threshold; Furthermore, when the local variation difference is equal to or greater than the second threshold value, the second acquisition unit acquires the second colorimetric values ​​from a chart printed out from the image forming apparatus in the second state for the second gradation values ​​near the first gradation value corresponding to the local variation difference; the creation unit creates a second prediction model corresponding to the first gradation value corresponding to the second colorimetric value based on the second colorimetric value acquired by the second acquisition unit when the local variation difference is equal to or greater than the second threshold value; The first calculation unit when there is the second prediction model corresponding to the first gradation value, a difference between the first predicted value predicted from the first gradation value using the second prediction model and the second predicted value predicted from the second gradation value corresponding to the first gradation value using the second prediction model is obtained as the difference; 2. The image processing device according to claim 1, wherein, when there is no second prediction model corresponding to the first gradation value, the difference between the first predicted value predicted from the first gradation value using the first prediction model corresponding to the first gradation value and the second predicted value predicted from the second gradation value corresponding to the first gradation value using the first prediction model is obtained as the difference.

4. 4. The image processing device according to claim 1, wherein the target color mixture, the neighboring color mixture, and the pre-correction color mixture are mixtures of cyan, magenta, and yellow.

5. 5. The image processing device according to claim 1, wherein the first colorimetric value, the second colorimetric value, and the third colorimetric value are color values ​​in an L*a*b* color space.

6. the image forming apparatus; a colorimeter for obtaining the first colorimetric value, the second colorimetric value, and the third colorimetric value; The image processing device according to any one of claims 1 to 5, An information processing system having the above.

7. An image processing method for performing color matching of colors having the same target gradation value acquired in two different states, comprising: a first acquisition step of acquiring, in a first state, first colorimetric values ​​of target color mixtures corresponding to one or more first gradation values ​​from a chart printed out from an image forming apparatus based on the one or more first gradation values; a second acquisition step of acquiring second colorimetric values ​​of nearby color mixture from a chart printed out from the image forming apparatus in the first state for second gradation values ​​that are nearby at least some of the first gradation values, and from a chart printed out from the image forming apparatus in a second state that is later in time than the first state for second gradation values ​​that are nearby the remaining first gradation values; a third acquisition step of acquiring, in the second state, third colorimetric values ​​of pre-correction mixed colors corresponding to each of the first gradation values ​​from a chart printed out from the image forming apparatus based on each of the first gradation values; a creating step of creating a prediction model corresponding to each of the first gradation values ​​for predicting a colorimetric value from a gradation value based on the second gradation values ​​near the part of the first gradation values ​​and the second gradation values ​​near the remaining first gradation values; a calculation step of calculating a predicted colorimetric value of the target color mixture by adding a difference between a first predicted value predicted from the first gradation value using the prediction model corresponding to the first gradation value and a second predicted value predicted from the second gradation value corresponding to the first gradation value using the prediction model, and the third colorimetric value corresponding to the first colorimetric value; a determining step of determining whether a color difference between the predicted colorimetric value and the first colorimetric value corresponding to the predicted colorimetric value is equal to or less than a first threshold value; a fourth acquisition step of acquiring, when the color difference is equal to or smaller than the first threshold value, the second gradation value of the second colorimetric value corresponding to the color difference as a correction value; a correcting step of correcting the first gradation value using the correction value so that the colorimetric value of the pre-correction color mixture becomes the colorimetric value of the target color mixture; An image processing method comprising:

8. On the computer, a first acquisition step of acquiring, in a first state, first colorimetric values ​​of target color mixtures corresponding to one or more first gradation values ​​from a chart printed out from an image forming apparatus based on the one or more first gradation values; a second acquisition step of acquiring second colorimetric values ​​of nearby color mixture from a chart printed out from the image forming apparatus in the first state for second gradation values ​​that are nearby at least some of the first gradation values, and from a chart printed out from the image forming apparatus in a second state that is later in time than the first state for second gradation values ​​that are nearby the remaining first gradation values; a third acquisition step of acquiring, in the second state, third colorimetric values ​​of pre-correction mixed colors corresponding to each of the first gradation values ​​from a chart printed out from the image forming apparatus based on each of the first gradation values; a creating step of creating a prediction model corresponding to each of the first gradation values ​​for predicting a colorimetric value from a gradation value based on the second gradation values ​​near the part of the first gradation values ​​and the second gradation values ​​near the remaining first gradation values; a calculation step of calculating a predicted colorimetric value of the target color mixture by adding a difference between a first predicted value predicted from the first gradation value using the prediction model corresponding to the first gradation value and a second predicted value predicted from the second gradation value corresponding to the first gradation value using the prediction model, and the third colorimetric value corresponding to the first colorimetric value; a determining step of determining whether a color difference between the predicted colorimetric value and the first colorimetric value corresponding to the predicted colorimetric value is equal to or less than a first threshold value; a fourth acquisition step of acquiring, when the color difference is equal to or smaller than the first threshold value, the second gradation value of the second colorimetric value corresponding to the color difference as a correction value; a correcting step of correcting the first gradation value using the correction value so that the colorimetric value of the pre-correction color mixture becomes the colorimetric value of the target color mixture; A program to execute.

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