Image processing apparatus and correction table creation method

Abstract

To achieve appropriate HS correction processing and density correction processing while shortening a working time.SOLUTION: An image processing apparatus for processing an image to be recorded on a recording medium by a plurality of recording elements arranged in a recording head is provided. The apparatus includes a colorimetry unit configured to acquire a measurement value for a first image region recorded on a recording medium by a part of a recording head, and an acquisition unit configured to acquire a measurement value for a second image region including a range of the first image region recorded on the recording medium by the recording head. This apparatus creates a first correction table for correcting a first measurement characteristic specified by acquiring a measurement value of a first reference image by a colorimetry unit to a given first target characteristic. In addition, a second correction table for correcting a second measurement characteristic of each of the plurality of recording elements, which is specified by acquiring a measurement value of a second reference image by the acquisition unit, to a second target characteristic uniform among the plurality of recording elements is created. The second target characteristic is a characteristic based on a measurement value of a portion corresponding to the first image region among the measurement values of the second image region acquired by the acquisition unit.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an image processing apparatus and a method for creating a correction table. [Background technology]

[0002] Inkjet (IJ) printers, which form images by ejecting ink from multiple nozzles, are widely used as image forming devices for forming any image on paper. In IJ image forming devices, deviations in the ink landing position and variations in the ejection amount can occur, which can cause uneven density and temporal density fluctuations, especially in full-line image recording devices.

[0003] One known countermeasure to density unevenness is head shading (abbreviated as HS processing), as described in Patent Document 1. HS processing corrects density unevenness by printing and measuring a chart that estimates the print unevenness characteristics caused by the ejection volume and positional misalignment of individual nozzles and modules (recording heads or chip units). Obtaining print unevenness characteristics requires measurement over a wide area, so a CCD or CIS line scanner is used.

[0004] In addition, density correction processing is performed to address density variations. This density correction processing corrects the density of the image formed according to the input image data to the desired density. It is desirable to use a high-precision colorimeter for density correction processing. This is because line scanners do not provide stable measurements over time due to fluctuations in the amount of light from the lighting, and are therefore not suitable for correcting density changes over time. In density correction processing, a chart consisting of patches with multiple gradations is printed and measured with a colorimeter to correct density variations.

[0005] As described above, HS processing is performed using a line scanner that can easily measure a wide range, and density correction processing is performed using a colorimeter that can measure a specific area with high precision. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-147126 Summary of the Invention [Problem to be solved by the invention]

[0007] When performing HS processing and density correction, if the charts used for each correction are printed and measured with the density unevenness and density characteristics being corrected, the density correction process is performed using the colorimetric values ​​obtained from the chart with density unevenness. However, when density unevenness is corrected using HS processing, the printer state assumed by the density correction process (i.e., the density values ​​used as the basis for density correction) changes. This makes it difficult to perform appropriate corrections using a chart with density unevenness. To properly correct density unevenness and density input / output characteristics, it is necessary to first perform one correction, such as HS correction, and then perform the other correction, such as density correction, after the correction has been completed. This problem is not limited to HS correction and density correction, but can also occur between multiple correction processes whose correction targets are interrelated. This serial correction process, in which multiple correction processes are applied sequentially, lengthens work time and reduces productivity. This problem is particularly noticeable in corrections that require printing correction charts, such as HS correction and density correction.

[0008] The present invention has been made in view of the above-mentioned conventional examples, and aims to achieve both appropriate HS correction and density correction, and shortening the work time required for the correction processing. [Means for solving the problem]

[0009] In order to achieve the above object, according to one aspect of the present invention, there is provided an image processing apparatus that processes an image recorded on a recording medium by a plurality of recording elements arranged in a recording head, the image processing apparatus comprising: a first acquisition means for acquiring a measurement value for a first image area recorded on a recording medium by at least a portion of the recording head; a second acquisition means for acquiring measurement values ​​for a second image area including the range of the first image area recorded on a recording medium by the recording head; a control means; The control means a first correction table is created for correcting a first measurement characteristic, which is determined by acquiring, by the first acquisition means, measurement values ​​of an image obtained by recording first reference image data on a recording medium by the recording head, and which associates input values ​​based on the first reference image data with the measurement values, to a given first target characteristic; a second correction table is created in which input values ​​based on the second reference image data and the measurement values ​​are associated with each other, the second correction table being used to correct second measurement characteristics of each of the plurality of recording elements to second target characteristics that are uniform across the plurality of recording elements; and The second target characteristic is a characteristic based on a measurement value of a portion of the second image region acquired by the second acquisition means that corresponds to the first image region. An image processing device is provided. [Effects of the Invention]

[0010] According to the present invention, it is possible to achieve appropriate HS correction processing and density correction processing while reducing the operation time. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram illustrating a recording system according to an embodiment of the present invention. [Figure 2] Schematic diagram of an inkjet printer according to an embodiment of the present invention. [Figure 3] FIG. 1 is a block diagram showing the configuration of an image processing unit according to an embodiment of the present invention. [Figure 4]FIG. 1 is a flowchart showing a printing process for a user image according to a first embodiment; [Figure 5] Flow diagram showing the process of creating a density correction table [Figure 6] FIG. 10 is a diagram showing an example of a chart used for density correction; [Figure 7] FIG. 10 is a diagram showing an example of a density correction table; [Figure 8] Flowchart showing the process for creating an HS correction table [Figure 9] A diagram showing an example of a chart used for HS correction [Figure 10] An example of an HS correction table [Figure 11] FIG. 10 is a flow chart showing the flow of calculating the correction amount for the print head. [Figure 12] Schematic diagram for explaining calculation of correction amount for a print head [Figure 13] FIG. 10 is a flowchart showing the flow of printing a user image according to a second embodiment; DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention claimed. Although multiple features are described in the embodiments, not all of these multiple features are necessarily essential to the invention, and multiple features may be combined arbitrarily. Furthermore, in the accompanying drawings, the same reference numerals are used to designate the same or similar components, and redundant explanations will be omitted.

[0013] First Embodiment (Hardware configuration of image forming system) 1 is a diagram showing the hardware configuration of an image forming system according to one embodiment of the present invention. The image forming system according to this embodiment includes a CPU 100, RAM 101, ROM 102, an operation unit 103, a display unit 104, an external storage device 105, an image processing unit 106, an image forming unit 107, an image acquisition unit 108, an I / F (interface) unit 109, an image colorimetry unit 110, and a bus 111. The CPU may be referred to as a processor or a control unit, and the RAM, ROM, and external storage device may be referred to as a memory unit or a storage unit.

[0014] The CPU (Central Processing Unit) 100 controls the operation of the entire image forming system using input data and computer programs stored in RAM and ROM (described later). Note that, although the case where the CPU 100 controls the entire image forming system will be described here as an example, the entire image forming system may also be controlled by multiple pieces of hardware sharing the processing.

[0015] RAM (Random Access Memory) 101 has a storage area for temporarily storing computer programs and data read from an external storage device 105 and data received from the outside via an I / F unit 109. RAM 101 is also used as a storage area used when CPU 100 executes various processes and as a storage area used when image processing unit 106 executes image processing.

[0016] A ROM (Read Only Memory) 102 has a storage area for storing setting parameters for setting each unit in the image forming system, a boot program, and the like.

[0017] The operation unit 103 is an input device such as a keyboard or a mouse, and receives operations (instructions) from an operator. This allows the operator to input various instructions to the CPU 100.

[0018] The display unit 104 is a display device such as a CRT (Cathode Ray Tube) or a liquid crystal screen, and can display the processing results by the CPU 100 as images, characters, etc. If the display unit 104 is a touch panel that can detect touch operations, the display unit 104 may function as a part of the operation unit 103.

[0019] The external storage device 105 is a large-capacity information storage device typified by a hard disk drive. The external storage device 105 stores computer programs and data for causing the OS (operating system) and CPU 100 to execute various processes. It also stores temporary data generated by the processing of each unit (for example, input / output image data and threshold matrices used in the image processing unit 106). The computer programs and data stored in the external storage device 105 are read as appropriate under the control of the CPU 100, stored in the RAM 101, and processed by the CPU 100.

[0020] The image processing unit 106 is realized as a processor capable of executing a computer program or a dedicated image processing circuit, and performs various image processing operations to convert image data input as the print target into image data that can be output by an image forming apparatus, which will be described later. Note that instead of providing a dedicated processor as the image processing unit 106, it is also possible to configure the CPU 100 to perform various image processing operations as the image processing unit 106.

[0021] The image forming unit 107 forms an image on a recording medium using a recording material based on image data received directly from the image processing unit 106 or via a RAM or an external recording device, as will be described in detail later.

[0022] The image acquisition unit 108 is an image sensor (a line sensor or an area sensor) for capturing an image of a recording medium formed by the image forming unit 107. Details will be described later.

[0023] The I / F unit 109 functions as an interface for connecting the image forming system to an external device. The I / F unit 109 also functions as an interface for exchanging data with a communication device using infrared communication, a wireless LAN (Local Area Network), etc., and as an interface for connecting to the Internet. This allows data such as input images to be exchanged with external devices.

[0024] The image colorimetry unit 110 is a colorimeter for measuring the color of a recorded image formed on a recording medium by the image forming unit 107. Details will be described later. Note that instead of providing the image colorimetry unit 110 as part of the image forming system, it is also possible to configure the image forming system to obtain the necessary colorimetry information from outside via the I / F unit. Each of the above-mentioned units is connected to a bus 111, and data can be exchanged via the bus 111. However, the image forming system may be configured such that each of the above-mentioned units (e.g., image forming unit 107) is connected via an I / F unit 109.

[0025] (Hardware configuration of the image forming unit and image acquisition unit) 2 is a diagram showing a schematic arrangement and configuration of an image forming unit 107, an image acquisition unit 108, and an image colorimetry unit 110 of an image forming system according to an embodiment of the present invention. The image forming unit 107 in this embodiment is an inkjet (IJ) printer that forms an image by ejecting ink from nozzles onto a recording medium. This image forming system is a color printer capable of printing color images using black (K), cyan (C), magenta (M), and yellow (Y) as color components.

[0026] 2(a), the image forming unit 107 includes a plurality of recording heads 201-204 corresponding to black (K), cyan (C), magenta (M), and yellow (Y), respectively. The recording heads 201-204 are of the so-called full-line type, in which a plurality of nozzles, i.e., recording elements, for ejecting ink of each color component are arranged along a predetermined direction within a range corresponding to the width of the recording paper 206. In the following description, the width direction of the recording paper is referred to as the x-direction, and the transport direction of the recording paper is referred to as the y-direction.

[0027] In this case, the print heads 201 to 204 are each made up of a plurality of chip modules 201-1 to 201-5, as shown in Fig. 2(b). Each chip module is connected to an independent board. The chip module is sometimes called a print chip.

[0028] Figure 2(c) is a diagram of the chip module as seen from the paper side, showing that the chip module has multiple nozzles, i.e., recording elements. In the example shown in Figure 2(c), the chip module has 16 nozzles. The nozzle arrangement resolution of the nozzle array for each ink color is 1200 dpi.

[0029] The recording paper 206 is transported in the direction indicated by arrow 207 in the figure by the rotation of transport roller 205 (and other rollers, not shown) by the driving force of a motor (not shown). Then, while the recording paper 206 is being transported, ink is ejected from the multiple nozzles of each of the recording heads 201 to 204 in accordance with the recording data, thereby sequentially forming an image for one raster corresponding to the nozzle array of each recording head. In this way, by repeating the ink ejection operation from each recording head onto the transported recording paper, an image for one page can be recorded.

[0030] In addition, an image acquisition unit is disposed downstream of the recording heads 201 to 204. The recording paper 206 on which an image has been formed by the recording heads 201 to 204 is transported to the image acquisition unit .

[0031] The image acquisition unit 108 sequentially captures images of the conveyed recording paper and stores the images as two-dimensional RGB image data in the external recording device 105. At this time, the resolution of the image data is 600 dpi. The resolution may be any, and may be different in the x and y directions, such as 1200 dpi in the x direction and 600 dpi in the y direction. The image acquisition unit 108 acquires RGB image data by capturing an image of an area (first image area) formed by all (or almost all) nozzles, i.e., recording elements, of each of the recording heads 201 to 204. In other words, the first image area has a width spanning the width of the recording head. The image acquisition unit 108 may be an image sensor, such as a CCD or CMOS sensor arranged in a line along the X direction.

[0032] The image colorimetry unit 110 is disposed downstream of the image acquisition unit 108. As shown in FIG. 2(d), the image colorimetry unit 110 is composed of four colorimetry modules 110a to 110d. Each colorimetry module sequentially measures the color of the conveyed recording paper and stores the colorimetry data in the external recording device 105. In this embodiment, the image colorimetry unit 110 is equipped with a spectral sensor capable of acquiring spectral reflectance data in 10-nm increments from 380 to 780 nm. The measured spectral reflectance data is converted into values ​​in the CIE XYZ color space and sent. Note that any color space may be used, and CIE L*a*b*, sRGB, or optical density may also be used. Alternatively, the spectral reflectance data may be sent as is. The image colorimetry unit 110 acquires reflectance data by capturing an image of an area (second image area) formed by at least some of the nozzles, i.e., the recording elements, of each of the recording heads 201 to 204. It is desirable that the color measurement modules 110a to 110d are all arranged so as not to straddle the boundaries between the chip modules.

[0033] The image acquisition unit 108 and the image colorimetry unit 110 differ in their measurement range and measurement accuracy. The image acquisition unit 108 can acquire the entire area of ​​the recording paper as a two-dimensional image, while the image colorimetry unit 108 can measure color only at positions corresponding to the four colorimetry modules 110a to 110d. On the other hand, the image colorimetry unit 110 has excellent measurement accuracy and can acquire highly accurate measurement values ​​with high reproducibility. The areas that are the targets of colorimetry by each of the four colorimetry modules 110a to 110d correspond to the second image area described above.

[0034] (Functional configuration of image processing unit) The configuration of the image processing unit 106 will be described below with reference to Fig. 3. As shown in Fig. 3, the image processing unit 106 is made up of an input color conversion processing unit 301, a density correction processing unit 302, an HS correction processing unit 303, an HT processing unit 304, a density correction table creation unit 305, a density correction table 306, an HS correction table creation unit 307, and an HS correction table 308. Each processing unit constituting the image processing unit 106 may be a functional module realized, for example, when the CPU 100 of the image processing system executes a program stored in a memory system including the RAM 101, the ROM 102, and the external storage device 105.

[0035] The input color conversion processing unit 301 converts input image data into image data corresponding to ink colors. In this embodiment, input RGB image data is converted into CMYK image data. The signal value of each image is 8 bits per color. The color conversion processing uses a well-known 3D LUT (lookup table) process.

[0036] The density correction processing unit 302 performs density correction processing on the CMYK color signal image data by referring to a density correction table 306. Here, the density correction processing is processing that performs correction for each ink color in order to correct density fluctuations in printed matter caused by the density fluctuation characteristics of the print heads 201 to 204. A density correction table 306 is stored for each CMYK color, and in this embodiment, four types of density correction tables are provided.

[0037] FIG. 7 shows an example of the density correction table 306 in this embodiment. An input signal value 701 is an 8-bit signal value corresponding to the signal value of one of the colors in the CMYK image. As shown in FIG. 7, the density correction table 306 is a table that stores a correction value for each input signal value. The density correction processing unit 302 corrects the signal value by referencing the correction value for the signal value corresponding to each color of the CMYK image data. Here, the density correction processing can be performed, for example, by replacing the input signal value with the correction value if the corrected signal value (corrected signal value or output signal value) associated with the pre-correction signal value (input signal value) is registered as the correction value in the density correction table 306. Alternatively, if the difference between the corrected signal value associated with the input signal value and the input signal value is registered as the correction value in the density correction table 306, the correction value can be added to the input signal value.

[0038] It should be noted that linear interpolation is performed when referencing the table. For example, in FIG. 7, when the input signal value is 8, a correction value of 14 is obtained by linear interpolation between the "correction value for input value 0" (0) and the "correction value for input value 16" (28). The density correction table 306 is created by a density correction table creation unit 305. The density correction table creation process will be described in detail later.

[0039] Returning to FIG. 3, the HS correction processing unit 303 performs HS correction processing on the CMYK color signal image data by referencing a head shading (HS) correction table 308. Here, HS correction processing is a process of performing correction for each nozzle to correct density unevenness in printed matter caused by the unevenness characteristics of the print heads 201-204. An HS correction table 308 is stored for each CMYK color, and in this embodiment, four types of HS correction tables are provided. The unevenness characteristics are characteristics that indicate the positional (i.e., nozzle-by-nozzle) variations in density on the print head when printing is performed with a uniform input signal value. The unevenness characteristics that are the target of HS correction processing are density variations in the longitudinal direction of the print head. The HS correction processing eliminates density unevenness in the print head and corrects the density characteristics of each nozzle to make them uniform. Note that the density characteristics are characteristics that indicate the relationship between the input signal value and the read value of the image formed corresponding to it, over the entire range of values ​​that the input signal value can take. The read values ​​of the image are values ​​expressed in a color system such as RGB or L*a*b* obtained by the image acquisition unit 108. Furthermore, they may be converted into values ​​in the CMYK color system.

[0040] 10 shows an example of the HS correction table 308 in this embodiment. The input signal value 1001 is an 8-bit signal value corresponding to the signal value of one of the colors in the CMYK image. The nozzle number 1002 is a number associated with each nozzle of the print head.

[0041] 10, the HS correction table 308 is a table that holds the correction amount for each input signal value for each nozzle number. The HS correction processing unit 303 corrects the signal value by referencing the signal value corresponding to the signal value of each color of the CMYK image data and the correction value for the nozzle number. Specifically, for each pixel of the color signal image data, the pixel value of the pixel of interest and the corresponding nozzle number are acquired, and a correction value is obtained by referencing the HS correction table 308. The method of correcting the signal value may be the same as that for density correction.

[0042] It should be noted that linear interpolation is performed when referencing the table. For example, in Fig. 10, if the input signal value is 8 and the nozzle number is 0, a correction value of 14 is obtained by linear interpolation between "the value when the input value is 0 and the nozzle number is 0" (correction value 0) and "the value when the input value is 16 and the nozzle number is 0" (correction value 28). The HS correction table 308 is created by the HS creation table creation unit 307. The HS creation table creation process will be described in detail later.

[0043] Returning to FIG. 3, the HT processing unit 304 performs HT processing (quantization processing) on ​​the image data after the HS correction processing. In this embodiment, the HT processing converts image data with 8 bits per color into HT image data with 1 bit per color. A known dithering method is used for the HT processing. Note that any HT processing method can be used, and methods such as error diffusion can also be applied.

[0044] The image forming unit 107 receives the HT image data of each color component and forms an image on the recording paper 206 by ejecting ink from the recording heads 201 to 204 corresponding to each color component.

[0045] The image acquisition unit 108 captures an image recorded on a printed matter. If the printed matter is an HS chart (described later), the image acquisition unit 108 sends the read image data to an HS correction table creation unit 305.

[0046] The image colorimetry unit 110 measures the color of the image recorded on the print. If the print is a density correction chart (described later), the image colorimetry unit 110 sends the colorimetry values ​​to a density correction table creation unit 305.

[0047] (User image printing flow) The flow of printing a user image in this embodiment will be described below with reference to the flow diagram shown in Fig. 4. Fig. 4 shows the processing procedure executed by the CPU 100 of the image processing system. Even steps described below as being executed by a processing unit within the image processing unit 106 can be considered to be steps executed primarily by the CPU 100.

[0048] First, a user submits a print job to the image forming system via the operation unit 103. The CPU 100 stores the submitted print job in, for example, the external storage device 105 (step S401). The print job includes print data and various print settings. The print data is written in, for example, a page description language, and includes data representing an image, such as vector data, text data, and image data composed of pixels. Therefore, the print data included in the print job is called image data, and image data that has not been processed by the image processing unit 106 is called input image data.

[0049] In the next step S402, the CPU 100 performs input color conversion on the input image data. This step is performed by the input color conversion processing unit 301 in Fig. 3. Through this processing, the input image data is converted into CMYK image data.

[0050] Next, in step S403, the CPU 100 determines whether or not a density correction table needs to be created. This step may be performed by the density correction processing unit 302 in FIG. 3. In this embodiment, it is determined whether a density correction table has already been created, and if not, it is determined that creation is necessary. It is also determined whether a predetermined time has passed since the last density correction table was created, and if the predetermined time has passed, it is also determined that creation is necessary.

[0051] If the result of the determination in step S403 is that it is not necessary to create a density correction table, the process proceeds to step S405. On the other hand, if it is necessary to create a density correction table, the process proceeds to step S404. In step S404, the CPU 100 creates the density correction table, and then the process proceeds to step S405. Step S404 is performed by the density correction table creation unit 305 in FIG. 3. Details of the density correction table creation process will be described later with reference to FIG. 5.

[0052] In step S405, the CPU 100 determines whether an HS correction table needs to be created. This step is performed by the HS correction processing unit 303 in FIG. 3. In this embodiment, it is determined whether an HS correction table has already been created, and if not, it is determined that creation is necessary. It is also determined whether a predetermined time has passed since the last HS correction table was created, and if the predetermined time has passed, it is also determined that creation is necessary.

[0053] If the result of the determination is that an HS correction table does not need to be created, the process proceeds to step S407. On the other hand, if creation is required, the process proceeds to step S406. In step S406, CPU 100 creates an HS correction table, and then the process proceeds to step S407. Details of the HS correction table creation process will be described later with reference to FIG. 8. Step S406 is performed by HS correction table creation unit 307 in FIG. 3.

[0054] Note that the density correction table creation process in step S404 and the HS correction table creation process in step S406 are executed in parallel. That is, HS correction is not applied to the print data of the chart used in the density correction table creation process, and density correction processing is not applied to the print data of the chart used in the HS correction table creation process. Density correction may be applied to the print data of the chart used in the density correction table creation process, and HS correction processing may be applied to the print data of the chart used in the HS correction table creation process. In this case, each created correction table is the difference from the correction table used in the applied correction processing, and a new correction table may be created by applying that difference. Also, neither density correction nor HS correction may be applied to any of the charts. In this case, each created correction table becomes the respective new correction table. Also, S403-S404 and S405-S406 may be executed serially. In that case, the order of these processes does not matter.

[0055] In step S407, the CPU 100 performs density correction processing by referring to the density correction table 306. This step is performed by the density correction processing unit 302 in FIG.

[0056] Next, in step S408, the CPU 100 performs HS correction processing by referring to the HS correction table 307. This step is performed by the HS correction processing unit 303 in FIG.

[0057] Next, in step S409, the CPU 100 performs HT processing on the image data after the HS correction processing. This step is performed by the HT processing unit 304 in FIG.

[0058] Next, in step S410, CPU 100 inputs the image data that has undergone HT processing to image forming section 107, and image forming section 107 forms an image on paper based on the HT image.

[0059] By following the steps S401 to S410 above, the image specified by the user can be printed.

[0060] (S404, density correction table creation process) The process of creating the density correction table in step S404 will be described below with reference to the flowchart shown in Fig. 5. The process of Fig. 5 is executed mainly by the CPU 100. The process of Fig. 5 is also executed mainly by the density correction table creation unit 305 in Fig. 3.

[0061] First, in step S501, CPU 100 prints a density correction chart for creating a density correction table. FIG. 6 shows an example of a density correction chart. The density correction chart is an image that serves as a reference for correction and can also be called a reference image. The data of the density correction chart, which is saved in advance, can also be called reference image data. The density correction chart 600 is composed of nine uniform density acquisition areas 601c-609c, 601m-609m, 601y-609y, and 601k-609k for each of the CMYK colors. The density acquisition areas (also called patches) are formed for each of the CMYK color components at positions in the width direction (x direction) corresponding to the colorimetry modules 110a-110d of the image colorimetry unit 110. The width of each density acquisition area may be short, but may be the width that is the target of colorimetry by the colorimetry modules 110a-110d. In other words, the density acquisition area for each color component is formed by a portion of the print head, rather than the entire print head. The density correction chart is HT processed without at least HS correction, and is printed on a medium such as paper by the image forming unit 107. Note that the image data of the saved density correction chart may have already been HT processed. In that case, HT processing does not need to be performed. It is desirable that the paper used to print the density correction chart is the same color as the paper used to print the input image data after correction.

[0062] Next, in step S502, the CPU 100 performs colorimetry of the printed density correction chart using the image colorimetry unit 110. The colorimetry values ​​of the density acquisition areas for each color component of CMYK are measured by the colorimetry modules 110a to 110d, respectively. In this embodiment, colorimetry values ​​are acquired in nine density acquisition areas for each color. In this embodiment, the Y component of CIEXYZ is used as the colorimetry value. However, the Z component of CIEXYZ is used for the colorimetry value of yellow ink only.

[0063] Next, in step S503, CPU 100 acquires a measurement curve. Here, the measurement curve represents the density characteristics for each head, and is a curve showing the correspondence between input signal values ​​and colorimetric values, obtained by interpolating the colorimetric values ​​of the density acquisition areas for each density obtained for each color component. While colorimetric values ​​do not necessarily directly indicate density, it is possible to uniquely determine density, for example, the density of each CMYK color component, so the measurement curve can be said to represent density characteristics. This characteristic can also be called input / output characteristics.

[0064] FIG. 12 shows an example of an acquired measurement curve, i.e., density characteristics. The horizontal axis of FIG. 12 represents the input signal value of each gradation 601 to 609 of the density correction chart, and the vertical axis represents the colorimetric value. In the figure, a measurement curve 1201 is obtained by interpolation from the read image signal values ​​of each gradation 601 to 609. In this embodiment, piecewise linear interpolation is used as the interpolation method. Any interpolation method can be used, and a known spline curve, for example, may be used. The measured values ​​may be in a color system such as CIEXYZ or L*a*b*. When the measured values ​​are values ​​in the L*a*b* color system, the following value D may be used as the vertical axis (colorimetric value) of FIG. 12.

[0065] D=√((L-Lw) 2 +(a-aw) 2 +(b-bw) 2 ) Here, L, a, b are the values ​​of each L*a*b* component of the measured value, and Lw, aw, bw are the values ​​of each L*a*b* component of a predetermined white (paper white). In other words, the value D indicates the distance from white. Note that the horizontal axis of FIG. 12 may represent the values ​​of each color component of the input signal value, for example, each of YMCK.

[0066] Next, in step S504, the CPU 100 acquires target characteristics. In this embodiment, values ​​set in advance for each ink color are acquired from the external storage device 105 and used as the target characteristics. An example of the target characteristics is shown as a characteristic curve 1202 in FIG. 12. The target characteristics can also be called target density characteristics.

[0067] Next, in step S505, the CPU 100 initializes the input signal value. Here, the input signal value is a value corresponding to the input signal value 701 in the density correction table shown in Fig. 7. In this embodiment, the input signal value is initialized to 0, which is the first value of the input signal value 701.

[0068] Next, in step S506, CPU 100 calculates a correction value. The correction value calculation process will be described with reference to FIG. 12. The current input signal value is shown in FIG. 12 as input signal value 1203. At this time, the value of target characteristic 1202 corresponding to input signal value 1203 is obtained and set as target value 1204. Furthermore, the input signal value corresponding to target value 1204 is obtained from measurement curve 1201, and this signal value is set as correction value 1205 corresponding to the input signal value. The correction value 1205 thus obtained is associated with input signal value 1203 and stored in density correction table 306. As a result, by correcting input signal value 1203 to correction value 1205, it is possible to form a color on the medium that corresponds to target value 1204 for input signal value 1203.

[0069] Returning to FIG. 5, in step S507, it is determined whether correction values ​​have been calculated for all input signal values ​​in the density correction table 306. If there are unprocessed input signal values, the input signal value is advanced to the next, and the process returns to step S506. The next input signal value is determined by the granularity or step of the density correction table 306, and in the example of FIG. 7, the next input signal value is the current value plus 16. If the next input signal value exceeds the maximum value of the input signal value, that maximum value can be used as the next input signal value. If calculations have been completed for all input signal values, the process proceeds to step S508.

[0070] In step S508, the CPU 100 associates the calculated correction value for each head (that is, for each CMYK color component) with the input signal value, and stores the result as a density correction table 306 in a storage area such as the external storage device 105.

[0071] The above series of steps completes the density correction table creation process.

[0072] (S406, HS correction table creation process) The HS correction table creation process in step S406 will be described below with reference to the flowchart shown in Fig. 8. The process in Fig. 8 is executed mainly by the CPU 100. The process in Fig. 8 is also executed mainly by the HS correction table creation unit 307 in Fig. 3. The following process is performed for each ink color. The ink color (color component) that is the focus of the process in Fig. 8 is called the target color component.

[0073] First, in step S801, CPU 100 prints an HS chart of the target color component for creating an HS correction table. An example of an HS chart is shown in Figure 9. The HS chart is an image that serves as the basis for correction and can also be called a reference image, and the data of the HS correction chart that has been saved in advance can be called reference image data.

[0074] The HS chart 900 includes irregularity acquisition areas 901-909 with uniform (i.e., constant) color signal values ​​of a predetermined number of gradations, for example, nine gradations. It also includes markers 910a-910j for associating the nozzle positions of the print head with the positions of the irregularity acquisition areas. As shown in FIG. 9, the markers in this embodiment are multiple line segments extending in the y direction and arranged at predetermined intervals. The HS chart is HT processed without at least density correction, and is printed on a medium such as paper by the image forming unit 107.

[0075] Returning to FIG. 8, in step S802, the recorded HS chart is imaged by the image acquisition unit 108. The captured image is stored as two-dimensional read image data. It is desirable that the read image data be equal to or larger than the size recorded by all nozzles of the recording head in the width direction (X direction).

[0076] Next, in step S803, CPU 100 extracts an unevenness acquisition area from the read image data and calculates one-dimensional data (line profile) averaged in the transport direction (y direction). In this embodiment, nine line profiles corresponding to each of unevenness acquisition areas 901 to 909 are obtained.

[0077] Next, in step S804, CPU 100 associates the positions of the scanned image with the nozzle numbers. Specifically, markers 910a-910j (Fig. 9) are detected from the scanned image, and the positions of each marker are associated with the nozzle numbers that recorded the markers. For markers 910a-910j, the nozzles that recorded them can be identified based on their positions in the image data of the HS chart, so the markers scanned from the HS chart formed as an image can be associated with the nozzle numbers that recorded them.

[0078] Next, in step S805, the CPU 100 initializes the nozzle number of interest, which in this embodiment is set to nozzle number 0 for the nozzle at the left end of the print head.

[0079] Next, in step S806, the CPU 100 calculates the correction amount for the nozzle of interest. Details of the per-nozzle correction amount calculation process will be described later.

[0080] Next, in step S807, CPU 100 determines whether correction amounts have been calculated for all nozzles. If there are unprocessed nozzles, the nozzle number of interest is incremented by one, and the process returns to step S806. If the nozzle number of interest is incremented, the next nozzle number is determined by the granularity or step of HS correction table 308; in the example of FIG. 10, the next nozzle number is the current value plus 8. If the next nozzle number exceeds the maximum value of the nozzle number, that maximum value may be used as the next nozzle number. If calculation of correction values ​​has been completed for all nozzles, the process proceeds to step S808.

[0081] In step S808, the calculated correction amount for each nozzle is stored as an HS correction table for the target color component.

[0082] The above series of processes completes the HS correction table creation process for the target color component. This is then performed for each of the CMYK ink color components to complete the HS correction table.

[0083] (S806, calculation of correction amount for each nozzle) The correction amount calculation process for each nozzle in step S806 will be described below with reference to FIGS. 11 and 12. FIG. 11 is a flowchart of the correction amount calculation process for each nozzle. As with density correction, FIG. 12 is referenced because HS correction is performed in the same manner as density correction, and the density characteristics to be corrected may be different between HS correction and density correction, and the target characteristics are different between them. The process of FIG. 11 is for calculating the correction amount for the nozzle of interest that was selected in the process of FIG. 8. The process of FIG. 11 is also executed primarily by the CPU 100. Alternatively, the process of FIG. 11 is executed primarily by the HS correction table creation unit 307 in FIG. 3.

[0084] First, in step S1101, CPU 100 acquires a measurement curve. Here, the measurement curve represents the measurement characteristics (also called density characteristics or input / output characteristics) of the input signal value and read value of the nozzle of interest, and is a curve obtained by interpolating the signal values ​​of the read image.

[0085] FIG. 12 shows an example of an acquired measurement curve. The horizontal axis of FIG. 12 is the input signal value for each gradation 901 to 909 of the HS chart, and the vertical axis is the y-axis average value of the read values ​​for each gradation (also called the read signal value or read image signal value). In other words, the vertical axis is the read value at the position corresponding to the nozzle of interest, included in the line profile of each density. Note that if the read image is expressed in the RGB color system, for example, it may be converted to L*a*b*, and the value D may be calculated in the same way as for density correction, and used as the read signal value on the vertical axis of FIG. 12. Alternatively, the distance from the origin of each RGB color component may be calculated, and this value may be used as the read signal value on the vertical axis of FIG. 12.

[0086] In the figure, a measurement curve 1201 is obtained by interpolation calculation from the input signal values ​​of each gradation 901 to 909 and the read image signal values ​​corresponding to each input signal value. In this embodiment, piecewise linear interpolation is used as the interpolation method. Any interpolation method can be used, and a known spline curve or the like may also be used. The measurement curve 1201 differs depending on the characteristics of the nozzle. For example, for a nozzle with a small ejection volume, the curve shifts upward (towards brighter).

[0087] Next, in step S1102, the CPU 100 acquires the target characteristic. In this embodiment, the target characteristic is the average value of the read values ​​by the image acquisition unit 108 at the colorimetric position of each gradation by the image colorimetric unit 110. Since the target characteristic acquired here is a discrete characteristic corresponding to discrete input density values ​​(e.g., 9 gradations), a continuous target characteristic is obtained by interpolating the read values ​​for each input density value using, for example, linear interpolation. An example of the target characteristic is shown in the measurement curve 1202 in FIG. 12.

[0088] 9 shows the positions and ranges of colorimetry targets of the colorimetry modules 110a to 110d of the image colorimetry unit 110 as positions 911c, 911m, 911y, and 911k. Position 911c is the position where the density acquisition regions 601c to 609c corresponding to color component C of the density correction chart in FIG. 6 are measured. Similarly, position 911m is the position where the density acquisition regions 601m to 609m corresponding to color component M are measured, position 911y is the position where the density acquisition regions 601y to 609y corresponding to color component Y are measured, and position 911k is the position where the density acquisition regions 601k to 609k corresponding to color component K are measured. For example, when creating an HS correction table for cyan ink, the target value is the average value in the y and x directions of the read values ​​for each gradation at position 911c of the unevenness acquisition regions 901 to 909. Here, position 911c indicates the position and range to be measured by the corresponding color measurement module, and since averaging in the y direction has already been performed using the line profile, it is sufficient to perform averaging in the x direction. Averaging in the x direction (width direction) can be performed by calculating the average value in the range corresponding to position 911c for the x direction of the line profile of each gradation of the input signal value. This is also true for color components other than cyan. In this way, target values ​​corresponding to each gradation of the input signal value are obtained, and the target characteristics are obtained by interpolating these values.

[0089] In other words, to obtain the target value for each gradation for each color component of HS correction, the measurement values ​​of the unevenness acquisition area for each gradation are averaged in the conveyance direction (y direction) of that unevenness acquisition area. The average value in the conveyance direction is then averaged across the width direction (x direction) for the color measurement range of the color measurement module corresponding to each color component. This allows the target value for HS correction to match, or at least approximate, the colorimetric value of the patch for density correction.

[0090] Next, in step S1103, the input signal value is initialized. Here, the input signal value is a value corresponding to the input signal value 1001 in the HS correction table shown in Fig. 10. In this embodiment, the input signal value is initialized to 0, which is the first value of the input signal value 1001.

[0091] Next, in step S1104, a correction amount is calculated. The correction amount calculation process will be described with reference to FIG. 12. The input signal value initialized in step S1103 is shown in FIG. 12 as input signal value 1203. At this time, the value of target characteristic 1202 corresponding to input signal value 1203 is obtained and set as target value 1204. Furthermore, a signal value corresponding to target value 1204 is obtained from measurement curve 1201, and this signal value is set as correction amount 1205. The correction amount 1205 thus obtained is associated with the input signal value 1203 and saved in the column for the current nozzle number of interest in the HS correction table 308. As a result, by correcting the input signal value 1203 to the correction value 1205, it is possible to reproduce on the medium a color that corresponds to the target value 1204 for the input signal value 1203.

[0092] Returning to FIG. 11, in step S1105, it is determined whether the correction amount has been calculated for all input signal values. If there are unprocessed input signal values, the input signal value is advanced to the next, and the process returns to step S1104. If calculations have been completed for all input signal values, the per-nozzle correction amount calculation process ends. The next input signal value is determined by the granularity or step of the HS correction table 308; in the example of FIG. 10, the next input signal value is the current value plus 16. If the next input signal value exceeds the maximum input signal value, that maximum value can be used as the next input signal value.

[0093] As described above, when creating a density correction table, a first measurement characteristic based on the colorimetric values ​​obtained by measuring an image formed corresponding to each input signal value with a colorimetry module is corrected to a given first target characteristic. When creating an HS correction table, a second measurement characteristic based on the acquired values ​​obtained by a line sensor of the density of an image formed corresponding to each input signal value is corrected to a second target characteristic. Here, the second target characteristic is a target characteristic that sets the target value as the acquired values ​​of a portion of the acquired values ​​obtained by the line sensor that corresponds to the measurement area of ​​the colorimetry module that measures the image when determining the first measurement characteristic, particularly the average value of that portion. The density correction table and HS correction table created in this way are used to perform density correction processing and HS correction processing on the input image data.

[0094] (Image formation processing) Once the density correction table 306 and the HS correction table 308 have been created and saved using the procedures shown in Figures 5 and 8, the input image data is subjected to HS correction and density correction using the respective correction tables, as explained in Figure 4, and an image is formed. For example, the measurement characteristics of one of the nozzles to be corrected are indicated by the density characteristics. For the input signal value, a density correction value is first obtained through density correction processing, such that the colorimetric value of the reproduced color becomes the target value. Then, using the density correction value as the input value, an HS correction value is obtained through HS correction processing, such that the colorimetric value of the recorded color becomes the target value. If the HS correction value is used as the corrected input signal value, an image is formed such that the colorimetric value becomes the target value.

[0095] (effect) The effects of the correction process according to the present disclosure will be described below. As described above, in this embodiment, the density correction table creation process in step S404 and the HS correction table creation process in step S406 are executed in parallel. In other words, each correction table can be created independently without relying on either correction process.

[0096] As an example of conventional HS correction processing, consider a case where the average read value of the entire width of the unevenness acquisition areas 901-909 or a preset value is used as the target characteristic in S1102. In such a case, the read value of the density acquisition area at the colorimetric position for density correction processing may differ from the target value for HS correction processing corresponding to the input signal value of the read density acquisition area. In this case, the HS correction processing may change the density at the colorimetric position for density correction processing in order to correct the density unevenness. Meanwhile, the density correction processing calculates the correction amount by measuring the color of a chart that has not been subjected to HS correction processing. As a result, consistency between the HS correction processing and the density correction processing cannot be achieved, resulting in a failure of the density correction.

[0097] Therefore, in this embodiment, the target characteristics of the HS correction process acquired in S1102 are set to values ​​obtained by averaging, in the x direction, the read values ​​at the colorimetric positions of the density correction process in the unevenness acquisition areas 901 to 909. By doing so, the HS correction process can correct density unevenness without changing (or while roughly maintaining) the density at the colorimetric positions of the density correction process, thereby solving the above-mentioned problem.

[0098] As described above, according to this embodiment, even if printing and measuring the density correction processing chart and printing and measuring the HS processing are performed simultaneously, the processes do not interfere with each other (i.e., the corrections do not affect each other). Therefore, it is possible to achieve appropriate correction while suppressing the length of work time.

[0099] (Variation) In the above embodiment, an example has been described in which density correction processing and HS correction processing are performed on image data, but similar effects can also be obtained by performing correction on the threshold matrix used in the dither method.

[0100] The determination of whether or not to create the density correction table and HS correction table may be based on criteria different from those described above. For example, the determination may be based on the number of ink ejections since the table was created. The determination may also be based on printing and measuring a simple evaluation chart. The determination of whether or not to create the tables may also be based on the timing of performing maintenance processes such as head cleaning.

[0101] Furthermore, the colorimetric values ​​used for density correction are not limited to the CIEXYZ color space, and any color space such as CIELab can be used. Similarly, the scanned image used for HS correction processing is not limited to RGB image signals, and may be monochrome density.

[0102] The correction table may be created by an external information processing device other than the image forming device, and the created correction table may be loaded into the image forming device to perform the correction process.Furthermore, the correction process may be performed by an external information processing device, and the corrected image data may be passed to the image forming device to form an image.

[0103] Furthermore, in this embodiment, both density correction and HS correction may be performed using an HS chart for HS correction, such as that shown in FIG. 9. In this case, the HS chart is printed without density correction or HS correction. Furthermore, while the density correction chart shown in FIG. 6 has patches of different colors formed across the width of a single sheet, the HS chart shown in FIG. 9 has measurement areas (patches) of a single color with different densities formed on a single sheet. Therefore, color measurement may be performed using a color measurement module corresponding to each color, as in the above embodiment, or multiple colors may be measured using a single color measurement module. In either case, the measurement values ​​of the areas acquired by the image acquisition unit corresponding to the measurement areas of the color measurement modules are used to determine the target characteristics for HS correction, as in the above embodiment. According to this modification, density correction and HS correction can be performed using only the HS chart.

[0104] Second Embodiment In the first embodiment, an example was described in which the target value of the HS correction process is set to the average value of the colorimetric values ​​at the colorimetric measurement position for density correction, thereby preventing interference between the HS correction process and the density correction process. In the second embodiment, an example will be described in which it is possible to deal with cases in which there is significant density unevenness at the colorimetric measurement position. The configuration of this embodiment is the same as the configuration shown in FIGS. 1 to 3 of the first embodiment. Therefore, differences from the first embodiment will be mainly described, and similarities will be omitted. The flow of printing a user image in this embodiment will be described below using the flow diagram shown in FIG. 13. The processing in FIG. 13 is executed by the CPU 100, as in FIG. 4. From a software perspective, one of the processing units in the image processing unit 106 in FIG. 3 will be the main unit.

[0105] First, in step S1301, the CPU 100 accepts a print job input by a user and stores the input image data in the external storage device 105, for example.

[0106] Next, in step S1302, the CPU 100 performs input color conversion on the input image data. This step is performed by the input color conversion processing unit 301 in FIG.

[0107] Next, in step S1303, CPU 100 determines whether or not a density correction table needs to be created. This step may be performed by density correction processing unit 302 in FIG. 3. This determination may be made in the same manner as step S403 in FIG. 4. If the result of the determination is that creation of a density correction table is not necessary, the process proceeds to step S1309. On the other hand, if creation is necessary, the process proceeds to step S1304, where CPU 100 creates and saves the density correction table, and then proceeds to step S1309. Step S1304 is performed by density correction table creation unit 305 in FIG. 3. Details of the creation of the density table may be as shown in FIG. 5.

[0108] In step S1305, the CPU 100 determines whether or not an HS correction table needs to be created. This step is performed by the HS correction table creation unit 305 in FIG. 3. If the result of the determination is that an HS correction table does not need to be created, the process proceeds to step S1309. On the other hand, if creation is necessary, the process proceeds to step S1306, where the CPU 100 creates the HS correction table, and then the process proceeds to step S1307. Step S1306 is performed by the HS correction table creation unit 307 in FIG. 3. The details of the density table creation may be as shown in FIG. 8.

[0109] In step S1307, the CPU 100 determines the magnitude of unevenness, i.e., non-uniformity, of the acquired values ​​(pixel values) acquired by the image acquisition unit 108 at the colorimetry position by the image colorimetry unit 110. In this embodiment, for each color component, the average value in the y direction at the colorimetry position for each gradation is calculated, and the difference between the maximum and minimum values ​​is evaluated. If the difference is less than a predetermined value, it is determined that the density unevenness, i.e., non-uniformity, for that color component is smaller than a standard; if the difference exceeds the predetermined value, it is determined that the density unevenness, i.e., non-uniformity, is larger than a standard. Alternatively, the variance of the density values ​​acquired by the image acquisition unit 108 at the colorimetry position by the image colorimetry unit 110 may be calculated, and if the variance is less than a threshold, it may be determined that the non-uniformity is small, and if the variance exceeds the threshold, it may be determined that the non-uniformity is large.

[0110] If it is determined that the density unevenness is small, the process proceeds to step S1310. If it is determined that the density unevenness is small, the process is the same as in the first embodiment. On the other hand, if it is determined that the unevenness is large, the process proceeds to step S1308.

[0111] In step S1308, CPU 100 interrupts the correction table creation process (S1304) that may be progressing in parallel, and proceeds to S1309. In S1309, HS correction processing using the HS correction table generated in step S1306 is applied to the density correction chart shown in Fig. 6, and then the density correction chart to which HS correction has been applied is printed and measured, thereby creating a density correction table. The processing in step S1309 is the same as S1304, except that HS correction is applied to the density correction chart.

[0112] As in the first embodiment, the density correction table creation process in step S1304 and the HS correction table creation process in step S1306 are executed in parallel. However, if it is determined in step S1307 that there is significant unevenness in the colorimetry position, the process of S1304 is interrupted, and a process is executed to print and measure the density correction chart to which the HS correction process has been applied.

[0113] In step S1310, the density correction processing unit 302 refers to the density correction table 306 and performs density correction processing.

[0114] In step S1311, the HS processing unit 303 refers to the HS correction table 307 and performs HS correction processing.

[0115] In step S1312, the HT processing unit 304 performs HT processing on the image data after the HS correction processing.

[0116] In step S1313, the image forming unit 107 forms an image on the paper surface based on the HT image.

[0117] By following the above steps S1301 to S1312, it is possible to print the image specified by the user. In particular, for the density correction chart, it is possible to suppress uneven density in each density measurement area (patch) and form a uniform patch with the average density of each area.

[0118] (effect) As described above, according to this embodiment, a density correction chart to which unevenness correction has been applied is used only when unevenness at the color measurement position is large, thereby making it possible to achieve appropriate correction while minimizing the time required for the work.

[0119] (Other embodiments) In the above embodiment, it is assumed that the reading characteristics of the image acquisition unit 108 are uniform, but the present invention can also be applied when the reading characteristics are non-uniform. In this case, it is desirable to create the density correction chart taking into account the reading characteristics of the image acquisition unit. For example, in a case where the reading uniformity differs for each color to be read, it is preferable to arrange the density acquisition areas 601c to 609k shown in FIG. 6 at positions where the reading uniformity is best.

[0120] [Further Examples] The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or device via a network or a storage medium, and having one or more processors in the computer of the system or device read and execute the program.The present invention can also be realized by a circuit (e.g., ASIC) that realizes one or more functions.

[0121] Summary of embodiments The above embodiments can be summarized as follows: (Item 1) An image processing device that processes an image recorded on a recording medium by a plurality of recording elements arranged in a recording head, a first acquisition means for acquiring a measurement value for a first image area recorded on a recording medium by at least a portion of the recording head; a second acquisition means for acquiring measurement values ​​for a second image area including the range of the first image area recorded on a recording medium by the recording head; a control means; The control means a first correction table is created for correcting a first measurement characteristic, which is determined by acquiring, by the first acquisition means, measurement values ​​of an image obtained by recording first reference image data on a recording medium by the recording head, and which associates input values ​​based on the first reference image data with the measurement values, to a given first target characteristic; a second correction table is created in which input values ​​based on the second reference image data and the measurement values ​​are associated with each other, the second correction table being used to correct second measurement characteristics of each of the plurality of recording elements to second target characteristics that are uniform across the plurality of recording elements; and The second target characteristic is a characteristic based on a measurement value of a portion of the second image region acquired by the second acquisition means that corresponds to the first image region. 1. An image processing device comprising: (Item 2) Item 1, the image processing device according to item 1, The second target characteristic is a characteristic based on an average value of the measurement values ​​of the second image region corresponding to the first image region. 1. An image processing device comprising: (Item 3) Item 1 or 2, the image processing device The control means further performs a first correction process using the first correction table on the input image data, and performs a second correction process using the second correction table on the input image data that has been subjected to the first correction process. 1. An image processing device comprising: (Item 4) Item 3. The image processing device according to any one of items 1 to 3, the first reference image data is image data in which the input value is uniform for the first image region, The second reference image data is image data in which the input values ​​are uniform for the second image region. 1. An image processing device comprising: (Item 5) Item 4. The image processing device according to item 4, the first reference image data includes a plurality of the first image regions having different input values; The second reference image data includes a plurality of the second image areas in which the input values ​​are different. 1. An image processing device comprising: (Item 6) 6. The image processing device according to any one of items 1 to 5, the first reference image data includes a plurality of the first image regions in which the input values ​​are different for each of a plurality of different color components; The second reference image data includes a plurality of second image areas in which the input values ​​are different for each of a plurality of different color components. 1. An image processing device comprising: (Item 7) Item 1 to 6, an image processing device according to any one of items 1 to 6, The second reference image data is used as the first reference image data. 1. An image processing device comprising: (Item 8) Item 3. The image processing device according to item 3, When the non-uniformity of the measurement values ​​acquired by the second acquisition means in a portion corresponding to the first image region is greater than a reference value, the control means creates the first correction table using the first reference image data to which the second correction process has been applied. 1. An image processing device comprising: (Item 9) Item 3. The image processing device according to item 3, the first reference image data has not been subjected to at least the second correction process; The second reference image data has not been subjected to at least the first correction process. 1. An image processing device comprising: (Item 10) Item 9. The image processing device according to item 9, further comprising an image forming means for forming an image by the recording head, The control means causes the image forming means to form images of the first reference image data and the second reference image data. 1. An image processing device comprising: (Item 11) Item 11. The image processing device according to item 10, The control means further executes the first correction processing on the input image data, and the second correction processing on the input image data that has been subjected to the first correction processing, and forms an image using the input image data that has been subjected to the second correction processing by the image forming means. 1. An image processing device comprising: (Item 12) Item 11: An image processing device according to any one of items 1 to 11, The recording head is configured by connecting a plurality of recording chips each having a plurality of recording elements, The first acquisition means is arranged so that the first image area does not cross the boundary between the plurality of recording chips. 1. An image processing device comprising: (Item 13) Item 13. The image processing device according to any one of items 1 to 12, The measurements are expressed in optical density, CIE XYZ, or CIE L*a*b*. 1. An image processing device comprising: (Item 14) A method for creating a correction table by an image processing device that processes an image recorded on a recording medium by a plurality of recording elements arranged in a recording head, wherein the image processing device has: a first acquisition means that acquires measurement values ​​for a first image area recorded on the recording medium by at least a part of the recording head; a second acquisition means that acquires measurement values ​​for a second image area that includes the range of the first image area recorded on the recording medium by the recording head; and a control means; In the method for creating the correction table, the control means creates a first correction table for correcting a first measurement characteristic, which is determined by acquiring, by the first acquisition means, measurement values ​​of an image obtained by recording first reference image data on a recording medium by the recording head, and which associates input values ​​based on the first reference image data with the measurement values, to a given first target characteristic; the control means acquires, by the second acquisition means, measurement values ​​of an image obtained by recording second reference image data on a recording medium by the recording head, and creates a second correction table in which input values ​​based on the second reference image data and the measurement values ​​are associated with each other, for correcting second measurement characteristics of each of the plurality of recording elements to second target characteristics that are uniform across the plurality of recording elements; The second target characteristic is a characteristic based on a measurement value of a portion of the second image region acquired by the second acquisition means that corresponds to the first image region. A method for creating a correction table. The present invention is not limited to the above-described embodiments, and various changes and modifications can be made without departing from the spirit and scope of the invention. Therefore, the following claims are appended to apprise the public of the scope of the invention. [Explanation of symbols]

[0122] 100 CPU, 101 RAM, 103 operation unit, 104 display unit, 106 image processing unit, 108 image acquisition unit, 110 image colorimetry unit

Claims

1. An image processing device that processes an image recorded on a recording medium by a plurality of recording elements arranged in a recording head, a first acquisition means for acquiring a measurement value of a first image area recorded on a recording medium by at least a portion of the recording head; a second acquisition means for acquiring measurement values ​​for a second image area including the range of the first image area recorded on a recording medium by the recording head; a control means; The control means a first correction table is created for correcting a first measurement characteristic, which is determined by acquiring, by the first acquiring means, measurement values ​​of an image obtained by recording first reference image data on a recording medium by the recording head, and which associates input values ​​based on the first reference image data with the measurement values, to a given first target characteristic; a second correction table is created in which input values ​​based on the second reference image data and the measurement values ​​are associated with each other, the second correction table being used to correct second measurement characteristics of each of the plurality of recording elements to second target characteristics that are uniform across the plurality of recording elements; and the second correction table is created by acquiring, by the second acquisition means, measurement values ​​of an image obtained by recording second reference image data on a recording medium using the recording head, the measurement values ​​being identified by the second acquisition means; The second target characteristic is a characteristic based on a measurement value of a portion of the second image region acquired by the second acquisition means, the portion corresponding to the first image region.

1. An image processing device comprising:

2. 2. The image processing device according to claim 1, The second target characteristic is a characteristic based on an average value of the measurement values ​​of the second image region corresponding to the first image region.

1. An image processing device comprising:

3. 2. The image processing device according to claim 1, The control means further performs a first correction process using the first correction table on the input image data, and performs a second correction process using the second correction table on the input image data that has been subjected to the first correction process.

1. An image processing device comprising:

4. 2. The image processing device according to claim 1, the first reference image data is image data in which the input value is uniform for the first image region, The second reference image data is image data in which the input values ​​are uniform for the second image region.

1. An image processing device comprising:

5. 5. The image processing device according to claim 4, the first reference image data includes a plurality of the first image regions having different input values; The second reference image data includes a plurality of second image areas having different input values.

1. An image processing device comprising:

6. 2. The image processing device according to claim 1, the first reference image data includes a plurality of the first image areas in which the input values ​​are different for each of a plurality of different color components, The second reference image data includes a plurality of second image areas in which the input values ​​are different for each of a plurality of different color components.

1. An image processing device comprising:

7. 2. The image processing device according to claim 1, The second reference image data is used as the first reference image data.

1. An image processing device comprising:

8. 4. The image processing device according to claim 3, When the non-uniformity of the measurement values ​​acquired by the second acquisition means in a portion corresponding to the first image region is greater than a reference value, the control means creates the first correction table using the first reference image data to which the second correction process has been applied.

1. An image processing device comprising:

9. 4. The image processing device according to claim 3, the first reference image data has not been subjected to at least the second correction process; The second reference image data has not been subjected to at least the first correction process.

1. An image processing device comprising:

10. 10. The image processing device according to claim 9, further comprising an image forming means for forming an image by the recording head, The control means causes the image forming means to form images of the first reference image data and the second reference image data.

1. An image processing device comprising:

11. The image processing device according to claim 10, The control means further executes the first correction processing on the input image data, and the second correction processing on the input image data that has been subjected to the first correction processing, and forms an image using the input image data that has been subjected to the second correction processing by the image forming means.

1. An image processing device comprising:

12. 2. The image processing device according to claim 1, The recording head is configured by connecting a plurality of recording chips each having a plurality of recording elements, The first acquisition means is arranged so that the first image area does not cross the boundary between the plurality of recording chips.

1. An image processing device comprising:

13. 2. The image processing device according to claim 1, The measurements are expressed in optical density, CIE XYZ, or CIE L*a*b*.

1. An image processing device comprising:

14. A method for creating a correction table by an image processing device that processes an image recorded on a recording medium by a plurality of recording elements arranged in a recording head, the image processing device having a first acquisition means that acquires measurement values ​​for a first image area recorded on the recording medium by at least a part of the recording head, a second acquisition means that acquires measurement values ​​for a second image area that includes the range of the first image area recorded on the recording medium by the recording head, and a control means; In the method for creating the correction table, the control means creates a first correction table for correcting a first measurement characteristic, which is determined by acquiring, by the first acquisition means, measurement values ​​of an image obtained by recording first reference image data on a recording medium by the recording head, and which associates input values ​​based on the first reference image data with the measurement values, to a given first target characteristic; the control means acquires, by the second acquisition means, measurement values ​​of an image obtained by recording second reference image data on a recording medium by the recording head, and creates a second correction table in which input values ​​based on the second reference image data and the measurement values ​​are associated with each other, for correcting second measurement characteristics of each of the plurality of recording elements to second target characteristics that are uniform across the plurality of recording elements; The second target characteristic is a characteristic based on a measurement value of a portion of the second image region acquired by the second acquisition means, the portion corresponding to the first image region. A method for creating a correction table.

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