Printing system, image processing apparatus and method for controlling the same, and program

Abstract

To solve the problem in which when printing operations are interrupted and image diagnosis processing is executed using a test chart in the case where the image diagnosis processing is executed during creation of a printed matter, designation of a paper feeding unit and a change of a printing condition are required, and downtime until resumption of the creation of the printed matter becomes longer, which decreases productivity.SOLUTION: A printing system has an image forming apparatus and a diagnosis device. The image forming apparatus creates a printed matter corresponding to an input image. The diagnosis device detects an image defect included in the printed matter on the basis of a read image obtained by reading the printed matter, acquires a feature quantity of the detected image defect, and specifies a cause of the image defect on the basis of the acquired feature quantity. When detection means detects the image defect and a predetermined condition is satisfied in the acquisition of the feature quantity, the diagnosis device causes the image forming apparatus to stop creation of a printed matter, and acquires the feature quantity of the image defect on the basis of the read image.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present disclosure relates to a printing system, an image processing apparatus, a control method thereof, and a program.

Background Art

[0002] Due to a defect in an image forming apparatus including a printing unit, printing defects such as stains where coloring materials such as ink or toner adhere to unintended locations on the printed matter output from the printing unit, or color bleeding where the color becomes lighter than normal, may occur.

[0003] To repair such defects in the image forming apparatus, there is an image diagnosis technique for identifying the cause of the defect and taking countermeasures according to the identified cause. This image diagnosis technique identifies the cause of the defect from features such as the color and size of the printing defect, and performs corresponding measures according to the cause that are prepared in advance.

[0004] Patent Document 1 describes an image diagnosis technique for printing a pre-prepared diagnostic test chart by a printing unit and determining the cause of a defect from the image data obtained by reading the test chart.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, in the technology of Patent Document 1, it is necessary to print a diagnostic test chart in order to identify the causes of malfunctions in the image forming apparatus and the image reading apparatus. Therefore, adjustment operations such as specifying the paper feed unit where the paper for printing the test chart is stored and adjusting the fixing temperature of the printing unit according to the specified paper are required. Therefore, when it becomes necessary to perform image diagnosis processing during the creation of a printed matter, the printing operation is interrupted and image diagnosis processing using the test chart is performed, and it becomes necessary to specify the paper feed unit and change the printing conditions for that purpose. As a result, the downtime until printing resumes becomes long and productivity decreases.

[0007] An object of the present invention is to solve at least one of the problems of the above prior art.

[0008] An object of the present invention is to shorten the downtime by utilizing the read image data of a printed matter in which an image defect is detected.

Means for Solving the Problems

[0009] In order to achieve the above object, a printing system according to an aspect of the present invention has the following configuration. That is, A printing system having an image forming apparatus and a diagnostic apparatus, The image forming apparatus generates a printed matter corresponding to the input image, The diagnostic apparatus, Detection means for detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter, Acquisition means for acquiring a feature amount of the image defect detected by the detection means, Specification means for specifying the cause of the image defect based on the feature amount acquired by the acquisition means, and The acquisition means is characterized in that when the detection means detects the image defect and satisfies a predetermined condition, the generation of the printed matter by the image forming apparatus is stopped, and the feature amount of the image defect is acquired based on the read image.

Effects of the Invention

[0010] According to the present invention, it is possible to shorten the downtime by utilizing the read image data of the printed matter in which an image defect is detected.

[0011] Other features and advantages of the present invention will become apparent from the following description with reference to the accompanying drawings. In the accompanying drawings, the same or similar components are denoted by the same reference numerals.

Brief Description of the Drawings

[0012] The accompanying drawings are included in the specification, form a part thereof, show embodiments of the present invention, and are used to explain the principle of the present invention together with the description.

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[0013] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the invention according to the claims. Although a plurality of features are described in the embodiments, not all of these plurality of features are essential to the invention, and the plurality of features may be arbitrarily combined. Further, in the accompanying drawings, the same or similar configurations are denoted by the same reference numerals, and redundant descriptions are omitted.

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

[0015] As shown in FIG. 1, the 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 communicably connected via an internal LAN 105 and a video cable 106. The external controller 102 is communicably connected to a client PC 103 via an external LAN 104.

[0016] The client PC 103 can issue a printing 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) processable by the external controller 102 is installed in the client PC 103. A user who wants to perform printing can issue a printing instruction from various applications installed in the client PC 103 via the printer driver by operating the client PC 103. The printer driver creates PDL data, which is printing data, based on the printing instruction from the user and transmits it to the external controller 102. When the external controller 102 receives the PDL data from the client PC 103, it analyzes and interprets the received PDL data. The external controller 102 performs rasterization processing on the PDL data based on the result of the interpretation and generates a bitmap image (printing image data) with a resolution adapted to the image forming apparatus 101. Then, a printing instruction is issued by inputting a print job including the bitmap image to the image forming apparatus 101.

[0017] Next, the image forming apparatus 101 will be described. In the image forming apparatus 101, devices having a plurality of different functions are connected and configured to enable complex processes such as bookbinding. The image forming apparatus 101 includes a printing unit 107 (image forming unit), a diagnosis unit (diagnosis device) 108, a stacker 109, and a finisher 110. Each of these units will be described below.

[0018] The printing unit 107 prints an image on a recording material (such as paper or sheet) according to a print job and discharges the printed recording material. The printed recording material discharged from the printing unit 107 is conveyed through the interiors of the diagnosis unit 108, the stacker 109, and the finisher 110 in this order. In Embodiment 1, the image forming apparatus 101 of the printing system 100 is an example of an image forming apparatus, but in some cases, the printing unit 107 included in the image forming apparatus 101 may be referred to as an image forming apparatus. The printing unit 107 forms (prints) an image on a recording material fed and conveyed from a paper feeding unit disposed below the printing unit 107 using toner (color material).

[0019] The diagnosis unit 108 determines the presence or absence of defects (image defects) in the printed image, and identifies defective parts of the image forming apparatus 101 and the image reading units 331 and 332 (FIG. 3) based on the image of the printed recording material that has been printed by the printing unit 107 and conveyed through the conveyance path. Specifically, the diagnosis unit 108 reads the image printed on the conveyed printed recording material, and performs an inspection process for determining the presence or absence of image defects in the read image data obtained by the reading, and an image diagnosis process for identifying defective parts.

[0020] The inspection process for determining the presence or absence of image defects is performed by comparing result data (reference image) based on input image data from the user with read image data of a recording material (hereinafter referred to as a printed matter) on which an image corresponding to the input image data is printed. The image diagnosis process for identifying defective parts extracts a diagnosis area from the input image data. Further, a feature amount is calculated from an area of an image defect having a large signal value difference from the reference image within the extracted diagnosis area, and a defective factor is identified based on the feature amount. Details of the inspection process and the image diagnosis process will be described later.

[0021] The stacker 109 is an apparatus capable of stacking a large number of printed recording materials.

[0022] The finisher 110 is a device capable of performing finishing processes such as stapling, punching, and saddle-stitching on the conveyed printed recording material. The recording material after being processed by the finisher 110 is discharged onto a predetermined paper discharge tray.

[0023] In the example of the system in FIG. 1, an external controller 102 is connected to the image forming apparatus 101. However, the present embodiment can also be applied to configurations different from this. For example, a configuration may be used in which the image forming apparatus 101 is directly connected to the external LAN 104, and print data is transmitted from the client PC 103 to the image forming apparatus 101 without passing through the external controller 102. In this case, data analysis and rasterization for the print data are executed by the image forming apparatus 101.

[0024] FIG. 2 is a schematic cross-sectional view for explaining an example of the hardware configuration of the image forming apparatus 101 according to Embodiment 1. Hereinafter, a specific operation example of the image forming apparatus 101 will be described with reference to FIG. 2.

[0025] The printing unit 107 includes a plurality of paper feed decks. In Embodiment 1, two types of decks, the paper feed decks 301 and 302, are provided. Various recording materials (papers) are stored in each paper feed deck. Among the recording materials stored in each paper feed deck, the uppermost recording material is separated one by one and fed to the conveyance path 303. The printing unit 107 acquires paper information of the papers stored in each paper feed deck based on sensors of the paper feed decks and instructions from the user. The paper information in Embodiment 1 is the size, basis weight, and surface property of the paper. These pieces of paper information are acquired and held in an HDD described later. The paper size is automatically acquired by reading the position of a guide (not shown) in the paper feed deck with a sensor. The other information is acquired by the user selecting and inputting from a paper information change screen described later.

[0026] The image forming stations 304 to 307 each include a photosensitive drum (photoconductor), and form toner images on the photosensitive drums using toners of different colors. Specifically, the image forming stations 304 to 307 form toner images using yellow (Y), magenta (M), cyan (C), and black (K) toners, respectively. The toner images of each color formed at the image forming stations 304 to 307 are sequentially superimposed and transferred onto the intermediate transfer belt 308 (primary transfer). The toner images thus transferred onto the intermediate transfer belt 308 are conveyed to the secondary transfer position 309 as the intermediate transfer belt 308 rotates. At the secondary transfer position 309, the toner images are transferred from the intermediate transfer belt 308 to the recording material conveyed through the conveyance path 303 (secondary transfer). The recording material after secondary transfer is conveyed to the fixing unit 311. The fixing unit 311 includes a pressure roller and a heating roller. While the recording material passes between these rollers, heat and pressure are applied to the recording material, and a fixing process for fixing the toner image to the recording material is performed. The recording material that has passed through the fixing unit 311 is conveyed through the conveyance path 312 to the conveyance path 315 that conveys the recording material to the diagnosis unit 108. In this way, a color image is formed (printed) on the recording material.

[0027] When further fixing processing is required according to the type of the recording material, the recording material that has passed through the fixing unit 311 is guided to the conveyance path 314 where the fixing unit 313 is provided. The fixing unit 313 performs further fixing processing on the recording material conveyed through the conveyance path 314. The recording material that has passed through the fixing unit 313 is conveyed to the conveyance path 315. Also, when an operation mode for performing double-sided printing is set, an image is printed on the first side, and the recording material conveyed through the conveyance path 312 or the conveyance path 314 is guided to the reverse path 316. The recording material reversed in the reverse path 316 is guided to the double-sided conveyance path 317 and conveyed to the secondary transfer position 309. Thereby, the toner image is transferred to the second side opposite to the first side of the recording material at the secondary transfer position 309. Thereafter, as the recording material passes through the fixing unit 311 (and the fixing unit 313), the formation of the color image on the second side of the recording material is completed.

[0028] When the formation (printing) of the image in the printing unit 107 is completed and the printed recording material is conveyed to the conveyance path 315, it is then conveyed into the diagnosis unit 108. The diagnosis unit 108 includes image reading devices 331 and 332 having a CIS (Contact Image Sensor) on the conveyance path 330 through which the printed recording material from the printing unit 107 is conveyed. The image reading devices 331 and 332 are arranged at positions facing each other via the conveyance path 330. The image reading devices 331 and 332 are each configured to read the upper surface (first surface) and the lower surface (second surface) of the recording material. Note that the image reading device may be configured by, for example, a CCD (Charge Coupled Device) or a line scan camera instead of a CIS.

[0029] The diagnosis unit 108 performs an inspection process for determining the presence or absence of image defects in the printed matter based on the image data obtained by reading the printed matter (recording material) conveyed on the conveyance path 330 with the image reading devices 331 and 332. Specifically, at the timing when the printed matter being conveyed reaches a predetermined position, a reading process of reading the printed matter is performed using the image reading devices 331 and 332. When the reading process is completed, an inspection process is performed based on the read image data. As a result of the inspection process, when it is determined that there is a printing result in the printed matter, it is determined whether to stop the inspection based on the inspection result of the read image data for which the determination of the inspection process has been completed. Specifically, the inspection is stopped when image defects are continuously detected in a predetermined number or more of printed matters. It is desirable that this predetermined number can be specified by the user. When it is determined to stop the inspection, an image diagnosis process for diagnosing whether the malfunction location that is the cause of the image defect is included in the printing unit 107 and the image reading devices 331 and 332 is performed. The printed matter that has passed through the diagnosis unit 108 is sequentially conveyed to the stacker 109. The specific process of the diagnosis unit 108 will be described later.

[0030] Stacker 109 includes a stack tray 341 as a tray on which printed materials conveyed from a diagnostic unit 108 arranged upstream in the conveyance direction of the printed materials are stacked. The printed materials that have passed through the diagnostic unit 108 are conveyed through a conveyance path 344 in the stacker 109. When the printed materials conveyed through the conveyance path 344 are guided to a conveyance path 345, the printed materials are stacked on the stack tray 341. The stacker 109 further includes an escape tray 346 as a paper discharge tray. In Embodiment 1, the escape tray 346 is used for discharging printed materials in which image defects have been detected by the diagnostic unit 108. When the printed materials in which image defects have been detected and conveyed through the conveyance path 344 are guided to a conveyance path 347, they are conveyed to the escape tray 346. The printed materials that have been conveyed through the stacker 109 without being stacked or discharged are conveyed to a subsequent finisher 110 through a conveyance path 348. The stacker 109 further includes an inversion unit 349 for inverting the orientation of the conveyed printed materials. The inversion unit 349 is used, for example, to make the orientation of the printed materials input to the stacker 109 the same as the orientation of the printed materials when they are stacked on the stack tray 341 and output from the stacker 109. Note that the inversion operation by the inversion unit 349 is not performed on the printed materials that are conveyed to the finisher 110 without being stacked in the stacker 109.

[0031] The finisher 110 performs the finishing function specified by the user on the printed matter conveyed from the diagnostic unit 108 arranged upstream in the conveyance direction of the printed matter. In Embodiment 1, the finisher 110 has finishing functions such as a stapling function (stapling at one or two locations), a punching function (two holes or three holes), and a saddle stitching binding function. The finisher 110 includes two paper discharge trays 351 and 352. When the finishing process by the finisher 110 is not performed, the printed matter conveyed to the finisher 110 is discharged to the paper discharge tray 351 through the conveyance path 353. When the finishing process such as stapling is performed by the finisher 110, the printed matter conveyed to the finisher 110 is guided to the conveyance path 354. The finisher 110 performs the finishing process specified by the user on the printed matter conveyed through the conveyance path 354 using the finishing processing unit 355, and discharges the printed matter on which the finishing process has been performed to the paper discharge tray 352. The paper discharge trays 351 and 352 can be raised and lowered respectively, and it is also possible to operate so as to lower the paper discharge tray 351 and stack the sheets finished by the finishing processing unit 355 on the paper discharge tray 351. When saddle stitching binding is specified, after stapling at the center of the sheet by the saddle stitching processing unit 356, the sheet is folded in half and output to the saddle stitching binding tray 358 via the sheet conveyance path 357. The saddle stitching binding tray 358 has a belt conveyor configuration, and the saddle stitching bound bundle stacked on the saddle stitching binding tray 358 is configured to be conveyed to the left in FIG. 2.

[0032] FIG. 3 is a schematic functional block diagram of the image forming apparatus 101, the external controller 102, and the client PC 103 according to Embodiment 1.

[0033] First, the printing unit 107 of the image forming apparatus 101 will be described.

[0034] The printing unit 107 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 208, and a UI display unit 225. The printing unit 107 further includes a printing unit (printer engine) 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. The communication I / F 201 is connected to a diagnostic unit 108, a stacker 109, and a finisher 110 via a communication cable 260. Also, the CPU 206 performs communication for controlling each device via the communication I / F 201. The network I / F 204 is connected to an external controller 102 via an 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. Incidentally, the printing unit 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. Various programs or data are stored in the HDD 208. The CPU 206 controls the operation of the entire printing unit 107 by expanding and executing the programs stored in the HDD 208 in the memory 207. Programs and data required when the CPU 206 performs various processes are stored in the memory 207. The memory 207 includes a RAM, a ROM, and the like and operates as a work area for the CPU 206. The UI display unit 225 receives inputs of various settings and operation instructions from the user and is used for displaying various information such as setting information and the processing status of a print job.

[0035] Next, the diagnostic unit 108 of the image forming apparatus 101 will be described.

[0036] The diagnostic unit 108 includes a communication I / F 211, a CPU 212, a memory 213, an HDD 214, image reading devices 331 and 332, an inspection unit 216, an image diagnostic unit 217, and a UI display unit 215. These devices are connected to each other via a system bus 221 so as to be able to transmit and receive data. The communication I / F 211 is connected to the printing unit 107 via a communication cable 260. The CPU 212 performs communication necessary for controlling the diagnostic unit 108 via the communication I / F 211. The CPU 212 controls the operation of the diagnostic unit 108 by expanding and executing the control programs stored in the inspection unit 216, the image diagnostic unit 217, and the HDD 214 in the memory 213. Control programs for the image reading devices 331 and 332 are stored in the HDD 214. Also, a control program for inspection processing is stored in the inspection unit 216, and a control program for image diagnostic processing is stored in the image diagnostic unit 217, respectively. Further, the image diagnostic processing unit 217 includes a feature amount calculation unit 218 that calculates a feature amount for specifying a defect factor of the image forming apparatus 101 from an image defect, a factor specifying unit 219 that specifies the cause of the image defect based on the feature amount, and a corresponding processing unit 220 that performs corresponding processing according to the specified cause. Details of the inspection processing and the image diagnostic processing will be described later.

[0037] The image reading devices 331 and 332 read the image of the conveyed printed matter according to the instruction of the CPU 212. The CPU 212 determines whether or not the printed matter includes an image defect by using the program stored in the inspection unit 216 based on the read image data of the printed matter read by the image reading devices 331 and 332. When there is an image that satisfies a predetermined condition which is a detection condition of an image defect in the read image data (when there is an image defect), the CPU 212 specifies and diagnoses the defective part of the image forming apparatus 101 that caused the image defect by using the program stored in the image diagnostic unit 217. Note that the image reading devices 331 and 332 may be provided separately from the diagnostic unit 108.

[0038] The UI display unit 215 is used for displaying diagnostic results and setting screens, etc. For example, it is possible to set the type of image defect detected in the inspection process, the detection level indicating the lower limit of the size detected as an image defect, etc. Furthermore, it is possible to set the number of consecutive detections of image defects, which is a condition for stopping the inspection and starting the image diagnosis process. Note that the above setting examples are merely examples, and the present invention is not limited thereto. It may also be possible to set the factors to be detected in the image diagnosis process. The operation unit is also used as the UI display unit 215, is operated by the user, and receives various instructions from the user.

[0039] The stacker 109 controls whether to discharge the printed matter discharged from the diagnostic unit 108 and conveyed through the conveyance path to the stack tray, to the escape tray, or to convey it to the finisher 110 connected to the downstream side in the conveyance direction of the printed matter.

[0040] The finisher 110 controls the conveyance and discharge of the printed matter, and performs finishing processes such as stapling, punching, or saddle stitching on those printed matters.

[0041] Next, the external controller 102 will be described.

[0042] The external controller 102 includes a CPU 251, a memory 252, an HDD 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. The CPU 251 executes, by expanding and executing a program stored in the HDD 253 in the program memory 252, processes such as receiving print data from the client PC 103, RIP processing, and transmitting the print data to the image forming apparatus 101. Thus, the CPU 251 controls the operation of the entire external controller 102. Programs and data necessary for the CPU 251 to perform various processes are stored in the memory 252. The memory 252 includes a RAM and a ROM and operates as a work area for the CPU 251. Various programs and data are stored in the HDD 253. 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 operation screens. 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 communicable with a print unit 107, a diagnostic unit 108, a stacker 109, and a finisher 110 via the internal LAN 105 and a communication cable 260. The video I / F 258 is connected to the image forming apparatus 101 via a video cable 106 and is used for communication of data such as image data (print data).

[0043] Next, the client PC 103 will be described.

[0044] The client PC 103 includes a CPU 261, a memory 262, an HDD 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 that they can transmit and receive data. The CPU 261 controls the operation of each device via the system bus 269 by expanding and executing the program stored in the HDD 263 in the memory 262. Thereby, various processes by the client PC 103 are realized. For example, the CPU 261 generates print data and issues a print instruction by expanding and executing a document processing program stored in the HDD 263 in the memory 262. The memory 262 stores programs and data required when the CPU 261 performs various processes. The memory 262 includes a RAM and a ROM and operates as a work area for the CPU 261.

[0045] In the HDD 263, various applications such as a document processing program and programs such as a printer driver, and various data are stored. The display unit 264 is, for example, a display and is used for displaying information on the application being executed in the client PC 103 and the operation screen. The keyboard 265 is used for inputting operation instructions for the client PC 103 from the 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.

[0046] Next, the inspection process and the image diagnosis process executed by the inspection unit 216 according to Embodiment 1 will be described with reference to the drawings.

[0047] FIG. 4 is a flowchart for explaining the image diagnosis process by the diagnosis unit 108 according to Embodiment 1. In FIG. 4, the overall flow from the start of the reading operation of the printed matter by the image reading devices 331 and 332 to the image diagnosis process is shown. In the description of the flowchart, the symbol "S" represents a step. This shall also be the same in the following description of the flowchart. Also, the processing of each step in FIG. 4 is achieved by the CPU 212 of the diagnosis unit 108 executing the program expanded in the memory. In the image forming apparatus 101 according to the embodiment, although the images on both sides of the printed matter can be inspected using the image reading devices 331 and 332, here, for simplicity of explanation, an example of inspecting the image using the image reading device 331 will be described.

[0048] First, the details of the inspection process will be described. This inspection process is represented by the processes from S401 to S408, and the CPU 212 determines whether the read image data contains image defects by expanding and executing the program stored in the inspection unit 216 in the memory.

[0049] First, in S401, the CPU 212 acquires the read image data by reading the printed matter using the image reading device 331 at the timing when the printed matter conveyed on the conveyance path 330 reaches a predetermined position. Then, the CPU 212 stores this read image data in the HDD 214 of the diagnosis unit 108 and proceeds to S402. In S402, the CPU 212 acquires the settings of the inspection process stored in the HDD 214. In Embodiment 1, the CPU 212 acquires the types of image defects to be detected in this inspection process and the detection level for determining as an image defect, stores the acquired inspection process settings in the memory 213, and proceeds to S403.

[0050] In S403, the CPU 212 acquires the original image data used to print the printed matter stored in the HDD 208 of the printing unit 107. At this time, the CPU 212 uses a known color conversion matrix provided in advance to convert the color of the original image data into a color signal value with the same standard as the read image data. The original image data thus color-converted is stored in the HDD 214 as reference image data without image defects, and the process proceeds to S404. Here, an example of generating reference image data by color-converting the original image data is shown, but the method for creating the reference image data is not limited to the above example. For example, a printed matter created based on the original image data may be read by the image reading device 331, and the read image data may be used as the reference image data.

[0051] Next, the process proceeds to S404, and the CPU 212 detects the pixel positions of the paper corners of the paper of the printed matter from the read image data. The method for detecting the paper corners is not particularly limited here. For example, a method of extracting a pixel region similar to the paper corner image held in advance by template matching and calculating the centroid of the pixel region can be mentioned. Also, any method by which the paper corners can be detected may be used, and the intersection of the outermost edges extracted by the Hough transform may be used as the paper corners. The four pixel positions detected as the paper corners are stored in the HDD 214 of the diagnosis unit 108, and the process proceeds to S405.

[0052] At S405, the CPU 212 aligns the paper corners and the positions of the same pattern within the original image data between the reference image data created at S403 and the read image data. The purpose of aligning the positions here is to improve the accuracy in the differential detection described later. In Embodiment 1, alignment processing by rigid body transformation is performed. In the alignment processing, first, reference feature points such as paper corners and edge portions of the pattern are extracted from the reference image data and the read image data. Next, the feature points indicating the same components are associated with each other, and an affine matrix is calculated such that the sum of the Euclidean distances between the feature points is minimized. Finally, an affine transformation is performed on the reference image data to bring the positions of the feature points of the reference image data closer to the positions of the feature points of the read image data. When the reference image data generated by performing the affine transformation in this way is saved in the HDD 214, the process proceeds to S406. Note that, in Embodiment 1, an example of performing alignment processing by rigid body transformation has been described, but it is not limited to the above example. When performing alignment including local distortion in the read image data caused by uneven conveyance speed of the printed matter, etc., non-rigid transformation such as known free-form deformation (FFD), thin plate spline (TPS), and landmark LDDMM method may be used.

[0053] Then at S406, the CPU 212 creates a difference image by obtaining the difference in luminance of each pixel between the reference image data and the read image data, and creates a binary image representing the presence or absence of image defects by comparing each difference value of the difference image with a threshold value. Here, first, each image data is converted into luminance value data, and the difference in luminance values is calculated. The conversion from the RGB signal value to the luminance value Y is performed using the following formula (1) defined in ITU-T BT.709.

[0054] Y = 0.2126×R + 0.7152×G + 0.0722×B … Formula (1) The difference image data Ydiv is calculated using the following formula (2) with the reference image data as Yref and the read image data as Yins.

[0055] Ydiv = Yref - Yins … Formula (2) At this time, the differential image data Ydiv is held as data having both positive and negative values. When the differential image data Ydiv has a positive value, since it is an image darker than the reference, it represents an image defect with a higher density than expected. Next, a filtering process for emphasizing a specific shape is applied to the differential image data.

[0056] FIG. 7 is a diagram for explaining a filtering process for emphasizing a specific shape.

[0057] For example, FIG. 7(a) is a filter for emphasizing a dot-shaped image defect, and FIG. 7(b) is a filter for emphasizing a linear defect. Which filter to use is determined according to the type of image defect included in the inspection settings obtained in S402. For example, when a dot defect is set as the image defect to be detected, the filtering process using the filter of FIG. 7(a) is executed. On the other hand, when a linear defect is set as the image defect to be detected, the filtering process using the filter of FIG. 7(b) is executed. Further, the CPU 212 performs a binarization process on the filtered differential image based on a threshold value corresponding to the inspection level obtained in S402. In this binarization process, an image is generated in which the pixel value of a pixel exceeding the threshold is set to "1" and the pixel value of a pixel below the threshold is set to "0". Thus, when the CPU 212 generates the binary image data, it saves it in the HDD 214 and proceeds to S407.

[0058] In S407, the CPU 212 determines whether there are pixels exceeding the threshold in the binary image, that is, pixels (image defects) with a pixel value of "1". If it is determined in S407 that there are no pixels exceeding the threshold, that is, there are no image defects, the process proceeds to S408, and the CPU 212 ends this inspection process assuming that there are no image defects in the printed matter. On the other hand, if it is determined in S407 that there are pixels exceeding the threshold, the process proceeds to S409 and the image diagnosis process is started.

[0059] Next, the details of the image diagnosis process will be described. This image diagnosis process is represented by the processes of S409 to S413. Here, the CPU 212 functions as each of these units based on the program stored in the feature quantity calculation unit 218, the cause identification unit 219, and the cause identification unit 219. In this way, the CPU 2112 identifies the defective parts of the image forming apparatus 101 and the image reading apparatuses 331 and 332, and implements corresponding measures if there are defective parts.

[0060] In S409, the CPU 212 acquires the inspection result of the read image data that has already been conveyed and determined by the inspection process and stored in the HDD 214, and determines whether to stop the printing process including the determined inspection result. At this time, the CPU 212 acquires, for example, the number of printed materials in which image defects have been continuously detected based on the acquired inspection result. Then, referring to the threshold value of the number of consecutive detections of image defects included in the settings acquired in S402, it is determined whether the acquired number of continuously detected images exceeds the threshold value. If the number of consecutive detections does not exceed the threshold value, it is determined that there is a low possibility that the image forming apparatus 101 and the image reading apparatus 331 include defective parts, and this image diagnosis process is terminated.

[0061] Conversely, if it is determined that the number of consecutive detections exceeds the threshold value, the printing process by the image forming apparatus 101 is stopped and the process proceeds to S410. Specifically, the CPU 212 sends an instruction to interrupt the creation of the printed material to the printing unit 107. The printing unit 107 that has received this instruction to interrupt the creation of the printed material performs control to stop the printing unit 107 in order to interrupt the creation of the printed material by the CPU 206. By stopping the creation of the printed material, the processes from the generation to the inspection of the printed material are stopped.

[0062] Then, in S410, the CPU 212 calculates a feature amount for each image defect from the read image data and creates feature amount table data. The feature amount in Embodiment 1 is a feature of the image defect. Examples of the feature amount include the color of the image defect portion. The color is calculated from color information such as whether it is a single color of yellow, magenta, cyan, or black or a multi-color generated by a combination of color materials provided in the image forming apparatus 101. The feature amount also includes contrast information indicating the density of the image defect portion. There are two patterns for the contrast information: when the color material is not well formed on the paper and is left white, the density is lower than the assumed density; conversely, when the color material is formed too much and the density becomes high. Furthermore, the feature amount includes shape information indicating the size of the image defect, coordinate information indicating the position in a direction perpendicular to the paper conveyance direction in the printing unit 107, and periodic information indicating that image defects with similar features occur periodically among a plurality of consecutive pages. Note that the above feature amounts are examples, and the present invention is not limited thereto. The key point is that any feature amount necessary to identify the factor to be repaired or addressed (repair and countermeasure) based on the result of the image diagnosis process is acceptable. For example, when identifying a factor characteristic of density unevenness within an image defect, the uniformity of the density within the image defect may be added as contrast information. Also, when identifying a factor for which the image defect has a shape extending in the paper conveyance direction, the aspect ratio between the conveyance direction and the paper width direction may be added as shape information. Thus, in S410, the CPU 212 calculates the above feature amounts for each image defect detected in the determination of S407 and creates table data of the feature amounts as shown in, for example, FIG. 18(a).

[0063] FIG. 8 is a flowchart for explaining the calculation of the feature amount and the creation process of the feature amount table data in S410 of FIG. 4. The process shown in this flowchart is achieved by the CPU 212 of the inspection unit 108 executing a process based on a program stored in the feature amount calculation unit 218.

[0064] In S801, the CPU 212 acquires the differential image data calculated in S406 from the HDD 214, stores it in the memory 213, and proceeds to S802. In S802, the CPU 212 acquires the input image data corresponding to the printed matter stored in the HDD 208 of the printing unit 107, and calculates a diagnosable region based on it. The diagnosable region in Embodiment 1 is two-dimensional image data indicating a region where image defects can be correctly detected. When the inspection process according to the embodiment is performed, it is not possible to correctly detect only image defects, and there is a possibility of erroneously detecting a region that is not an image defect as an image defect. Therefore, based on the input image data, a diagnosable region is created by excluding a region where the calculation accuracy of the feature amount is low. In Embodiment 1, two types of diagnosable regions are created according to the density of the image defect. Specifically, based on the input image data, the amount of colorant for each region of the image is calculated, and a high-density region where the amount of colorant is equal to or more than a certain level is determined as a region where an image defect with a density lower than expected can be diagnosed. Also, a low-density region where the amount of colorant is equal to or less than a certain level is determined as a region where an image defect with a density higher than expected can be diagnosed.

[0065] FIG. 9 is a schematic diagram for explaining a diagnosable region used for calculating a feature amount in the image diagnosis process according to Embodiment 1.

[0066] FIG. 9(a) shows an example of data obtained by calculating the amount of colorant for each region of the image. The amount of colorant uses the total amount of each colorant amount held as input image data. The image forming apparatus 101 according to Embodiment 1 is equipped with four-color colorants and accepts input values of 0 to 100 for each colorant. Therefore, the maximum value is 400. FIG. 9(a) shows an example of a gradation image in which the density increases from the upper left to the lower right.

[0067] FIG. 9(b) shows a schematic diagram of an area where image defects with a concentration lower than expected can be diagnosed, and FIG. 9(c) shows a schematic diagram of an area where image defects with a concentration higher than expected can be diagnosed. In FIGS. 9(b) and 9(c), "0" indicates that diagnosis is impossible, and "1" indicates that diagnosis is possible. In FIG. 9(b), in order to diagnose image defects with a concentration lower than expected, an area with an intermediate concentration (ink amount = 200) or higher is set as the diagnosable area. On the other hand, in FIG. 9(c), in order to diagnose image defects with a concentration higher than expected, an area with an intermediate concentration (ink amount = 200) or lower is set as the diagnosable area. After calculating the diagnosable area in this way and storing it in the memory 213, the process proceeds to S803.

[0068] In S803, the CPU 212 compares the differential image data acquired in S801 with the diagnosable area acquired in S802, selects image defects in the diagnosable area, and further selects image defects from the selected image defects in order to improve the calculation accuracy of the feature amount. That is, when an image defect with a concentration lower than expected, for which the differential image data is a negative value, exists in the diagnosable area shown in FIG. 9(b), the print result is left as a valid image defect. Also, when an image defect with a concentration higher than expected, for which the differential image data is a positive value, exists in the diagnosable area shown in FIG. 9(c), the print result is left as a valid image defect. When the selection of the above image defects is completed, the process proceeds to S804.

[0069] The feature amount calculation process in S804 is performed for each image defect selected in S803 and is repeated until the feature amount is calculated for all image defects. The feature amount includes five types: the type of defect, color, coordinate information, shape information indicating the size, contrast information indicating the concentration, and periodic information of the image defect. In S804, feature amounts other than the periodic information that can be calculated only for the selected image defects are calculated. Here, the feature amount is calculated based on the read image data, the reference image data, the differential data calculated in S406, and the binary image indicating the presence or absence of the image defect obtained in S407.

[0070] For example, in the case where it has been detected as an image defect in a binary image binarized after applying the dot defect filter shown in FIG. 7(a), the dot is used as a feature quantity for the defect type. Also, the color of the image defect part is determined from the CMYK combination that exceeds the threshold value after converting the average RGB value in the image defect part of the read image data to CMYK based on the color conversion table. At this time, if only C (cyan) exceeds the threshold value, it is a C single color, and if CM, two colors, exceed the threshold value, it is a CM mixed color. This color conversion table is created in advance by measuring the color of a printed matter printed by the printing unit 107. More preferably, it is desirable to provide a color conversion table for each type of recording material. The coordinate information is based on the left end at the head of the conveyance direction of the printed matter, and the coordinates of the center of gravity of the image defect are calculated based on the difference image. Similarly, the shape information is calculated with the maximum size in the conveyance direction of the image defect as the height and the maximum size in the paper width direction as the width based on the difference image. The contrast information is determined from the positive or negative of the signal value of the image defect part of the difference data. When the difference data is positive, it is set as "plus (+)" as the contrast, and when it is negative, it is set as "minus (-)". An example of the calculation results of the feature quantities other than the periodicity information in the case of three types of image defects is shown in FIG. 18(a).

[0071] In FIG. 18(a), the defect ID is identification information for specifying the image defect. Two of the three types of image defects are dot-like, and one is vertical line-like, and it can be seen that this vertical line-like image defect occurs over the entire read image data.

[0072] Thus, in S804, the feature quantities other than the periodicity of each image defect are calculated and recorded. In S804, the CPU 212 selects one image defect for which the feature quantity has not been calculated and calculates the feature quantity. After calculating the feature quantity of the image defect selected in S803 and storing it in the memory 213, the process proceeds to S805.

[0073] The processes of S805 to S811 are repeated until periodicity determination is performed for all the selected image defects, similar to S804. In S805, the CPU 212 determines whether periodicity determination is necessary. Specifically, it determines whether the defect type occurs periodically in the conveyance direction. As described above, the image forming apparatus 101 has a specification in which parts corresponding to the paper width are rotated and images are formed in order from the upper end of the paper. Therefore, when a malfunction occurs in the apparatus, dot-like defects and linear defects (horizontal streaks) perpendicular to the conveyance direction occur periodically, but linear defects (vertical streaks) parallel to the conveyance direction do not have periodicity. From the above perspective, the CPU 212 determines whether the defect type of the image defect selected in S804 is dot-like or horizontal streak, and if it is dot-like or horizontal streak, it performs the processes after S806. Otherwise, it proceeds to S812.

[0074] In S806, the CPU 212 searches for and acquires from the HDD 214 the read image data that was read before the read image data already read and acquired in S401. It checks for image defects in the read image data read in the past, and determines whether the image defect selected in S804 occurs periodically in the conveyance direction. Here, the read image data that has been read is to acquire all of the read image data of the number of sheets in which image defects were continuously detected in the aforementioned S406. After acquiring the read image data in this way and storing it in the memory 213, it proceeds to S807. Note that in the first embodiment, an example of determining whether to perform periodicity determination according to the defect type has been shown, but this determination criterion is not limited to the above example. For example, the paper size of the printed matter may be compared with the period to be specified for determination. Specifically, when there is a possibility that a period of two cycles or more consisting of three image defects is included in the paper, periodicity determination may be performed from one sheet of read image data. In the above case, when the defect type is a linear defect (vertical streak) parallel to the conveyance direction, or when there are no three or more image defects at substantially the same main scanning position in one sheet of read image data, it is determined that periodicity determination is performed from a plurality of sheets of read image data.

[0075] In S807, the CPU 212 performs an inspection process on the previously read image data acquired in S806. Since this inspection process is the same as the process described in S402 to S406, the description thereof is omitted.

[0076] In S808 to S810, the CPU 212 executes a process of selecting image defects from the read image data and calculating feature amounts. Since these processes are the same as the processes described in S802 to S804, the description thereof is omitted. Thus, when the feature amounts of the image defects are calculated from the read image data in S810 and stored in the memory 213, the process proceeds to S811.

[0077] In S811, the CPU 212 compares the feature amounts of the image defect selected in S804 with those of other image defects, and determines whether or not the image defect is an image defect having a substantially identical main scanning position and periodicity in the conveyance direction. The presence or absence of this periodicity is determined by referring to the periodicity (distance information in the conveyance direction) generated for each part in the image forming apparatus 101 previously held in the HDD 214, and checking whether there is a combination in which the main scanning positions of the image defects are close and match the above-mentioned periodicity. If it is determined here that there is periodicity, the distance in the conveyance direction is added to the periodic part of the selected image defect and stored in the memory 213. An example of the feature amount data at the stage when the process in S811 is completed is schematically shown in FIG. 18(b). Thus, when the storage of the feature amount data in the memory 213 is completed, the process proceeds to S812.

[0078] In FIG. 18(b), the feature amount data is such that periodicity information is added to the feature amount data in FIG. 18(a).

[0079] In S812, the CPU 212 stores the feature amount data calculated for each image defect as feature amount table data in the HDD 214. Thus, when the feature amount table data is stored in the HDD 214 and the feature amount calculation process is completed, the process proceeds to S411 in FIG. 4.

[0080] In S411, the CPU 212 compares the feature quantity table data with the factor identification table data (Fig. 18(c)) prepared in advance for each image defect, and identifies the factor corresponding to the image defect. Then, it selects a repair or countermeasure method according to the identified factor, and creates correspondence table data (Fig. 18(d)) in which the factor and the correspondence are added to the feature quantity table data.

[0081] Fig. 10 is a flowchart for explaining the process of creating feature quantity table data with the factors and correspondences added in S812 of Fig. 8.

[0082] In S1001, the CPU 212 acquires the factor identification table data stored in the HDD 214. An example of the factor identification table data is shown in Fig. 18(c). The factor identification table data is data showing the combination of the feature quantity calculated in S410 and the factor. It holds the features of the image defects corresponding to each defect factor. In Fig. 18(c), the condition marked with "-" represents a feature quantity that is not considered for determining whether it matches or not. In Fig. 18(c), it can be seen that the image defects caused by the photoreceptor drum occur at a cycle of 200 mm in the paper conveyance direction, and the image defects caused by the intermediate transfer belt occur at a cycle of 900 mm in the paper conveyance direction. After acquiring the factor identification table data in this way, the process proceeds to S1002.

[0083] In S1002, the CPU 212 selects one by one from the feature quantity table data created in S811 in ascending order of the defect ID, and executes the processes of S1003 to S1004. After the processes up to S1004 are completed, it selects the image defects for which the correspondence has not been determined and executes the processes of S1003 to S1004. In this way, the processes of S1003 and S1004 are repeated until the repair and countermeasure methods are determined for all the image defects.

[0084] In S1003, the CPU 212 determines whether there is a cause in the cause characteristic table data acquired in S1001 where all of the image defects selected in S1002 match the feature amounts. If there is a cause where all match, it is determined as the cause corresponding to the selected image defect. Also, when not all of the feature amounts prepared from the image defect in the cause characteristic table data have been detected, the cause where all of the detected feature amounts match is determined as the cause for that image defect. When the causes corresponding to all of the detected image defects have been determined in this way, the process proceeds to S1004.

[0085] In S1004, the CPU 212 determines a repair or corresponding measure (repair countermeasure method) according to the cause. The corresponding measures in Embodiment 1 are classified into corresponding measures that can be automatically restored by the adjustment function in the image forming apparatus 101 and corresponding measures that cannot be automatically restored. Examples of the corresponding measures that can be automatically restored include corresponding measures that can be automatically restored in the printing unit 107, such as cleaning the wire or grid of the corona charger, which is the charging means of the photosensitive drum provided in the printing unit 203 of the printing unit 107.

[0086] Examples of the corresponding measures that cannot be automatically restored include corresponding measures that require the user's work, such as cleaning the dirt on the reading surface of the image reading apparatus, and corresponding measures that require the work of a service technician, such as replacing parts. Also, examples of the corresponding measures that cannot be automatically restored include corresponding measures for fibers or foreign substances contained in the recording material before image formation.

[0087] The CPU 212 selects a corresponding measure for each cause based on a correspondence table (not shown) of causes and corresponding measures held in the HDD 214 in advance. In this way, a corresponding measure is selected for each image defect, and finally, correspondence table data describing the combination of the features, causes, and corresponding measures of the image defects is created and stored in the HDD 214. An example of this feature amount table data is shown in Fig. 18(d). The cause specified in S1003 and the corresponding measure selected in S1004 are added to the feature amount table data and stored in the HDD 214. When the feature amount table data is stored in this way, the process proceeds to S412.

[0088] At S412, the CPU 212 performs a countermeasure for fixing the defect based on the part that is the cause identified at S411. It is determined whether the countermeasure for the cause of the image defect identified at S411 is automatically recoverable. Here, if the CPU 212 determines that it is automatically recoverable, it executes automatic recovery control corresponding to the cause of the defect. On the other hand, if the CPU 212 determines that the countermeasure for the cause of the image defect is not automatically recoverable, it displays the image diagnosis result and its countermeasure method on the UI display unit 215.

[0089] FIG. 11 is a diagram showing an example of a UI display when dirt has occurred on the reading surfaces of the image reading devices 331 and 332 in Embodiment 1 and the user is requested to perform a cleaning operation.

[0090] As shown in FIG. 11, a display 1100 is made that shows the information "dirt on the image reading unit" indicating the identified cause of the defect, its countermeasure method, and the corresponding location within the image forming apparatus 101, and a prompt is presented to encourage the user to take countermeasures. The above-described countermeasures are implemented for each identified cause, and when the processing corresponding to all causes is completed, the process proceeds to S413.

[0091] At S413, the CPU 212 creates a PDF file as a diagnosis report for the content implemented as the image diagnosis process and saves it in the HDD 214.

[0092] FIG. 12 is a diagram showing an example of a diagnosis report according to Embodiment 1.

[0093] This diagnosis report describes information linking the model name of the image forming apparatus 101, the diagnosis date and time, the inspection settings, the types of image defects detected from the printed matter, the cause of the defect, and the countermeasures based on the cause of the defect. Note that this description is an example and is not limited to the above example. For example, the read image data used when identifying the cause of the defect may be saved as part of the report.

[0094] FIG. 13 is a diagram showing an example of a diagnosis report list according to Embodiment 1.

[0095] In addition, based on the user's instruction, it is desirable to display a list of diagnosed reports as shown in FIG. 13 so that a list of diagnosis results can be checked. Note that the format of this report is not limited to the above example. It is desirable to retain more detailed main body information as a report for the service technician who replaces parts.

[0096] FIG. 14 is a diagram showing an example of a diagnosis report list with the service mode according to Embodiment 1 added.

[0097] As shown in FIG. 14, the version of the main body firmware of the image forming apparatus 101, the feature amount of the image defect calculated in S410, and the counter information for each part may be saved together.

[0098] As described above, according to Embodiment 1, when an image defect is detected, if a predetermined condition is satisfied based on the previous inspection results, an image diagnosis process for specifying the cause of the malfunction of the apparatus is performed based on the read image data of the printed matter in which the image defect was detected. As a result, since the feature amount of the image defect is obtained using the read image data that has been read by the image reading apparatus, the downtime until resuming the creation of the printed matter can be shortened compared to the case of using a test chart, and it is possible to suppress a decrease in productivity.

[0099] <Modification Example of Embodiment 1> Note that in Embodiment 1, an example has been described in which the number of sheets of the recording medium (paper) in which image defects are continuously detected is used as the start condition for performing the image diagnosis process, but it is not limited to the above example. For example, it may be a case where an image defect capable of determining whether there is a malfunction in the image forming apparatus 101 and the image reading apparatus is detected. For example, when an image defect having a size and shape that can specify the cause of the malfunction is detected, the image diagnosis process may be performed even if the number of sheets of the recording medium (paper) in which the image defect is detected is one sheet. Similarly, when a plurality of image defects with a short cycle are included in the paper, or when the number of image defects is large at substantially the same main scanning position, the image diagnosis process may be performed.

[0100] [Embodiment 2] Next, the image diagnosis process according to Embodiment 2 of the present invention will be described. In the above-described Embodiment 1, an example of performing an image diagnosis process for identifying the cause of a malfunction of an image forming apparatus and an image reading apparatus based on the read image data of a printed matter in which an image defect is detected after the image defect detected in the printed matter satisfies a predetermined condition has been described.

[0101] In contrast, in Embodiment 2, for example, in a re-diagnosis mode for confirming whether a countermeasure implemented after identifying the cause of an image defect by an image diagnosis process has been correctly implemented, an example of determining whether to perform an image diagnosis process based on the read image data of a printed matter printed after resuming printing will be described. Note that since the configuration of the system according to Embodiment 2 and the hardware configuration in FIG. 2 are the same as those in the above-described Embodiment 1, the description thereof will be omitted.

[0102] FIG. 5 is a flowchart for explaining the image diagnosis process by the diagnosis unit 108 according to Embodiment 2. Note that in FIG. 5, processes common to the processes in FIG. 4 described above are denoted by the same reference numerals as in FIG. 4, and the description thereof will be omitted. Also, the processes in each step of FIG. 5 are achieved by the CPU 212 of the diagnosis unit 108 executing a program developed in the memory. The difference from FIG. 4 is that S501 is performed after S412.

[0103] In S501, the CPU 212 checks whether the image defect has been eliminated because the countermeasure in S412 has been correctly implemented. Specifically, without using a test chart, the stop state for inspection is released and printing is resumed. Then, the effect of the countermeasure is confirmed using the read image data of the printed matter obtained by resuming printing. In this way, in re-diagnosis, by using the read image data of the printed matter printed after resuming printing, it becomes possible to further shorten the downtime. The details of the re-diagnosis process will be described below.

[0104] FIG. 15 is a flowchart for explaining the re-diagnosis process of S501 in FIG. 5 according to Embodiment 2. Note that since the configuration of the printing system according to Embodiment 2, the flow of the image diagnosis process, etc. are the same as those in Embodiment 1, the explanations thereof are omitted. Only the re-diagnosis process implemented in addition to Embodiment 1 will be described. The processing of each step in FIG. 15 is executed by the CPU 212 of the diagnosis unit 108 in the same manner as in Embodiment 1.

[0105] In S1501, the CPU 212 releases the suspension of the printing process performed in S409, and resumes the creation and inspection process of the printed matter instructed by the user. When the creation of the printed matter is resumed and the printed matter reaches a position where it can be read by the image reading device 331, the process proceeds to S1502 where the read image data is acquired. Since the processing of S1502 to S1507 is the same inspection process as S401 to S406 in FIG. 4 of the foregoing Embodiment 1, the explanations thereof are omitted.

[0106] In S1508, if the CPU 212 determines that there is no image defect in the printed matter, the process proceeds to S1509; if it determines that there is an image defect in the printed matter, the process proceeds to S1510. In S1509, since the printed matter printed after the corresponding measure in S412 does not contain an image defect, the CPU 212 determines that the corresponding measure in S412 has been correctly executed. Then, a diagnosis report is created by the same process as in S413, and the re-diagnosis process ends.

[0107] Next, after resuming printing after the diagnosis process to repair the cause of the image defect, if an image defect is detected in the printed matter, the process proceeds to S1510. In S1510, the CPU 212 calculates a feature amount from the image defect in the same manner as in Embodiment 1. The calculated feature amount is stored in the HDD 214, and the process proceeds to S1511. In S1511, the CPU 212 identifies the cause of the image defect from the feature amount by the same process as in Embodiment 1 and stores it in the HDD 214. When the storage in the HDD 214 is completed, the process proceeds to S1512.

[0108] In S1512, the CPU 212 acquires the factors specified in S411 that are stored in the HDD 214, and determines whether they match the image defects caused by the factors specified in S1511. In this way, it is determined whether the countermeasure is not functioning correctly and the image defects are recurring. If image defects caused by the same factor are recurring, the process proceeds to S1513. After performing the countermeasure again, the process returns to S1501. On the other hand, if in S1512 image defects caused by the same factor are not recurring, it is determined that the countermeasure in S412 has been properly implemented, the process proceeds to S1509, a diagnosis report is created, and this re-diagnosis process ends.

[0109] As described above, according to Embodiment 2, a re-diagnosis process is executed to confirm whether the countermeasure by the image diagnosis process is being properly implemented. In this re-diagnosis process, the read image data obtained from the printed matter after resuming printing is used. By performing such a re-diagnosis process, it becomes possible to confirm the certainty of the countermeasure.

[0110] In addition, in Embodiment 2, an example was described in which it is determined whether the countermeasure has been properly implemented based on the result of the inspection process of the first printed matter after resuming printing. However, it is not limited to the above example. More preferably, it may be determined whether the countermeasure for the previous image defect has been properly implemented based on the inspection results for a plurality of sheets of paper after resuming printing.

[0111] Also, depending on the image defect, there are cases where the image defect is likely to be detected and cases where it is difficult to detect, depending on the pattern on the paper used to form the printed matter. For example, an image defect where the coloring material periodically drops at an unintended position on the printed matter is likely to be detected in an area of the paper where there is little coloring material, but may be difficult to detect in a high-density area where a large amount of coloring material is to be recorded. Therefore, based on the characteristics of the image defect due to the factor for which the countermeasure was implemented in S412 and the amount of coloring material for each area of the input image data, it may be determined whether to perform the diagnosis process after S1510. By doing so, it is possible to determine whether to perform the diagnosis process for each read image data.

[0112] [Embodiment 3] Next, the image diagnosis process according to Embodiment 3 of the present invention will be described. In the above-described Embodiment 1, an example was described in which when an image defect detected in a printed matter satisfies a predetermined condition, the cause of the image defect is specified based on the read image data of the printed matter in which the image defect was detected. However, the configuration of the image diagnosis process according to the present invention is not limited to the above example.

[0113] For example, after starting up the image forming apparatus 101, image diagnosis processing may be performed to check the state of the apparatus. In such a case, there is no read image data that has been read. Therefore, it is desirable to print a test chart prepared in advance and perform image diagnosis processing from the read image data of the test chart. In Embodiment 3, in addition to the image diagnosis process based on the read image data of the printed matter described in Embodiment 1, a configuration for performing image diagnosis processing using a test chart is provided, and an example of switching according to the state of the main body at the timing of performing the image diagnosis process will be described. Note that the configuration of the system according to Embodiment 3 and the hardware configuration in FIG. 2 are the same as those in the aforementioned Embodiment 1, and thus the description thereof will be omitted.

[0114] FIG. 16 is a flowchart for explaining the flow of the image diagnosis process performed after stopping printing for the inspection in S409 in Embodiment 3. What is different from Embodiment 1 is the determination process (S1601) for determining whether the image forming apparatus 101 is in an unprinted state after the main body is started up, and the processes of S1603 to S1605 that are performed when it is determined in the determination of S1601 that the image forming apparatus 101 is in an unprinted state. Hereinafter, these processes different from Embodiment 1 will be described in detail. These processes are executed by the CPU 212 in the diagnosis unit 108. S1601 starts when a printed matter including an image defect is continuously detected in S409 of Embodiment 1, or when an image diagnosis execution instruction from the user is instructed from the UI display unit 215.

[0115] In S1601, the CPU 212 queries the printing unit 107 to determine whether there is a job in the printing unit 107 where the creation of the printed matter has been interrupted. As a result of this determination process, if there is no interrupted job, the process proceeds to S1603. On the other hand, if there is an interrupted job, the process proceeds to S1602, and in the same process as S410 in Embodiment 1, the feature amount is calculated from the read image data after printing. Then, in the same manner as S411 and S412 in FIG. 4, the cause of the image defect is specified based on the feature amount, and the corresponding countermeasure corresponding to the cause is executed.

[0116] On the other hand, in S1603 when there is no interrupted job, the CPU 212 instructs the printing unit 107 to print the test chart to cause the test chart to be printed.

[0117] FIG. 17 is a diagram showing an example of a test chart used in the image diagnosis process in Embodiment 3.

[0118] The test chart 1700 has a non-image part 1701 where dirt adhering to unintended locations can be easily detected, and an image part 1702 where color bleeding that makes the color lighter than normal can be easily detected. The non-image part 1701 is a region located at the chart tip of the test chart 1700 in the conveyance direction and indicates a region where no image is formed. The image part 1702 is a region located other than the chart tip of the test chart 1700 in the conveyance direction and indicates a region where an image formed by a coloring material is formed. For example, the image part 1702 uses a single-color 50% area ratio image, and four test charts, one for each of the CMYK single colors, are printed. The CPU 251 of the external controller 102 transmits the bitmap data of the rasterized test chart from the video I / F 258 through the video cable 106 to the video I / F 205 of the printing unit 107. The CPU 206 of the printing unit 107 performs halftone processing on the bitmap data of the test chart received by the video I / F 205, and the test chart is printed in the print unit 203 based on the image data after halftone processing. Note that the configuration of the test chart is an example and is not limited to the above example. Although an example of the chart 1700 with a mixture of the non-image part 1701 and the image part 1702 has been described, charts separated into pages for each may also be used. Also, in order to reduce the number of recording materials used for the test chart, the image part 1702 may be a mixed color with two or more coloring materials mixed. When a printed matter corresponding to the test chart is created and conveyed to the position of the image reading device, it proceeds to S1604.

[0119] In S1604, the CPU 212 acquires the read image data of the test chart printed in S1603 through the same process as in S401. After acquiring the read image data, it saves it to the HDD 214 and proceeds to S1605. In S1605, the CPU 212 calculates the feature amount of the image defect from the read image data of the test chart based on the program stored in the feature amount calculation unit 218 in the same manner as in S410. Here, the difference from S410 is the area where the feature amount is detected. In S410 of Embodiment 1, since it is the read image data of the printed matter, it was necessary to specify the area where the feature amount was easily detected depending on the pattern. On the other hand, in Embodiment 3, since the feature amount is calculated from the image portion 1702 of the test chart with uniform density, it becomes possible to calculate the feature amount from the entire surface of the read image data. After calculating the feature amount in this way and saving it to the HDD 214, it proceeds to S1606. In S1606, through the same process as in S411 of Embodiment 1, based on the feature amount stored in the HDD 214, the cause of the malfunction in the image forming apparatus 101 and the image reading apparatus is specified and it proceeds to S1607. Since the corresponding measures in S1607 are the same as in S412, the description thereof is omitted.

[0120] As described above, according to Embodiment 3, in addition to the image diagnosis process based on the read image data of the printed matter, it has a configuration for performing the image diagnosis process using the test chart, and switches according to the state of the main body at the timing of performing the image diagnosis process. By automatically switching the image diagnosis process regardless of the user's instruction, it is possible to shorten the downtime due to the image diagnosis process and suppress the decrease in the productivity of the image forming apparatus.

[0121] In addition, in this embodiment, as an example of the condition for calculating the feature amount from the read image data of the test chart, an example where the image forming apparatus is in an unprinted state is shown, but it is not limited to the above example. For example, when it is difficult to specify the cause of the image defect only from the read image data of the printed matter, the test chart printing may be performed.

[0122] As described in S802, the calculation accuracy of the feature amount of the image defect may be low depending on the pattern of the read image data of the printed matter. For example, in a pattern with a large amount of coloring material and a high printing density, the difference from the reference image in the image defect due to dirt where the coloring material adheres to an unintended location is small, the calculation accuracy of the feature amount is low, and it is difficult to narrow down the cause. In such a case, it is desirable to stabilize the calculation accuracy of the feature amount by using the read image data obtained from the test chart 1700 including the non-image portion 1701 and the intermediate density image portion 1702. By stabilizing the calculation accuracy of the feature amount, it becomes possible to improve the accuracy of specifying the cause.

[0123] FIG. 6 is a flowchart for explaining the image diagnosis process by the diagnosis unit 108 according to Embodiment 3. In FIG. 6, the processes common to the process of FIG. 4 described above are denoted by the same reference numerals as in FIG. 4, and the description thereof is omitted. The process of each step in FIG. 6 is achieved by the CPU 212 of the diagnosis unit 108 executing a program developed in the memory. The difference from FIG. 4 is that the processes of S601 to S605 are performed between S411 and S412.

[0124] In S601, the CPU 212 acquires the total number of causes of the image defect specified in S411, and acquires the maximum value of the total number of causes of all image defects as the maximum number of factors. It is determined whether this maximum number of factors exceeds a threshold value, and if it exceeds the threshold value, the process proceeds to S602. This threshold value is set based on the downtime generated by printing the test chart and the downtime generated by the countermeasures for a plurality of causes, and is set based on the number of factors for which printing the test chart results in a shorter downtime. In Embodiment 3, for example, 5 is set as this threshold value. Since the processes of S602 to S605 are the same as the processes of S1603 to S1606 in FIG. 16, the description thereof is omitted.

[0125] As described above, according to the third embodiment, when the total number of factors of image defects is large, the identification of factors is insufficient, and it is assumed that the downtime is prolonged due to countermeasures, an image diagnosis process for calculating feature amounts from a test chart is performed. By doing so, it is possible to suppress the prolongation of the downtime.

[0126] (Other Embodiments) 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 apparatus via a network or a storage medium, and causing one or more processors in a computer of the system or apparatus to read and execute the program. Further, it can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

[0127] This specification and the drawings disclose the following printing system, image processing apparatus, and control method and program thereof.

[0128] [Item 1] A printing system having an image forming apparatus and a diagnostic apparatus, wherein the image forming apparatus generates a printed matter corresponding to an input image, and the diagnostic apparatus has a detection means for detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter, an acquisition means for acquiring a feature amount of the image defect detected by the detection means, and a specification means for specifying a cause of the image defect based on the feature amount acquired by the acquisition means, wherein the acquisition means stops generation of the printed matter by the image forming apparatus when the detection means detects the image defect and satisfies a predetermined condition, and acquires a feature amount of the image defect based on the read image.

[0129] [Item 2] The printing system according to item 1, wherein the predetermined condition includes a case where the number of printed materials in which the detection means has continuously detected image defects has reached a predetermined number.

[0130] [Item 3] The printing system according to item 2, wherein the predetermined condition further includes at least any one of a case where an image defect having a size and shape capable of specifying the cause of the image defect is detected, a case where a plurality of image defects with a short cycle are included in the printed material, and a case where many image defects occur at the same main scanning position.

[0131] [Item 4] The printing system according to any one of items 1 to 3, wherein the predetermined condition includes a case where the detection means detects the image defect after a countermeasure has been taken against the cause specified by the specifying means.

[0132] [Item 5] The printing system according to item 4, further comprising means for determining whether an image defect caused by the cause against which the countermeasure has been taken has recurred.

[0133] [Item 6] The printing system according to any one of items 1 to 5, wherein the acquisition means determines a region excluding a region where the calculation accuracy of the feature amount in the read image is low, and acquires the feature amount of the image defect based on the region of the read image.

[0134] [Item 7] The printing system according to any one of items 1 to 6, wherein the feature amount includes at least any one of the shape, color, size of the image defect, and information regarding the periodicity with which the image defect periodically occurs in the conveyance direction of the printed material.

[0135] [Item 8] The diagnostic device further includes storage means for storing data indicating the correspondence between the feature amount of the image defect and the parts of the image forming apparatus. The printing system according to any one of Items 1 to 7, wherein the specifying means specifies the cause of the image defect based on the feature amount and the data acquired by the acquiring means.

[0136] [Item 9] Further comprising an image reading device that reads the printed matter, The printing system according to Item 8, wherein the data stored in the storage means further includes data indicating the correspondence between the feature amount of the image defect and the parts of the image reading device.

[0137] [Item 10] The printing system according to any one of Items 1 to 9, wherein the diagnostic device further includes a presenting means that determines a corresponding measure corresponding to the cause specified by the specifying means and presents it to the user.

[0138] [Item 11] Further comprising a storage means for storing a test chart, When the predetermined condition is that the image forming apparatus has not printed, the acquiring means causes the image forming apparatus to print the test chart when the predetermined condition is satisfied, and acquires the feature amount of the image defect based on the read image of the test chart. The printing system according to any one of Items 1 to 10.

[0139] [Item 12] An image processing apparatus that detects an image defect of a printed matter printed by an image forming apparatus, A detecting means for detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter, An acquiring means for acquiring the feature amount of the image defect detected by the detecting means, And a specifying means for specifying the cause of the image defect based on the feature amount acquired by the acquiring means. The acquisition means stops generation of a printed matter by the image forming apparatus when the detection means detects the image defect and satisfies a predetermined condition, and acquires a feature amount of the image defect based on the read image. An image processing apparatus characterized by that.

[0140] [Item 13] The image processing apparatus according to item 12, wherein the predetermined condition includes a case where the number of printed matters in which the detection means continuously detects image defects reaches a predetermined number.

[0141] [Item 14] The image processing apparatus according to item 13, wherein the predetermined condition further includes at least any one of a case where an image defect having a size and shape capable of specifying the cause of the image defect is detected, a case where a plurality of image defects with a short cycle are included in the printed matter, and a case where many image defects occur at the same main scanning position.

[0142] [Item 15] The image processing apparatus according to any one of items 12 to 14, wherein the predetermined condition includes a case where the detection means detects the image defect after a countermeasure against the cause specified by the specifying means has been taken.

[0143] [Item 16] The image processing apparatus according to item 15, further comprising means for determining whether an image defect caused by the cause for which the countermeasure has been taken has recurred.

[0144] [Item 17] The image processing apparatus according to any one of items 12 to 16, wherein the acquisition means determines a region excluding a region where the calculation accuracy of the feature amount in the read image is low, and acquires the feature amount of the image defect based on the region of the read image.

[0145] [Item 18] The feature amount includes at least any one of information regarding the shape, color, size of the image defect, and periodicity in which the image defect periodically occurs in the conveyance direction of the printed matter, according to any one of claims 12 to 17. The image processing apparatus described.

[0146] [Item 19] Further comprising storage means for storing data indicating the correspondence between the feature amount of the image defect and the parts of the image forming apparatus, The specifying means specifies the cause of the image defect based on the feature amount acquired by the acquiring means and the data, according to claim 12. The image processing apparatus described.

[0147] [Item 20] Further comprising an image reading device for reading the printed matter, The data stored in the storage means further includes data indicating the correspondence between the feature amount of the image defect and the parts of the image reading device, according to claim 19. The image processing apparatus described.

[0148] [Item 21] Further comprising presenting means for determining a corresponding measure corresponding to the cause specified by the specifying means and presenting it to the user, according to any one of claims 12 to 20. The image processing apparatus described.

[0149] [Item 22] Further comprising storage means for storing a test chart, When the predetermined condition is that the image forming apparatus is unprinted, the acquiring means causes the image forming apparatus to print the test chart when the predetermined condition is satisfied, and based on the read image of the test chart. The image processing apparatus according to any one of claims 12 to 21, wherein the feature amount of the image defect is acquired.

[0150] [Item 23] A control method for controlling an image processing apparatus that detects an image defect of a printed matter printed by an image forming apparatus, A detecting step of detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter; An obtaining step of obtaining a feature amount of the image defect detected in the detecting step; A specifying step of specifying a cause of the image defect based on the feature amount obtained in the obtaining step, and having: The obtaining step stops generation of a printed matter by the image forming apparatus when the detecting step detects the image defect and satisfies a predetermined condition, and obtains a feature amount of the image defect based on the read image. A control method characterized by that.

[0151] [Item 24] A program characterized by causing a computer to function as each means of the image processing apparatus according to any one of Items 12 to 22.

[0152] The present invention is not limited to the above embodiments, and various changes and modifications are possible without departing from the spirit and scope of the present invention. Therefore, in order to disclose the scope of the present invention, the following claims are attached.

Explanation of Signs

[0153] 102... External controller, 107... Printing unit, 108... Diagnostic unit, 218... Feature amount calculation unit, 219... Cause specification unit, 220... Corresponding processing unit, 331, 332... Image reading device

Claims

1. A printing system having an image forming apparatus and a diagnostic apparatus, wherein the image forming apparatus generates a printed matter corresponding to an input image, and the diagnostic apparatus includes: detection means for detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter; acquisition means for acquiring a feature amount of the image defect detected by the detection means; specification means for specifying a cause of the image defect based on the feature amount acquired by the acquisition means, wherein the acquisition means stops generation of the printed matter by the image forming apparatus when the detection means detects the image defect and satisfies a predetermined condition, and acquires the feature amount of the image defect based on the read image. A printing system characterized by the above.

2. The printing system according to claim 1, wherein the predetermined condition includes a case where the number of printed matters in which the detection means continuously detects image defects reaches a predetermined number.

3. The printing system according to claim 2, wherein the predetermined condition further includes at least any one of a case where an image defect having a size and shape capable of specifying the cause of the image defect is detected, a case where the printed matter includes a plurality of image defects with a short cycle, and a case where many image defects occur at the same main scanning position.

4. The printing system according to claim 1, wherein the predetermined condition includes a case where the detection means detects the image defect after a countermeasure for the cause specified by the specification means has been taken.

5. The printing system according to claim 4, further comprising means for determining whether an image defect caused by the cause for which the countermeasure has been taken has recurred.

6. The printing system according to claim 1, wherein the acquisition means determines a region excluding a region where the calculation accuracy of the feature amount in the read image is low, and acquires the feature amount of the image defect based on the region of the read image.

7. The printing system according to claim 1, wherein the feature amount includes at least any one of the shape, color, size of the image defect, and information regarding periodicity in which the image defect periodically occurs in the conveyance direction of the printed matter.

8. The diagnostic apparatus further includes: storage means for storing data indicating a correspondence between the feature amount of the image defect and parts of the image forming apparatus. The printing system according to claim 1, wherein the specific means specifies the cause of the image defect based on the feature amount and the data acquired by the acquisition means.

9. Further comprising an image reading device that reads the printed matter, The printing system according to claim 8, wherein the data stored in the storage means further includes data indicating the correspondence between the feature amount of the image defect and the parts of the image reading device.

10. The printing system according to claim 1, wherein the diagnostic device further includes a presentation means for determining a corresponding measure corresponding to the cause specified by the specific means and presenting it to the user.

11. Further comprising a storage means for storing a test chart, When the predetermined condition is that the image forming apparatus is unprinted, the acquisition means causes the image forming apparatus to print the test chart when the predetermined condition is satisfied, and acquires the feature amount of the image defect based on the read image of the test chart. The printing system according to claim 1.

12. An image processing apparatus for detecting an image defect of a printed matter printed by an image forming apparatus, A detection means for detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter; An acquisition means for acquiring a feature amount of the image defect detected by the detection means; A specific means for specifying the cause of the image defect based on the feature amount acquired by the acquisition means, The acquisition means stops the generation of the printed matter by the image forming apparatus when the detection means detects the image defect and satisfies a predetermined condition, and acquires the feature amount of the image defect based on the read image. Image processing apparatus.

13. The image processing apparatus according to claim 12, wherein the predetermined condition includes a case where the number of printed matters in which the detection means continuously detects image defects reaches a predetermined number.

14. The predetermined condition further includes at least any one of a case where an image defect having a size and shape capable of specifying the cause of the image defect is detected, a case where the printed matter includes a plurality of image defects with a short cycle, and a case where many image defects occur at the same main scanning position. The image processing apparatus according to claim 13.

15. The image processing apparatus according to claim 12, wherein the predetermined conditions include a case where the detection means detects the image defect after a countermeasure has been taken against the factor specified by the specifying means.

16. The image processing apparatus according to claim 15, further comprising means for determining whether an image defect caused by the factor against which the countermeasure has been taken has recurred.

17. The acquisition means determines a region excluding a region where the calculation accuracy of the feature amount in the read image is low, and acquires the feature amount of the image defect based on the region of the read image. The image processing apparatus according to claim 12.

18. The feature amount includes at least any one of the shape, color, size of the image defect, and information regarding the periodicity with which the image defect periodically occurs in the conveyance direction of the printed matter. The image processing apparatus according to claim 12.

19. The image processing apparatus according to claim 12, further comprising storage means for storing data indicating the correspondence between the feature amount of the image defect and the parts of the image forming apparatus, wherein the specifying means specifies the cause of the image defect based on the feature amount acquired by the acquisition means and the data.

20. The image processing apparatus according to claim 12, further comprising an image reading apparatus for reading the printed matter, wherein the data stored in the storage means further includes data indicating the correspondence between the feature amount of the image defect and the parts of the image reading apparatus. The image processing apparatus according to claim 19.

21. The image processing apparatus according to claim 12, further comprising presenting means for determining a countermeasure corresponding to the cause specified by the specifying means and presenting it to the user.

22. The image processing apparatus according to claim 12, further comprising storage means for storing a test chart, wherein when the predetermined condition is that the image forming apparatus is unprinted, the acquisition means causes the image forming apparatus to print the test chart when the predetermined condition is satisfied, and acquires the feature amount of the image defect based on the read image of the test chart.

23. A control method for controlling an image processing apparatus that detects an image defect of a printed matter printed by an image forming apparatus, a detection step of detecting an image defect included in the printed matter based on a read image obtained by reading the printed matter; An acquisition step of acquiring a feature amount of the image defect detected in the detection step; A specifying step of specifying a cause of the image defect based on the feature amount acquired in the acquisition step, and having: The acquisition step is characterized in that when the detection step detects the image defect and satisfies a predetermined condition, the generation of a printed matter by the image forming apparatus is stopped, and the feature amount of the image defect is acquired based on the read image. A control method.

24. A program characterized by causing a computer to execute each step of the control method according to claim 23.

Images