Image processing apparatus and method for controlling the same, and program

Abstract

To solve the problem in which: when an image abnormality being a white dot occurs in a test chart formed of single-color patches, it is difficult to distinguish whether the image abnormality occurs in a developing unit that develops toner to form the single patches or occurs in a secondary transfer unit that may cause the image abnormality in every single-color patch part.SOLUTION: An image processing apparatus reads an image of a chart on which a test image is printed to acquire a read image, detects an image abnormality included in the read image, and acquires the feature quantity of the detected image abnormality. The image processing apparatus specifies the cause of the occurrence of the image abnormality based on the feature quantity. The test image includes at least one mixed color image in multi-valued colors being secondary or higher-valued colors.SELECTED DRAWING: Figure 8

Description

[Technical field]

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

[0002] There is an image diagnostic technique for diagnosing the cause of image abnormalities in an image forming apparatus. Patent Document 1 describes an image diagnostic technique using a first chart and a second chart. The first chart is a chart in which a pattern is printed only on the first side of a recording medium in a single-sided mode, and the reverse side is a non-printed side. The second chart is a chart in which a pattern is printed on one side of a recording medium different from the recording medium of the first chart in a double-sided mode, and a white image is printed on the reverse side. Images of the recording media on which the first chart and the second chart are output are read to obtain read images, and the side to be used for diagnosis is determined according to the image diagnosis item based on the read images to perform diagnosis. It is described that this allows for proper diagnosis of images while saving recording media (materials) and time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-133020 A Summary of the Invention [Problem to be solved by the invention]

[0004] In image diagnosis technology, since it is necessary to perform image diagnosis with higher accuracy, it is required to accurately identify the cause of the image abnormality that occurs. For example, in the development section of an image forming station that forms an image using a photosensitive drum, and in the secondary transfer section that transfers a toner image from an intermediate transfer belt, similar shaped image abnormalities with white spots occur. Image abnormalities with white spots caused by the development section occur only in the image section of the toner color contained in the developer. Image abnormalities with white spots caused by the secondary transfer section occur in image sections of any color, and in multi-color image sections where multiple color toners are layered, multiple color toners are transferred inadequately and become white.

[0005] However, when an image abnormality such as whiteout occurs on a test chart composed of single-color patches as shown in Patent Document 1, it is difficult to determine whether the abnormality occurred in the developing unit that develops the toner that forms the single-color patches, or in the secondary transfer unit, where there is also a possibility that the abnormality may occur in each single-color patch area.

[0006] An object of the present invention is to solve at least one of the problems of the above-mentioned conventional techniques.

[0007] An object of the present invention is to provide a technique that can easily identify the cause of an image abnormality that occurs in a printed test image. [Means for solving the problem]

[0008] In order to achieve the above object, an image processing device according to one aspect of the present invention has the following configuration. a reading means for reading an image of a chart on which a test image is printed and acquiring a read image; a detection means for detecting an image abnormality contained in the read image; an acquisition means for acquiring a feature amount of the image anomaly detected by the detection means; and an identification means for identifying a cause of the image abnormality based on the feature amount, and the test image includes at least one mixed color image of secondary or higher colors. Effect of the Invention

[0009] According to the present invention, it is possible to easily identify the cause of an image abnormality occurring in a printed test image.

[0010] Other features and advantages of the present invention will become apparent from the following description taken in conjunction with the accompanying drawings, in which the same reference numerals are used to designate the same or similar components throughout the drawings. [Brief description of the drawings]

[0011] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. [Figure 1] FIG. 1 is a diagram showing an example of a network configuration including a printing system (image processing system) according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a cross-sectional view showing an example of the hardware configuration of the image forming apparatus according to the first embodiment. [Diagram 3] FIG. 2 is a block diagram illustrating a schematic functional configuration of the image forming apparatus, an external controller, and a client PC according to the first embodiment. [Figure 4] 5 is a flowchart illustrating the procedure of image diagnosis processing in the printing system according to the first embodiment. [Diagram 5] FIG. 2 is a diagram showing an example of a test chart used in image diagnostic processing according to the first embodiment. [Figure 6] FIG. 2 is a diagram showing an example of a test chart set used in image diagnosis according to the first embodiment. [Figure 7] 5 is a flowchart for explaining the procedure of a comparison process between a reference image and a read image performed in S411 of FIG. 4 according to the first embodiment. [Figure 8] 5 is a flowchart illustrating a procedure of a process for determining whether or not a cause of an image abnormality occurs before transfer in the feature extraction process in S415 of FIG. 4 according to the first embodiment. [Figure 9] 1A and 1B are schematic diagrams showing an example of an image abnormality occurring in a color mixing chart and an image thereof after binarization. [Figure 10]5 is a flowchart illustrating the procedure of a comparison process performed in S411 of FIG. 4 by an inspection module according to the second embodiment of the present invention. [Figure 11] 8A to 8C are schematic diagrams illustrating separation of process colors using complementary color channels in the second embodiment. [Figure 12] 5 is a flowchart for explaining a process in which an inspection module according to the second embodiment determines whether or not a cause of an image abnormality is before transfer in the image abnormality feature extraction process in S415 of FIG. 4. [Figure 13] 11A and 11B are schematic diagrams showing a process of determining whether the cause of an image abnormality is before transfer or not from a color mixing chart according to the second embodiment. [Figure 14] 13A and 13B are schematic diagrams illustrating a process for determining a cause of an image anomaly using the L*C*h* channel according to a modification of the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, the embodiments of the present invention will be described in detail with reference to the attached drawings. Note that the following embodiments do not limit the invention according to the claims. Although the embodiments describe a number of features, not all of these features are essential to the invention, and the features may be combined in any manner. Furthermore, in the attached drawings, the same reference numbers are used for the same or similar configurations, and duplicated descriptions are omitted.

[0013] In the following description, the external controller may also be called an image processing controller, a digital front end, a print server, a DFE, etc. The image forming apparatus may also be called a multifunction device, a multifunction peripheral, or an MFP.

[0014] [Embodiment 1] FIG. 1 is a diagram showing an example of a network configuration including a printing system (image processing system) according to a first embodiment of the present invention.

[0015] This printing system 100 includes an image forming apparatus 101 and an external controller 102. The image forming apparatus 101 and the external controller 102 are communicatively connected via an internal LAN 105 and a video cable 106. The external controller 102 is communicatively connected to a client PC 103 via an external LAN 104.

[0016] The client PC 103 can issue a print instruction to the external controller 102 via the external LAN 104. A printer driver having a function of converting image data to be printed into a page description language (PDL) that can be processed by the external controller 102 is installed in the client PC 103. A user who wishes to print can issue a print instruction from various applications installed in the client PC 103 via the printer driver by operating the client PC 103. The printer driver transmits PDL data, which is print data, to the external controller 102 based on a print instruction from the user. When the external controller 102 receives the PDL data from the client PC 103, it analyzes and interprets the received PDL data. Based on the result of the interpretation, it performs a rasterization process, generates a bitmap image (print image data) with a resolution matching the image forming device 101, and issues a print job to the image forming device 101 to issue a print instruction. The resolution of the image forming device is usually 600 dpi, and in many cases, high definition is 1300 dpi. In the following, an example of a resolution of 600 dpi will be described.

[0017] Next, the image forming apparatus 101 will be described. In the image forming apparatus 101, a plurality of devices having different functions are connected, and it is configured to be capable of complex printing processes such as bookbinding. The image forming apparatus 101 has a printing module 107, an inserter 108, an inspection module 109, a stacker 110, and a finisher 111. Each module will be described below.

[0018] The printing module 107 prints an image in accordance with a print job and ejects the printed recording material. The printed recording material ejected from the printing module 107 is transported inside each device in the order of the inserter 108, the inspection module 109, the stacker 110, and the finisher 111. In the first embodiment, the image forming device 101 of the printing system 100 is an example of an image forming device, but the printing module 107 included in the image forming device 101 may also be referred to as an image forming device.

[0019] The printing module 107 forms (prints) an image on a recording material fed and conveyed from a paper feed unit disposed below the printing module 107 by using a toner (developer). The inserter 108 is a device that inserts, for example, a partition recording material for separating a series of recording materials conveyed from the printing module 107 at an arbitrary position. The inspection module (image processing device) 109 is a device that inspects printing abnormalities (image abnormalities) of the printed recording material on which an image is printed by the printing module 107 and conveyed through a conveying path. Specifically, the inspection module 109 reads the image printed on the conveyed printed recording material, and compares the obtained read image with a reference image registered in advance to determine whether the image printed on the printed recording material is normal or not, and inspects the presence or absence of image abnormalities. The image abnormalities include, for example, defects and unevenness in the form of dots or streaks. The stacker 110 is a device that can stack a large number of printed recording materials. The finisher 111 is a device capable of performing finishing processes such as stapling, punching, saddle stitching, etc. on the conveyed printed recording material. The recording material processed by the finisher 111 is discharged to a predetermined discharge tray.

[0020] 1, an external controller 102 is connected to the image forming apparatus 101, but this embodiment can also be applied to a different configuration. For example, a configuration may be used in which the image forming apparatus 101 is connected to an external LAN 104, and print data is sent from a client PC 103 to the image forming apparatus 101 without going through the external controller 102. In this case, data analysis and rasterization of the print data are performed by the image forming apparatus 101.

[0021] 2 is a cross-sectional view showing an example of the hardware configuration of the image forming apparatus 101 according to the embodiment 1. Hereinafter, a specific operation example of the image forming apparatus 101 will be described with reference to FIG.

[0022] In the printing module 107, various recording materials are stored in the paper feed decks 301 and 302. Of the recording materials stored in each paper feed deck, the uppermost recording material is separated one by one and fed to a conveying path 303. The image forming stations 304 to 307 each include a photosensitive drum (photoconductor) and form a toner image on the photosensitive drum using toner of a different color. Specifically, the image forming stations 304 to 307 form a toner image using toner of yellow (Y), magenta (M), cyan (C), and black (K), respectively.

[0023] The toner images of each color formed in the image forming stations 304 to 307 are transferred onto the intermediate transfer belt 308 in order, superimposed on top of each other (primary transfer). The toner images transferred onto the intermediate transfer belt 308 are transported to a secondary transfer position 309 as the intermediate transfer belt 308 rotates. At the secondary transfer position 309, the toner image is transferred from the intermediate transfer belt 308 onto the recording material transported along the transport path 303 (secondary transfer). The recording material after the secondary transfer is transported to a fixing unit 311. The fixing unit 311 includes a pressure roller and a heating roller. Heat and pressure are applied to the recording material while the recording material passes between these rollers, thereby performing a fixing process in which the toner image is fixed onto the recording material. The recording material that has passed through the fixing unit 311 is transported to a connection point 315 between the print module 107 and the inserter via a transport path 312. In this manner, a color image is formed (printed) on the recording material.

[0024] When further fixing processing is required depending on the type of recording material, the recording material that has passed the fixing unit 311 is guided to a conveying path 314 provided with a fixing unit 313. The fixing unit 313 performs further fixing processing on the recording material conveyed on the conveying path 314. The recording material that has passed the fixing unit 313 is conveyed to a connection point 315. When an operation mode for performing double-sided printing is set, an image is printed on the first side, and the recording material conveyed on the conveying path 312 or the conveying path 314 is guided to a reversing path 316. The recording material that has been reversed on the reversing path 316 is guided to a double-sided conveying path 317 and conveyed to a secondary transfer position 309. As a result, a toner image is transferred to a second side of the recording material that is opposite to the first side at the secondary transfer position 309. Thereafter, the recording material passes through the fixing unit 311 (and the fixing unit 313), completing the formation of a color image on the second side of the recording material.

[0025] When image formation (printing) in the printing module 107 is complete, the printed recording material that has been transported to the connection point 315 is transported into the inserter 108. The inserter 108 has an inserter tray 321 on which the recording material to be inserted is set. The inserter 108 inserts the recording material fed from the inserter tray 321 into an arbitrary insertion position in a series of printed recording materials transported from the printing module 107, and transports the recording material to a downstream device (inspection module 109). The printed recording materials that have passed through the inserter 108 are transported in order to the inspection module 109.

[0026] The inspection module 109 includes image reading units 331 and 332 each having a contact image sensor (CIS) on a transport path 330 along which the printed recording material from the inserter 108 is transported. The image reading units 331 and 332 are disposed in positions facing each other across the transport path 330. The image reading units 331 and 332 are configured to read the top surface (first surface) and bottom surface (second surface) of the recording material, respectively. Note that the image reading unit may be configured with, for example, a charge coupled device (CCD) or a line scan camera instead of the CIS.

[0027] The inspection module 109 performs an inspection process to inspect an image printed on a printed recording material being transported along a transport path 330. Specifically, the inspection module 109 performs a reading process to read an image on the printed recording material using image reading units 331 and 332 at the timing when the printed recording material being transported reaches a predetermined position. Furthermore, the inspection module 109 inspects the image printed on the recording material based on the image obtained by the reading process. The recording materials that have passed the inspection module 109 are transported to the stacker 110 in order.

[0028] In the first embodiment, the inspection module 109 performs a process of inspecting an image for abnormalities by comparing a read image obtained by reading an image printed on a printed recording material with a reference image registered in advance. Methods of comparing images in this inspection process include, for example, a method of comparing pixel values ​​for each pixel, and a method of comparing the position of an object obtained by edge detection. Another method uses extraction of character data by OCR (Optical Character Recognition). The inspection module 109 also performs an abnormality inspection process for preset inspection items. Inspection items include, for example, misalignment of the print position of an image, color tone of an image, image density, streaks or blurs that have occurred in an image, printing defects, etc.

[0029] The stacker 110 includes a stack tray 341 as a tray on which printed recording materials transported from an inspection module 109 disposed upstream in the transport direction of the printed recording materials are stacked. The printed recording materials that have passed through the inspection module 109 are transported along a transport path 344 in the stacker 110. The printed recording materials transported along the transport path 344 are guided to a transport path 345, whereby the printed recording materials are stacked on the stack tray 341.

[0030] The stacker 110 further includes an escape tray 346 as a paper discharge tray. In the present embodiment 1, the escape tray 346 is used for discharging printed recording materials that have been determined to have an abnormality in the printed image as a result of the abnormality inspection by the inspection module 109. The printed recording materials conveyed along the conveying path 344 are guided to a conveying path 347 and conveyed to the escape tray 346. The printed recording materials that are conveyed without being stacked and discharged in the stacker 110 are conveyed via a conveying path 348 to the subsequent finisher 111.

[0031] The stacker 110 further includes an inverting unit 349 for inverting the orientation of the printed recording material being transported. The inverting unit 349 is used, for example, to make the orientation of the recording material input to the stacker 110 the same as the orientation of the printed recording material when it is stacked on the stack tray 341 and output from the stacker 110. Note that the inverting operation by the inverting unit 349 is not performed on the printed recording material that is not stacked in the stacker 110 and is transported to the finisher 111.

[0032] The finisher 111 executes a finishing function designated by a user on the printed recording material conveyed from the inspection module 109 arranged upstream in the conveying direction of the printed recording material. In the first embodiment, the finisher 111 has finishing functions such as a staple function (one or two-point binding), a punch function (two or three holes), and a saddle stitch binding function. The finisher 111 includes two paper discharge trays 351 and 352. When the finishing process is not performed by the finisher 111, the printed recording material conveyed to the finisher 111 is discharged to the paper discharge tray 351 through a conveying path 353. When the finishing process such as stapling is performed by the finisher 111, the printed recording material conveyed to the finisher 111 is guided to a conveying path 354. The finisher 111 uses a processing section 355 to perform a finishing process designated by the user on the printed recording material conveyed along a conveying path 354, and discharges the printed recording material on which the finishing process has been performed to a paper discharge tray 352. When saddle stitching is designated, a saddle stitching processing section 356 staples the center of the sheet, folds the sheet in two, and outputs the sheet to a saddle stitching tray 358 via a sheet conveying path 357. The saddle stitching tray 358 is configured as a belt conveyor, and the saddle stitched bundle loaded on the saddle stitching tray 358 is configured to be conveyed to the left side.

[0033] FIG. 3 is a block diagram illustrating the schematic functional configuration of the image forming apparatus 101, the external controller 102, and the client PC 103 according to the first embodiment.

[0034] The print module 107 of the image forming apparatus 101 includes a communication I / F (interface) 201, a network I / F 204, a video I / F 205, a CPU 206, a memory 207, an HDD unit 208, and a UI display unit 225. The print module 107 further includes an image processing unit 202 and a print unit 203. These are connected to each other via a system bus 209 so as to be able to transmit and receive data to and from each other.

[0035] The communication I / F 201 is connected to the inserter 108, the inspection module 109, the stacker 110, and the finisher 111 via a communication cable 260. The CPU 206 communicates through the communication I / F 201 to control each device. The network I / F 204 is connected to the external controller 102 via the internal LAN 105, and is used for communication of control data and the like. The video I / F 205 is connected to the external controller 102 via a video cable 106, and is used for communication of data such as image data. Note that the print module 107 (image forming apparatus 101) and the external controller 102 may be connected only by the video cable 106, as long as the external controller 102 can control the operation of the image forming apparatus 101.

[0036] Various programs and data are stored in the HDD unit 208. The CPU 206 controls the operation of the entire print module 107 by expanding the programs stored in the HDD unit 208 into the memory 207 and executing them. The memory 207 stores programs and data required when the CPU 206 performs various processes. The memory 207 operates as a work area for the CPU 206. The UI display unit 225 accepts various setting inputs and operation instructions from the user, and is used to display various information such as setting information and the processing status of a print job.

[0037] The inserter 108 controls the insertion of the recording material fed from the paper feed unit and the transport of the recording material transported from the print module 107 .

[0038] The inspection module 109 includes a communication I / F 211, a CPU 214, a memory 215, an HDD unit 216, image reading units 331 and 332, and a UI display unit 241. These devices are connected via a system bus 219 so as to be able to transmit and receive data to and from each other. The communication I / F 211 is connected to the printing module 107 via a communication cable 260. The CPU 214 performs communication required for controlling the inspection module 109 via the communication I / F 211. The CPU 214 controls the operation of the inspection module 109 by executing a control program stored in the memory 215. A control program for the inspection module 109 is saved in the memory 215.

[0039] The image reading units 331 and 332 read an image (sample) of the conveyed recording material according to an instruction from the CPU 214. The CPU 214 performs a process of storing the image read by the image reading units 331 and 332 in the HDD unit 216 as a reference image for abnormality inspection. The CPU 214 further performs an abnormality inspection process in which the inspection image read by the image reading units 331 and 332 is compared with the reference image for abnormality inspection stored in the HDD unit 216, and the image printed on the recording material is inspected based on the comparison result. Although an example in which the image read by the image reading units 331 and 332 is used as the reference image for abnormality inspection has been described, the present invention is not limited to this. It is also possible to store a bitmap image obtained by rasterizing PDL data in the HDD unit 216 as a reference image for abnormality inspection and use it in the abnormality inspection process.

[0040] The UI display unit 241 is used to display the anomaly inspection results, a setting screen, etc. The operation unit also serves as the UI display unit 241 and is operated by the user to accept various instructions from the user, such as changing the settings of the inspection module 109, an instruction to register a reference image for anomaly inspection, an instruction to perform image diagnosis, etc. The HDD unit 216 stores various setting information and image data required for anomaly inspection. The various setting information and image data stored in the HDD unit 216 can be reused.

[0041] The stacker 110 controls the printed recording material transported along the transport path so that it is discharged to a stack tray, discharged to an escape tray, or transported to a finisher 111 connected downstream in the transport direction of the printed recording material.

[0042] The finisher 111 controls the transport and discharge of printed recording materials, and performs finishing processes such as stapling, punching, or saddle stitching.

[0043] The external controller 102 includes a CPU 251, a memory 252, an HDD unit 253, a keyboard 256, a display unit 254, network I / Fs 255 and 257, and a video I / F 258. These devices are connected to each other via a system bus 259 so as to be able to transmit and receive data to each other. The CPU 251 executes a program stored in the HDD unit 253 to control the overall operation of the external controller 102, such as receiving print data from the client PC 103, RIP processing, and transmitting print data to the image forming apparatus 101. The memory 252 stores programs and data required for the CPU 251 to perform various processes. The memory 252 operates as a work area for the CPU 251.

[0044] The HDD unit 253 stores various programs and data. The keyboard 256 is used for inputting operation instructions for the external controller 102 from the user. The display unit 254 is, for example, a display, and is used for displaying information on applications being executed in the external controller 102 and an operation screen. The network I / F 255 is connected to the client PC 103 via the external LAN 104 and is used for communication of data such as print instructions. The network I / F 257 is connected to the image forming apparatus 101 via the internal LAN 105 and is used for communication of data such as print instructions. The external controller 102 is configured to be able to communicate with the print module 107, the inserter 108, the inspection module 109, the stacker 110, and the finisher 111 via the internal LAN 105 and the communication cable 260. The video I / F 258 is connected to the image forming apparatus 101 via the video cable 106 and is used for communication of data such as image data (print data).

[0045] The client PC 103 includes a CPU 261, a memory 262, an HDD unit 263, a display unit 264, a keyboard 265, and a network I / F 266. These devices are connected to each other via a system bus 269 so as to be able to transmit and receive data to and from each other. The CPU 261 controls the operation of each device via the system bus 269 by developing a program stored in the HDD unit 263 in the memory 262 and executing the program. This allows the client PC 103 to perform various processes. For example, the CPU 261 generates print data and issues a print instruction by executing a document processing program stored in the HDD unit 263. The memory 262 stores programs and data required for the CPU 261 to perform various processes. The memory 262 operates as a work area for the CPU 261.

[0046] The HDD unit 263 stores various applications such as a word processing program, programs such as a printer driver, and various data. The display unit 264 is, for example, a display, and is used to display information about applications running on the client PC 103 and an operation screen. The keyboard 265 is used to input operation instructions for the client PC 103 from a user. The network I / F 266 is communicably connected to the external controller 102 via the external LAN 104. The CPU 261 communicates with the external controller 102 via the network I / F 266.

[0047] Note that a configuration may be used in which the image forming apparatus 101 is connected to an external LAN 104, and print data is sent from the client PC 103 to the image forming apparatus 101 without going through the external controller 102. In this case, data analysis, interpretation, and rasterization of the print data are performed by the image forming apparatus 101.

[0048] Next, the image diagnosis processing according to the first embodiment will be described with reference to the drawings.

[0049] FIG. 4 is a flowchart illustrating the procedure of image diagnosis processing in the printing system according to the first embodiment.

[0050] First, in S401, the printing system 100 starts image diagnosis processing when it receives an image diagnosis instruction from a user or a serviceman via the UI display unit 241 of the inspection module 109, which also serves as an operation unit. In S402, the CPU 251 of the external controller 102 reads out a test chart stored in advance, rasterizes it into a bitmap, and creates the rasterized bitmap of the test chart as a reference image. This test chart is an image for fault diagnosis of the image forming apparatus (hereinafter, also referred to as a test image). Next, the process proceeds to S403, where the CPU 251 temporarily stores the reference image of the test chart created in S402 in the HDD unit 253 of the external controller 102. Thereafter, the reference image of the test chart stored in the HDD unit 253 is sent to the inspection module 109 and stored in the HDD unit 216 of the inspection module 109. The following description will be given assuming that the resolution of the reference image of the test chart is 600 dpi.

[0051] Next, the process proceeds to S404, and the CPU 251 transmits the bitmap data of the rasterized test chart from the video I / F 258 to the video I / F 205 of the print module 107 through the video cable 106. As a result, the CPU 206 of the print module 107 performs halftone processing on the bitmap data of the test chart received by the video I / F 205, and the print unit 203 prints the test chart based on the image data after the halftone processing.

[0052] Next, the process proceeds to S405, where the CPU 214 of the inspection module 109 executes processing to read the test chart printed by the print module 107 using the image reading units 331, 332. The process then proceeds to S406, where the CPU 214 stores the read image of the test chart obtained by reading in S405 as an inspection image in the HDD unit 216 of the inspection module 109. Here, the read image is stored as a color image having three RGB channels. In the following description of the first embodiment, the resolution when the test chart is read by the image reading units 331, 332 is 600 dpi.

[0053] Next, the process proceeds to S407, where the CPU 214 executes a filter process for suppressing the occurrence of moire on the read image of the printed matter (test chart) obtained by reading in S405. Next, the process proceeds to S408, where the CPU 214 executes a process for converting the resolution of the read image of the printed matter (test chart) after the filter process. As a result, the resolution of the read image of the printed matter (test chart) after the filter process is converted to 300 dpi. Then, the process proceeds to S409, where the CPU 214 executes a gamma correction process using a lookup table stored in the memory 215 of the inspection module 109 so as to match the gradation of the reference image created in S402 with the read image converted in S408.

[0054] Next, the process proceeds to S410, where the CPU 214 performs deformation correction on the reference image, and executes alignment between the read image and the reference image that has been deformation-corrected in S410. Next, the process proceeds to S411, where the CPU 214 executes a process of comparing the read image with a reference image of a test chart that has been adjusted for conditions such as resolution. This comparison process of S411 will be described later with reference to the flowchart of FIG. 7. When the comparison process between the read image and the reference image is thus completed, the process proceeds to S412, where the CPU 214 determines whether the printed image (test chart image) is normal or not based on the comparison result between the reference image and the read image by the comparison process. Here, if the CPU 214 obtains a determination result that the printed image is normal, the process proceeds to S413. In S413, the CPU 214 displays an image diagnosis result indicating that there is no problem on the UI display unit 241 of the inspection module 109. For example, the CPU 214 displays "No problem" and ends this process.

[0055] On the other hand, if the CPU 214 determines in S412 that the printed image is not normal (the image has an abnormality), the process proceeds to S414. In S414, the CPU 214 acquires the image abnormality data obtained by performing a comparison process between the reference image and the read image in S411. Next, the process proceeds to S415, where the CPU 214 extracts the characteristics of the image abnormality from the image abnormality data. Examples of the characteristic information of the image abnormality obtained by this extraction process include color information such as whether the image is a single color such as yellow, magenta, cyan, or black, or a multi-color occurring in multiple colors, contrast information indicating the density of the abnormality, and shape information such as size and vertical length. In addition, examples of the information include coordinate information indicating the position in a direction perpendicular to the transport direction of the test chart in the printing module 107, and periodic information indicating that an abnormality of similar characteristics occurs periodically in the transport direction of the test chart in the printing module 107.

[0056] Next, the process proceeds to S416, and the CPU 214 identifies the parts that cause the image abnormality in the printing module 107 and the inspection module 109 based on the characteristic information of the image abnormality obtained in S415. Then, the process proceeds to S417, and the CPU 214 determines how to deal with the image abnormality based on the parts that cause the image abnormality identified in S416. These measures are divided into measures that can be automatically restored and measures that cannot be automatically restored. The measures that can be automatically restored include measures that can be automatically restored by the printing module 107, such as cleaning the wires and grids of the corona chargers that are charging means for the photosensitive drums provided in the image forming stations 304 to 307 of the printing module 107. The measures that cannot be automatically restored include measures that require user work, such as cleaning dirt from the reading surfaces of the image reading units 331 and 332 of the inspection module 109 and adjusting the recording material to be used, and measures that require service personnel work, such as replacing parts. The measures that cannot be automatically restored include measures, such as dealing with fibers or foreign matter that are in the recording material before image formation.

[0057] The process then proceeds to S418, where the CPU 214 determines whether the action determined in S417 is an automatically reversible action. If it is determined in S418 that the action is automatically reversible, the process proceeds to S419. In S419, the CPU 214 executes automatic return control to address the cause of the image abnormality. On the other hand, if the CPU 214 determines in S418 that the action is not automatically reversible, the process proceeds to S420. In S420, the CPU 214 displays the image diagnosis results and the method of response on the UI display unit 241 of the inspection module 109. When any of the above-mentioned processes of S413, S419, or S420 is completed, the flow (image diagnosis processing) shown in FIG. 4 ends.

[0058] When an abnormality is detected in a scanned image by the above-described process, its feature amounts, such as color information indicating whether it is a single color or a multi-color occurring in multiple colors, contrast information indicating the density of the abnormality, shape information such as size and vertical length, and periodic information are obtained. Then, based on these feature amounts, the part that is the cause of the image abnormality can be identified in the printing module or the inspection module.

[0059] FIG. 5 is a diagram showing an example of a test chart used in the image diagnosis processing according to the first embodiment.

[0060] 5(a) is a diagram showing an example of a color image chart. The color image chart 500 has a halftone image portion 501. The halftone image portion 501 indicates an area in which an image expressed in halftones with the same signal value over the entire surface is formed, and is composed of, for example, 50% halftone.

[0061] 5B shows a test chart 502 which is an example of a blank chart. The blank chart 502 has a non-image portion 503. The non-image portion 503 indicates an area where no image is formed.

[0062] In the halftone image area 501 of the color image chart 500, image abnormalities such as low halftone density or whiteout, and image abnormalities such as high halftone density are inspected. In the non-image area 503 of the blank chart 502, image abnormalities such as a formed image being dark rather than white are inspected.

[0063] FIG. 6 is a diagram showing an example of a test chart set used in image diagnosis according to the first embodiment.

[0064] 6(a) shows a set of multiple monochrome charts, each of which has halftone image portions of the process colors yellow (Y), magenta (M), cyan (C), and black (K).

[0065] FIG. 6(b) shows a mixed color chart (mixed color image). The mixed color chart has a halftone image portion formed of intermediate multi-tone colors of secondary or higher colors. For example, this mixed color chart is composed of a halftone image of 50% cyan and 50% magenta. Here, the halftone image portion constituting the mixed color chart of FIG. 6(b) is composed of two colors, cyan and magenta, but is not limited to this. FIG. 6(c) shows a blank chart. The blank chart has a non-image portion.

[0066] Fig. 7 is a flowchart for explaining the procedure of the comparison process between the reference image and the read image performed in S411 of Fig. 4 according to the first embodiment. Note that the process shown in this flowchart is achieved by the CPU 214 of the inspection module 109 executing a program loaded in the memory 215.

[0067] In S701, the CPU 214 performs a difference calculation between the reference image and the read image. Here, the difference calculation calculates the difference for each RGB channel of each image. Next, the process proceeds to S702, where the CPU 214 converts the difference image obtained in S701 into a luminance image. This conversion to a luminance image is obtained, for example, by converting the signal value of each pixel in the RGB channel of the difference image using the following formula.

[0068] Luminance = 0.299 x R + 0.587 x G + 0.114 x B Next, the process proceeds to S703, where the CPU 214 determines a threshold value for binarizing the luminance image. Here, the threshold value may be a previously stored threshold value that is read out, or may be dynamically calculated from the luminance value of the luminance image. The process proceeds to S704, where the CPU 214 binarizes the luminance image based on the threshold value determined in S703.

[0069] Through this binarization process, for example, "1" parts are determined to be abnormal parts of the image, and "0" parts are determined to be normal parts.

[0070] 8 is a flowchart for explaining the procedure of a process for determining whether or not the cause of an image abnormality is before transfer in the feature extraction process of S415 in FIG. 4 according to the first embodiment. Here, before transfer refers to a process up to when a toner image is superimposed and transferred onto the intermediate transfer belt 308. Specifically, it refers to a process of forming a toner image on the photosensitive drum of the image forming stations 304 to 307 and a process up to when a toner image of any one of the process colors constituting the color mixture chart is transferred onto the intermediate transfer belt 308.

[0071] In S801, the CPU 214 acquires coordinate information where the image abnormality occurs from the image abnormality data acquired in S414. Next, the process proceeds to S802, where the CPU 214 acquires information indicating the shape of the image abnormality from the image abnormality data. Then, the process proceeds to S803, where the CPU 214 determines whether the test chart being used is a mixed-color chart. If a mixed-color chart is being used, the process proceeds to S804, where characteristic information is set to indicate that the image abnormality is an abnormality that occurred after transfer, and the process proceeds to S809. In this case, in S809, the CPU 214 stores the characteristic information (post-transfer abnormality information) set in S804 in the HDD unit 216, and ends this process. This characteristic information makes it possible to narrow down and identify the defective part of the device that caused the image abnormality.

[0072] Here, in the case of a mixed color chart, as shown in the flowchart of Fig. 7, the difference image is converted into a luminance image (S702), and the luminance value of the luminance image is binarized (S704) to detect image abnormalities. Therefore, an abnormality in which only one of the cyan and magenta mixed color charts is missing is binarized as black and is not detected as an image abnormality. Therefore, an image abnormality detected in the mixed color chart is detected in both colors of the cyan and magenta mixed color charts.

[0073] FIG. 9 is a schematic diagram showing an example of an image abnormality occurring in a color mixing chart and the image after binarization.

[0074] 9A is a diagram showing an example of an image abnormality occurring in a read image of a mixed color chart. A halftone image area 900 is formed by a first process color and a second process color. Here, the first process color is cyan and the second process color is magenta.

[0075] Image anomaly 901 represents an image anomaly after transfer due to the absence of both cyan and magenta toner. Thus, image anomaly 901 after transfer appears as a white image anomaly due to the absence of both cyan and magenta toner. Image anomalies 902 and 903 represent image anomalies that occurred in cyan and magenta before transfer, respectively. Image anomaly 902 before cyan transfer appears as a magenta image anomaly due to the absence of cyan toner. Similarly, image anomaly 903 before magenta transfer appears as a cyan image anomaly due to the absence of magenta toner.

[0076] Fig. 9(b) shows the binarized image of Fig. 9(a). Through the binarization process, the pre-transfer image anomalies 902 and 903 are both converted to black pixels, and only the post-transfer image anomaly 901 is converted to white pixels. In this way, only the post-transfer image anomaly 901 can be detected.

[0077] Returning to the description of FIG. 8 again, when the CPU 214 determines in S803 that the color mixing chart is not used, the process proceeds to S805, where the CPU 214 reads out characteristic information of the image abnormality after transfer from the HDD unit 216. Next, the process proceeds to S806, where the CPU 214 performs a similar image anomaly search to determine whether there is a similar image anomaly in the characteristic information of the image anomaly read out in S805. This similar image anomaly search is performed, for example, based on the similarity of the image anomaly shape. A known template matching method can be used to determine the similarity of the shape. Then, the process proceeds to S807, where the CPU 214 determines whether there is a similar image anomaly, and if it determines that there is a similar image anomaly, the process proceeds to S804, and if it determines that there is no similar image anomaly, the process proceeds to S808. In S808, the CPU 214 sets characteristic information that the image anomaly is an image anomaly that occurred before transfer, and proceeds to S809. In this case, in S809, the CPU 214 stores the characteristic information (pre-transfer image abnormality information) set in S808 in the HDD unit 216, and ends this process.

[0078] By the above-described process, it is possible to identify the part causing the image abnormality based on the feature information of the detected image abnormality information and determine how to deal with the image abnormality. In this way, by determining with high accuracy whether the part causing the image abnormality is before or after transfer, it is possible to take appropriate measures to deal with the image abnormality.

[0079] [Modification of the first embodiment] In the above-mentioned embodiment 1, the presence or absence of an image abnormality is judged from a difference image obtained by comparing a reference image with a read image, but the presence or absence of an image abnormality may be judged using only the read image. For example, a reference signal value may be determined from the read image, and the presence or absence of an image abnormality may be judged based on a difference image obtained by calculating the difference between the reference signal value and the read image. Here, the reference signal value may be determined by using, for example, the median value of the signal value of the entire surface of the read image.

[0080] As described above, according to the first embodiment, by performing image diagnosis using a test chart including a color mixing chart, it becomes possible to distinguish whether the cause of an image abnormality occurs before or after transfer. In this way, it becomes possible to accurately identify the part that causes the image abnormality.

[0081] [Embodiment 2] In the above-described first embodiment, a method for determining whether or not the cause of an image abnormality occurs before transfer using a single color chart and a mixed color chart of each process color has been described.

[0082] In contrast, in the second embodiment, a mode is described in which a multiple color mixing chart is used without using a single color chart to determine whether the cause of an image abnormality is before transfer. By performing image diagnosis using only the multiple color mixing chart, it is expected that the number of test charts output in image diagnosis can be reduced. Note that the configuration of the printing system according to the second embodiment is the same as that of the first embodiment described above, and therefore a description thereof will be omitted.

[0083] FIG. 10 is a flowchart illustrating the procedure of the comparison process performed by the inspection module 109 according to the second embodiment of the present invention in S411 of FIG.

[0084] In S1001, the CPU 214 of the inspection module 109 performs a difference calculation between the reference image and the read image. Here, the difference calculation calculates the difference for each RGB channel of each image. Next, the process proceeds to S1002, where the CPU 214 determines a color of interest. This color of interest is selected from the process colors that make up the multi-order colors included in the test chart printed in S404 of FIG. 4. Then, the process proceeds to S1003, where the CPU 214 acquires a complementary color channel image of the color of interest determined in S1002 from the difference image. Next, the process proceeds to S1004, where the CPU 214 determines a threshold value for binarizing the complementary color channel image acquired in S1003. Here, the threshold value may be a threshold value that has been previously stored and may be dynamically obtained from the luminance value of the luminance image.

[0085] FIG. 11 is a schematic diagram illustrating separation of process colors using complementary color channels.

[0086] 11(a) is a diagram showing the reflectance of the toners of the process colors cyan, magenta, and yellow. For convenience, the low wavelength portion of visible light will be referred to as the B (blue) wavelength portion, the medium wavelength portion as the G (green) wavelength portion, and the high wavelength portion as the R (red) wavelength portion.

[0087] Cyan toner reflects light in the B and G wavelength regions and absorbs light in the R wavelength region. Magenta toner reflects light in the B and R wavelength regions and absorbs light in the G wavelength region. Yellow toner reflects light in the G and B wavelength regions and absorbs light in the B wavelength region.

[0088] 11B is a diagram showing the transmittance of the RGB filters used in the image reading units 331 and 332. In the image reading units 331 and 332, light transmitted through each of the R, G, and B filters is read as pixel values ​​of each of the R, G, and B channels.

[0089] When a cyan toner image is formed on a recording material by the image forming apparatus 101, the cyan toner absorbs light in the R wavelength range, so the amount of light that passes through the R filter is reduced. Similarly, when magenta toner is formed, the amount of light that passes through the G filter is reduced, and when yellow toner is formed, the amount of light that passes through the B filter is reduced. This is the relationship between cyan, magenta, and yellow toner and the complementary colors of the RGB channels.

[0090] Therefore, by focusing on the pixel values ​​of the RGB channels that are the complementary colors of cyan, magenta, and yellow, it is possible to separate the process colors.

[0091] Next, the process proceeds to S1005, where the CPU 214 binarizes the complementary color channel image of the color of interest based on the threshold determined in S1004. This binarization process determines, for example, that a "1" portion is an abnormal portion of the image and a "0" portion is a normal portion. The process then proceeds to S1006, where the CPU 214 determines whether the above process has been performed for all process colors that make up the multi-order colors used in the test chart. If there are any unprocessed colors, the process proceeds to S1002, where the CPU 214 determines the color of interest from among the unprocessed colors. If all process colors have been processed in this manner in S1006, the comparison process flow ends.

[0092] This process makes it possible to detect abnormalities in images generated in each color of interest.

[0093] FIG. 12 is a flowchart for explaining the process in which the inspection module 109 according to the second embodiment determines whether or not the cause of the image abnormality is before transfer in the image abnormality feature extraction process in S415 of FIG.

[0094] In S1201, the CPU 214 acquires coordinate information of the image abnormality from the image abnormality data. Next, the process proceeds to S1202, where the CPU 214 acquires shape information of the image abnormality from the image abnormality data. Next, the process proceeds to S1203, where the CPU 214 determines whether or not there is an image abnormality at the same location in multiple channels of the image abnormality data. If it is determined that there is an image abnormality at the same location in multiple channels, the process proceeds to S1204, where the CPU 214 determines whether the image abnormalities at the same location have the same shape. If it is determined that they have the same shape, the process proceeds to S1205, where the CPU 214 sets characteristic information that the image abnormality is an image abnormality that occurred after transfer, and then the process proceeds to S1206.

[0095] On the other hand, when the CPU 214 determines in S1203 that there is no image abnormality in the same location in a plurality of channels, or when the CPU 214 determines in S1204 that the image abnormalities in the same location do not have the same shape, the process proceeds to S1207. In S1207, the CPU 214 sets characteristic information that the image abnormality is an image abnormality that occurred before transfer, and proceeds to S1206. When the process of either S1205 or S1207 described above is thus completed, the process proceeds to S1206, where the CPU 214 stores the characteristic information of the image abnormality in the HDD 216, and ends this process.

[0096] By using the above-described process, it is possible to identify the part causing the image abnormality based on the feature information of the detected image abnormality information by using a plurality of color mixing charts, and to determine how to deal with the image abnormality. In this way, by determining with high accuracy whether the part causing the image abnormality is before or after transfer, it is possible to take appropriate measures to deal with the image abnormality.

[0097] FIG. 13 is a schematic diagram showing a process for determining whether or not an image abnormality factor occurs before transfer from a color mixing chart according to the second embodiment.

[0098] 13(a) shows an example of test chart data. An image portion 1300 is composed of intermediate tones of secondary or higher colors. In the following description, the image portion is assumed to be composed of secondary colors of cyan (C) and yellow (Y).

[0099] Fig. 13(b) shows the read image data obtained by printing and reading the test chart of Fig. 13(a). Image anomalies 1301 to 1303 occur in the read image data of Fig. 13(b). Here, image anomaly 1301 indicates a post-transfer image anomaly. Image anomalies 1302 and 1303 indicate pre-transfer image anomalies occurring in cyan and yellow, respectively.

[0100] Figure 13(c) shows the red channel image of the scanned image data of Figure 13(b). Red is the complementary color of cyan, and image anomalies occurring in cyan can be detected by using the red channel image. Figure 13(d) shows image anomaly data obtained by comparing the red channel image of Figure 13(c) with the red channel of a reference image.

[0101] Figure 13(e) shows the blue channel image of the scanned image data of Figure 13(b). Blue is the complementary color of yellow, and image anomalies occurring in yellow can be detected by using the blue channel image. Figure 13(f) shows image anomaly data obtained by comparing the blue channel image of Figure 13(e) with the blue channel of a reference image.

[0102] By analyzing the image anomaly data in Figures 13(d) and 13(f), it can be determined that the image anomaly 1301 occurring at the same position in both is a pre-transfer image anomaly, the image anomaly 1302 occurring only in Figure 13(d) is a pre-transfer image anomaly occurring in cyan, and the image anomaly 1303 occurring only in Figure 13(f) is a pre-transfer image anomaly occurring in yellow.

[0103] [Modification of the second embodiment] In the above-mentioned second embodiment, the comparison process and the image anomaly factor determination process are performed using RGB channels (complementary color channels) which are complementary colors, but the process may be performed by converting to a color space such as L*C*h* or XYZ. The following describes the process using the L*C*h* color space. Here, L is the lightness index, C is the saturation, and H is the hue angle.

[0104] FIG. 14 is a schematic diagram illustrating a process for determining an image anomaly cause using the L*C*h* channel according to a modification of the second embodiment.

[0105] FIG. 14(a) shows a read image obtained by reading a mixed-color test chart. An image portion 1400 is an image portion in which a mixed-color halftone image is formed. In the following description, the image portion 1400 is assumed to be formed of secondary colors of cyan (C) and yellow (Y). An image anomaly 1401 shows a post-transfer image anomaly. Image anomalies 1402 and 1403 show pre-transfer image anomalies occurring in cyan and yellow, respectively. FIG. 14(b) shows an image obtained by converting the read image 14(a) into the L*C*h* channel. For simplicity, only the area within the image formation portion of the read image is shown here.

[0106] Fig. 14(c) is a schematic diagram of the pixel value distribution of pixels included in the image anomaly plotted on the L*C* (lightness component and saturation component) plane. Fig. 14(d) is a schematic diagram of the pixel value distribution plotted on the C*h* plane. Reference numbers 1404 to 1407 indicate pixel value groups that group together similar colors among the plotted pixel values.

[0107] Reference number 1404 indicates a pixel value group of a color mixing portion (non-image abnormality portion) of the image portion 1400, reference number 1405 indicates a pixel value group of a post-transfer image abnormality 1401, reference number 1406 indicates a pixel value group of a pre-transfer image abnormality 1402, and reference number 1407 indicates a pixel value group of a pre-transfer image abnormality 1403. If no image abnormality exists in the read image, the pixel value group will be only the pixel value group 1404 of the color mixing portion, and therefore the presence or absence of an image abnormality can be determined from the number of pixel value groups.

[0108] The post-transfer image abnormality 1401 appears as a paper-white portion due to the lack of both cyan and yellow toner. In other words, the pixel value group 1406 has low saturation and high brightness, so by focusing on the L*C* channel, the post-transfer image abnormality can be determined.

[0109] Pre-transfer image abnormalities 1402 and 1403 appear as the other toner image due to the absence of cyan and yellow toner, respectively. Therefore, the pixel values ​​of the image abnormality portion change hue with respect to the mixed color portion. Since the cyan pre-transfer image abnormality 1402 appears as a yellow image abnormality, the pixel value group 1406 has a smaller h* than the pixel value group 1404 of the mixed color portion. Similarly, the yellow pre-transfer image abnormality 1403 appears as a cyan image abnormality, so the pixel value group 1407 has a larger h* than the pixel value group 1404 of the mixed color portion. As described above, by focusing on the C*h* channel, it is possible to determine a pre-transfer image abnormality.

[0110] As described above, according to the second embodiment, by performing image comparison processing using signal values ​​of multiple channels and feature extraction processing of image abnormality, it becomes possible to simply distinguish whether the cause of the image abnormality is before transfer or not using only the color mixing chart. This makes it possible to accurately identify the part that causes the image abnormality. In addition, by performing image diagnosis using only multiple color mixing charts, it is expected that the number of output test charts in image diagnosis can be reduced.

[0111] (Other embodiments) The present invention can also be realized by a process in which a program for implementing one or more of the functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a 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 implements one or more of the functions.

[0112] This specification and drawings disclose the following image processing device, its control method, and program.

[0113] [Item 1] a reading means for reading an image of a chart on which a test image is printed and acquiring a read image; a detection means for detecting an image abnormality contained in the read image; an acquisition means for acquiring a feature amount of the image anomaly detected by the detection means; and an identification means for identifying a cause of the image abnormality based on the feature amount, The image processing device according to claim 1, wherein the test image includes at least one mixed color image of a multi-color having a secondary or higher order color.

[0114] [Item 2] The image processing device according to item 1, characterized in that the detection means obtains a difference image between the read image and a reference image, converts the difference image into a luminance image, and detects the image abnormality based on the luminance value of the luminance image and a threshold value.

[0115] [Item 3] 3. The image processing device according to item 1 or 2, characterized in that, when the detection means detects an image abnormality in the mixed color image included in the read image, the acquisition means acquires a feature amount indicating that the image abnormality occurred after transfer in a printing device that prints the test image.

[0116] [Item 4] The image processing device according to any one of items 1 to 3, characterized in that, when the detection means detects an image abnormality in a monochrome image included in the read image, the acquisition means determines whether there is a post-transfer image abnormality similar to the image abnormality, and when there is a post-transfer image abnormality similar to the image abnormality, acquires a feature indicating that the image abnormality occurred after transfer in a printing device that prints the test image.

[0117] [Item 5] 5. The image processing device according to item 4, characterized in that, when there is no post-transfer image abnormality similar to the image abnormality, the acquisition means acquires a feature amount indicating that the abnormality occurred before transfer in a printing device that prints the test image.

[0118] [Item 6] The image processing device according to any one of items 1 to 5, characterized in that the detection means obtains a reference signal value, which is a median of the signal values ​​of the entire surface of the read image, from the read image, and determines the presence or absence of an image abnormality based on a difference image between the reference signal value and the read image.

[0119] [Item 7] A reading means for reading an image of a chart on which a test image of a multi-color having a secondary or higher order color is printed, and acquiring a read image; a detection unit for detecting an image abnormality corresponding to each color included in the read image; an acquisition means for acquiring a feature amount of the image anomaly detected by the detection means; An identification means for identifying a cause of the image abnormality based on the feature amount; 13. An image processing device comprising:

[0120] [Item 8] 8. The image processing device according to item 7, wherein the detection means detects the image abnormality by comparing an image of a complementary color channel of a process color included in the multi-order colors of the test image with a reference image.

[0121] [Item 9] The image processing device described in item 7 or 8, characterized in that when the same image abnormality occurs in multiple process colors included in the multi-color of the test image, the acquisition means acquires a feature indicating that the image abnormality occurred after transfer in a printing device that prints the test image.

[0122] [Item 10] 10. The image processing device according to item 9, wherein the identical image abnormality is an image abnormality at the same position and with the same shape.

[0123] [Item 11] The image processing device described in any one of items 7 to 10, characterized in that when an image abnormality occurs in one of the process colors included in the multi-color of the test image, the acquisition means acquires a feature indicating that the image abnormality occurred before transfer in a printing device that prints the test image.

[0124] [Item 12] 12. The image processing device according to any one of items 7 to 11, wherein the acquisition means acquires the feature amount based on a lightness component and a saturation component of a pixel value group that groups together similar colors of pixel values ​​included in the image anomaly.

[0125] [Item 13] Item 13. The image processing device according to item 12, wherein the acquisition means acquires a feature value indicating that an image abnormality involving a group of pixel values ​​with a high brightness component occurred after transfer in a printing device that prints the test image, and acquires a feature value indicating that an image abnormality involving a group of pixel values ​​with a high saturation component occurred before transfer in a printing device that prints the test image.

[0126] [Item 14] 14. The image processing device according to any one of items 7 to 13, wherein the acquisition means extracts the feature amount using RGB channels that are complementary to process colors included in the multi-order colors of the test image.

[0127] [Item 15] 15. The image processing device according to any one of items 1 to 14, wherein the test image is constituted by a halftone image.

[0128] [Item 16] 16. The image processing device according to any one of items 1 to 15, wherein the occurrence cause includes whether the occurrence occurs before the test image is transferred to the chart or whether the occurrence occurs after the test image is transferred to the chart.

[0129] [Item 17] 17. The image processing device according to any one of items 1 to 16, wherein the image abnormality is a dot-like or streak-like image abnormality.

[0130] [Item 18] A control method for controlling an image processing device, comprising: a reading step of reading an image of a chart on which a test image is printed to obtain a read image; a detection step of detecting an image abnormality contained in the read image; an acquisition step of acquiring a feature amount of the image anomaly detected by the detection step; and identifying a cause of the image abnormality based on the feature amount, A control method, characterized in that the test image includes at least one mixed color image of secondary or higher multi-colors.

[0131] [Item 19] A control method for controlling an image processing device, comprising: a reading step of reading an image of a chart on which a test image of a multi-color having secondary or higher colors is printed to obtain a read image; a detection step of detecting an image abnormality corresponding to each color included in the read image; an acquisition step of acquiring a feature amount of the image anomaly detected by the detection step; a step of identifying a cause of the image abnormality based on the feature amount; A control method comprising the steps of:

[0132] [Item 20] 20. A program for causing a computer to execute each step of the control method according to item 18 or 19.

[0133] The present invention is not limited to the above-described embodiments, and various modifications and variations can be made without departing from the spirit and scope of the present invention. Therefore, the following claims are appended to apprise the public of the scope of the present invention. [Explanation of symbols]

[0134] 101: image processing device, 102: external controller, 107: print module, 108: inserter, 109: inspection module, 110: stacker, 111: finisher, 331, 332: image reading unit

Claims

1. A reading means for reading an image of a chart on which a test image of a multi-color (secondary or higher) color is printed and acquiring a read image; a detection means for detecting an image abnormality contained in the read image; an acquisition means for acquiring a feature amount of the image abnormality detected by the detection means; an identification means for identifying a cause of the image abnormality by determining whether or not the same image abnormality exists among image abnormalities corresponding to all colors included in the multi-color of the test image based on the feature amount; and 1. An image processing device comprising:

2. The image processing device described in Claim 1, characterized in that the identification means detects the image abnormality by comparing images corresponding to each color contained in the multi-color of the test image.

3. 2. The image processing device according to claim 1, wherein, when the same image abnormality exists in the image abnormalities corresponding to all colors included in the multi-color of the test image, the acquisition means acquires a feature value indicating that the image abnormality occurred after transfer in a printing device that prints the test image.

4. An image processing device as described in Claim 1, characterized in that the same image abnormality is an image abnormality at the same position and with the same shape.

5. An image processing device as described in Claim 1, characterized in that when the image abnormality detected by the detection means does not include the same image abnormality among the image abnormalities corresponding to all colors contained in the multi-color of the test image, the acquisition means acquires a feature indicating that the image abnormality occurred before transfer in a printing device that prints the test image.

6. 2. The image processing apparatus according to claim 1, wherein the acquiring means acquires the feature amount based on a lightness component and a saturation component of a pixel value group that groups together pixel values ​​of similar colors included in the image abnormality.

7. 2. The image processing apparatus according to claim 1, wherein the acquiring means extracts the feature amount using RGB channels that are complementary colors of process colors included in the multi-order colors of the test image.

8. 2. The image processing apparatus according to claim 1, wherein the test image is a halftone image.

9. 2. The image processing apparatus according to claim 1, wherein the occurrence factor includes whether the error occurred before the test image was transferred onto the chart or whether the error occurred after the test image was transferred onto the chart.

10. 2. The image processing apparatus according to claim 1, wherein the image abnormality is a spot-like or streak-like image defect.

11. The image processing device described in Claim 1, characterized in that the detection means obtains a difference image between the read image and a reference image, converts the difference image into a luminance image, and detects the image abnormality based on the luminance value of the luminance image and a threshold value.

12. The image processing device described in Claim 1, characterized in that the detection means obtains a reference signal value, which is the median of the signal values ​​of the entire read image, from the read image, and determines whether or not there is an image abnormality based on a differential image between the reference signal value and the read image.

13. An image processing device as described in Claim 1, further comprising a display means for displaying the cause of the image abnormality.

14. A control method for controlling an image processing device, comprising: a reading step of reading an image of a chart on which a test image of a multi-color (secondary or higher) color is printed to obtain a read image; a detection step of detecting an image abnormality contained in the read image; an acquisition step of acquiring a feature amount of the image abnormality detected by the detection step; an identifying step of identifying a cause of the image anomaly by determining whether or not the same image anomaly exists among image anomalies corresponding to all colors included in the multi-color of the test image based on the feature amount; A control method comprising:

15. A program that causes a computer to execute each step of the control method according to claim 14.

Images