Image diagnosis system and image diagnosis method

Abstract

The invention discloses an image diagnosis system and an image diagnosis method, the image diagnosis system comprises one or a plurality of processors, and the one or the plurality of processors are used for acquiring an image used for image diagnosis and formed by an image forming device as a diagnosis object. An image quality defect existing in the image for image diagnosis is detected from the image for image diagnosis, and emphasis display of the occurrence position of the detected image quality defect is switched according to an event.

Description

Technical Field

[0001] This invention relates to an image diagnostic system and an image diagnostic method. Background Technology

[0002] Japanese Patent Application Publication No. 2022-41718 discloses a mechanism that, when using multiple types of detection processes to detect image defects, outputs the types of detection processes in a manner that allows for confirmation of each image defect. In Patent Document 1, to address this problem, a method is disclosed that displays an overall image of the inspected object on a result display screen and uses a color representing the defect detection process used to emphasize the detected defects. Summary of the Invention

[0003] For example, in the event of a malfunction in a customer's image forming apparatus, the customer's engineer, in order to respond quickly, utilizes an image diagnostic system to diagnose the anomaly. This system, for example, detects features such as points or lines in the image to identify specific image quality defects that occurred during image formation or image reading. The customer engineer uses the image diagnostic system to determine the specific faulty component and appropriate action based on the image quality defects. However, even if the image quality defects are highlighted in the output image as a diagnostic result, in the prior art, this display can only show image quality defects by type. Therefore, in the prior art, to confirm the problem, the customer engineer needs to measure and confirm each image quality defect according to the display.

[0004] The objective of this invention is to reduce the burden on customer engineers in identifying image quality defects during image diagnostics.

[0005] According to a first aspect of the present invention, an image diagnostic system is provided, the image diagnostic system comprising one or more processors, the one or more processors acquiring an image for image diagnostic purposes formed by an image forming apparatus and being the object of diagnosis, detecting image quality defects present in the image for image diagnostic purposes, and switching the emphasis display of the location of the detected image quality defects according to a event switching.

[0006] According to a second aspect of the invention, in the image diagnostic system of the first aspect, the phenomenon is a variety of image quality defects, wherein one type of image quality defect is distinguished from other types of image quality defects and highlighted.

[0007] According to a third aspect of the invention, in the image diagnostic system of the first or second aspect, the mode of display of the emphasis differs according to the characteristics of the image quality defects of the aforementioned class.

[0008] According to a fourth aspect of the invention, in the image diagnostic system of the first aspect, the phenomenon is a plurality of image quality defects of one class, wherein, among the plurality of image quality defects of one class, each image quality defect is switched to be highlighted.

[0009] According to a fifth aspect of the invention, in the image diagnostic system of the fourth aspect, the mode of display of the emphasis varies according to the characteristics of the various image quality defects.

[0010] According to a sixth aspect of the invention, in the image diagnostic system of the fifth aspect, the characteristic of each image quality defect is the size of the respective image quality defect.

[0011] According to a seventh aspect of the invention, in the image diagnostic system of the fifth aspect, the characteristic of each image quality defect is the period during which the respective image quality defect is displayed.

[0012] According to an eighth aspect of the present invention, in the image diagnostic system of the first aspect, discrimination information for identifying the image quality defect is acquired as the image for image diagnostic formed by the image forming apparatus, and the discrimination information is displayed together with the emphasis display.

[0013] According to a ninth aspect of the present invention, in the image diagnostic system of the eighth aspect, the discrimination information is used to determine whether the defect is caused by the formation of an image of a specific color among multiple colors, or by the formation of an image of another color.

[0014] According to a tenth aspect of the invention, in the image diagnostic system of the eighth or ninth aspect, the discrimination information is a single-sided printed image and a double-sided printed image.

[0015] According to an eleventh aspect of the present invention, in an image diagnostic system according to any one of the eighth to tenth aspects, the discrimination information is information used to discriminate defects caused by readings from the image forming apparatus.

[0016] According to a twelfth aspect of the present invention, an image diagnosis method is provided, wherein an image for image diagnosis formed by an image forming apparatus is acquired and becomes the object of diagnosis; image quality defects present in the image are detected from the image for image diagnosis; and an emphasis display of the location of the detected image quality defects is switched according to the event.

[0017] (Effect)

[0018] According to the first aspect, the burden on customer engineers in identifying image quality defects during image diagnostics can be reduced.

[0019] Based on the second aspect, users are likely to focus on a type of image quality defect.

[0020] According to the third aspect, it is possible to uniquely identify each type of image quality defect.

[0021] Based on the fourth aspect, users are likely to focus on various image quality defects.

[0022] Based on the fifth aspect, it is easy to grasp the differences in the characteristics of various image quality defects.

[0023] According to the sixth aspect, it can help users grasp the severity of image quality defects.

[0024] According to the seventh aspect, users can easily confirm that the image quality defects are caused by the same reason.

[0025] According to the eighth aspect, users can easily identify image quality defects associated with a specific image.

[0026] According to the ninth aspect, the defects caused by the formation of images of a specific color become more apparent to the user.

[0027] According to the tenth aspect, the user is able to distinguish and confirm defects caused by the transfer section.

[0028] According to the eleventh aspect, the user is able to distinguish and confirm defects caused by reading.

[0029] According to the twelfth aspect, the burden on customer engineers in identifying image quality defects during image diagnostics can be reduced. Attached Figure Description

[0030] Figure 1 This is a diagram illustrating the image diagnostic system of this embodiment;

[0031] Figure 2 This is a diagram illustrating an example of the hardware structure of a server device;

[0032] Figure 3 This is a diagram used to illustrate an image forming apparatus;

[0033] Figure 4 This is a diagram illustrating the functional structure of a server device;

[0034] Figure 5 This is a flowchart illustrating the processing of the server device;

[0035] Figure 6 This is a graph representing the diagnostic results output by the server device;

[0036] Figure 7 This is a diagram representing a variation of Example 1 of the diagnostic results output by the server device;

[0037] Figure 8 This is a diagram representing a variation of the diagnostic results output by the server device, as shown in Example 2.

[0038] Figure 9 This is a diagram representing a variation of the diagnostic results output by the server device, as shown in Example 3.

[0039] Figure 10 This is a diagram representing a variation of the diagnostic results output by the server device, as shown in Example 4. Detailed Implementation

[0040] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0041] <Image Diagnostic System>

[0042] Figure 1 This is a diagram illustrating the image diagnostic system 1 of this embodiment. Additionally, Figure 2 This is a diagram illustrating an example of the hardware structure of server device 200. Additionally, Figure 3 This is a diagram used to illustrate the image forming apparatus 100.

[0043] The image diagnostic system 1 of this embodiment includes an image forming apparatus 100 that forms an image on paper, and a server device 200 connected to the image forming apparatus 100 via a communication line 190. In this embodiment, image diagnostics of the image diagnostic system 1 are performed in the server device 200. Furthermore, the image diagnostic system 1 includes a user terminal 300 connected to the server device 200, which receives operations from the user.

[0044] User terminal 300 has a display device 310. User terminal 300 is implemented by a computer. Examples of user terminal 300 include PC (Personal Computer), smartphone, and tablet terminal.

[0045] The server device 200 is implemented by a computer.

[0046] like Figure 2 As shown, the server device 200 has an arithmetic processing unit 11 that performs digital arithmetic processing according to a program and a secondary storage unit 12 that stores information. Furthermore, the control unit 21 of the image forming apparatus 100, described later, also has the same structure.

[0047] The secondary storage unit 12 is implemented, for example, by existing information storage devices such as HDD (Hard Disk Drive), semiconductor memory, and magnetic tape.

[0048] The arithmetic processing unit 11 has a CPU 11a as an example of a processor.

[0049] In addition, the arithmetic processing unit 11 has RAM 11b, which serves as working memory for CPU 11a, and ROM 11c, which stores programs executed by CPU 11a.

[0050] In addition, the arithmetic processing unit 11 has an interface unit 11e for various units such as a non-volatile memory 11d that is rewritable and can retain data even when the power supply is interrupted, and a communication unit that controls the connection with the arithmetic processing unit 11.

[0051] The non-volatile memory 11d is, for example, composed of SRAM or flash memory that is backed up by a battery. For example, the non-volatile memory 11d stores the correlation between image quality defects and emphasis display, which will be described later. Additionally, for example, the non-volatile memory 11d stores diagnostic images, which will be described later. In addition to storing files, the secondary storage unit 12 also stores programs executed by the arithmetic processing unit 11.

[0052] In this embodiment, the arithmetic processing unit 11 executes each process by reading the program stored in the ROM 11c or the secondary storage unit 12.

[0053] The program executed by CPU 11a can be provided to server device 200 in a state stored on a computer-readable recording medium such as magnetic recording medium (magnetic tape, disk, etc.), optical recording medium (optical disc, etc.), optical-magnetic recording medium, semiconductor memory, etc. Alternatively, the program executed by CPU 11a can also be provided to server device 200 via a communication unit such as the Internet.

[0054] In this specification, processor refers to processor in a broad sense, including general-purpose processors (e.g., CPU: Central Processing Unit, etc.) and special-purpose processors (e.g., GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, etc.).

[0055] Furthermore, the actions of the processor can be performed not only by a single processor, but also collaboratively by multiple processors located in physically separate locations. Additionally, the order of the processor's actions is not limited to the order described in this embodiment and can be varied.

[0056] In the processing described below, the processing performed by the image forming apparatus 100 is performed by the CPU 11a provided in the control unit 21 of the image forming apparatus 100. Furthermore, in the processing described below, the processing performed by the server device 200 is performed by the CPU 11a provided in the server device 200 as an example of a processor.

[0057] Furthermore, in the processing described below, the server device 200 performs image diagnosis-related processing for the image diagnostic system 1. This image diagnosis-related processing for the image diagnostic system 1 can be implemented by one server device 200 or by multiple devices.

[0058] like Figure 3 As shown, the image forming apparatus 100 of this embodiment includes a printing unit 100A and a paper output unit 100B. Additionally, the image forming apparatus 100 includes an image reading unit 110 for reading images formed on paper, such as charts or graphs. Furthermore, paper is an example of a recording material.

[0059] The printing unit 100A includes an image forming section 20 that forms an image on paper. Additionally, the printing unit 100A includes a control section 21 that controls various parts of the image forming apparatus 100.

[0060] In addition, the printing unit 100A has an image processing unit 22. The image processing unit 22 performs image processing on the image data sent from the image reading unit 110.

[0061] The image reading unit 110 is a scanner. The image reading unit 110 includes a light source that emits light that illuminates the paper, and a light-receiving unit such as a CCD that receives reflected light from the paper. In this embodiment, image data to be read (described later) is generated based on the reflected light received by the light-receiving unit.

[0062] The image forming unit 20 has six image forming units 30T, 30P, 30Y, 30M, 30C and 30K (hereinafter sometimes simply referred to as "image forming units 30") arranged side by side at a certain interval.

[0063] Each image forming unit 30 has a photosensitive drum 31 that forms an electrostatic latent image while rotating in the direction of arrow A. Furthermore, each image forming unit 30 has a charging roller 32 that charges the surface of the photosensitive drum 31. Additionally, each image forming unit 30 has a developing unit 33 that develops the electrostatic latent image formed on the photosensitive drum 31. Finally, each image forming unit 30 has a drum cleaner 34 that removes toner and other substances from the surface of the photosensitive drum 31.

[0064] In addition, each image forming unit 30 has an exposure device 35 that exposes its respective photosensitive drum 31 using a laser. Furthermore, the exposure of the photosensitive drum 31 by the exposure device 35 is not limited to using a laser. For example, a light source such as an LED (Light Emitting Diode) may be provided for each image forming unit 30, and the light emitted from that light source may be used to expose the photosensitive drum 31.

[0065] Each image forming unit 30 is similarly configured except for the toner stored in the developer 33. Image forming units 30Y, 30M, 30C, and 30K form toner images of yellow (Y), magenta (M), blue-green (C), and black (K), respectively.

[0066] In addition, image forming units 30T and 30P use toners corresponding to the company's standard colors, foaming toners for Braille, fluorescent toners, and toners that enhance gloss to form toner images. In other words, image forming units 30T and 30P use toners of special colors to form toner images.

[0067] In addition, the image forming unit 20 has an intermediate transfer belt 41 on which toner images of various tones formed by the photosensitive drums 31 of each image forming unit 30 are transferred.

[0068] Furthermore, the image forming unit 20 has a primary transfer roller 42, which transfers the toner images of each image forming unit 30 onto the intermediate transfer belt 41 in the primary transfer unit T1.

[0069] In addition, the image forming unit 20 has a secondary transfer roller 43, which transfers the toner images of various colors transferred on the intermediate transfer belt 41 onto the paper in the secondary transfer unit T2.

[0070] Furthermore, the image forming section 20 is equipped with a belt cleaner 45 for removing toner and the like from the surface of the intermediate transfer belt 41, and a fixing device 44 for fixing the image after secondary transfer onto paper.

[0071] The image forming unit 20 performs image forming operations based on control signals from the control unit 21.

[0072] Specifically, in the image forming unit 20, firstly, the image processing unit 22 performs image processing on the image data input from the image reading unit 110, and the image data after image processing is supplied to the exposure device 35.

[0073] Furthermore, for example, in the magenta (M) image forming unit 30M, after the surface of the photosensitive drum 31 is charged by the charging roller 32, the photosensitive drum 31 is irradiated by the exposure device 35 with a laser that has been modulated according to the image data obtained from the image processing unit 22.

[0074] Thus, an electrostatic latent image is formed on the photosensitive drum 31.

[0075] The resulting electrostatic latent image is developed by the developer 33, forming a magenta toner image on the photosensitive drum 31.

[0076] Similarly, toner images of yellow, blue-green, and black are formed in image forming units 30Y, 30C, and 30K, while toner images of special colors are formed in image forming units 30T and 30P.

[0077] The toner images formed in each image forming unit 30 are sequentially electrostatically transferred to the image along the image forming unit 30 by a primary transfer roller 42. Figure 3 The intermediate transfer belt 41 rotates in the direction of arrow B, forming an overlapping toner image on the intermediate transfer belt 41.

[0078] The overlapping toner image formed on the intermediate transfer belt 41 is transferred to the secondary transfer section T2, which is composed of the secondary transfer roller 43 and the support roller 49, as the intermediate transfer belt 41 moves.

[0079] On the other hand, the paper is taken out from the paper storage section (not shown) and then conveyed to the position of the positioning roller 74 via the conveying path.

[0080] When the overlay toner image is conveyed to the secondary transfer section T2, paper is supplied from the positioning roller 74 to the secondary transfer section T2 in accordance with the timing.

[0081] Then, in the secondary transfer section T2, the overlapping toner image is electrostatically transferred onto the paper by the transfer electric field formed between the secondary transfer roller 43 and the support roller 49.

[0082] Afterwards, the paper with the overlaid toner image electrostatically transferred is conveyed to the fixing unit 44.

[0083] In the fixing device 44, the paper with the unfixed toner image is pressurized and heated under the control of the control unit 21 to perform fixing treatment on the toner image on the paper. Then, the paper that has undergone fixing treatment is conveyed to the paper stacking unit (not shown) after passing through the curl correction unit 81 provided in the paper output unit 100B.

[0084] Furthermore, when images are formed on both sides of the paper, the reverse mechanism 82 provided in the paper feeding unit 100B feeds the reversed paper back to the secondary transfer roller 43. As a result, toner images are formed on both sides of the paper.

[0085] In this embodiment, during the image forming apparatus 100 (refer to...) Figure 1 During image diagnosis, the control unit 21 first reads the diagnostic image from the non-volatile memory 11d of the server device 200, for example. The diagnostic image is used by the image forming apparatus 100 (see reference 100). Figure 1 The image is used for diagnosis. Then, the image forming unit 20 is activated to form a diagnostic image on paper based on the diagnostic image. Thus, as... Figure 1 As shown by symbol 1A, the paper on which the diagnostic image is formed is called diagnostic paper.

[0086] When diagnostic papers are generated, such as Figure 1 As shown by reference numeral 1B in the accompanying drawings, the diagnostic paper is placed in the image reading unit 110. Then, the image reading unit 110 reads the diagnostic paper, on which a diagnostic image is formed. This generates a reading image of the diagnostic paper. The reading image of the diagnostic paper is an image used for image diagnosis. Furthermore, in this embodiment, the reading image of the diagnostic paper is sometimes referred to as a "chart image".

[0087] Then, the chart image, which is the read image, is sent to the server device 200 and stored in the server device 200. The server device 200 performs image diagnosis using the image diagnosis system 1 based on the chart image, which is the read image.

[0088] Furthermore, in this embodiment, the maintainer or other user of the image diagnostic system 1 of this embodiment accesses the server device 200 and refers to the diagnostic results of the server device 200 when performing maintenance on the image forming apparatus 100.

[0089] Figure 4 This is a diagram illustrating the functional structure of the server device 200. The server device 200 includes a chart image acquisition unit 201, an image quality defect detection unit 202, an image quality defect classification unit 203, an emphasis display unit 204, and a chart image output unit 205.

[0090] Chart image acquisition unit 201, for example, from Figure 1 The image forming apparatus 100 shown acquires a reading image of a diagnostic paper as a chart image.

[0091] The image quality defect detection unit 202 detects image quality defects in the chart image acquired from the chart image acquisition unit 201. For example, the image quality defect detection unit 202 detects image quality defects by comparing a diagnostic image of the non-volatile memory 11d with the chart image. There are various types of image quality defects that can be detected; for example, the type of image quality defect varies depending on the shape of the defect, such as dots or stripes.

[0092] The image quality defect classification unit 203 classifies the image quality defects detected by the image quality defect detection unit 202 according to their types. Furthermore, the image quality defect classification unit 203 classifies each image quality defect into a specific type based on its characteristics. These characteristics might include, for example, the size of each image quality defect or the period at which each image quality defect occurs. The image quality defect information classified by the image quality defect classification unit 203 is then stored, for example, in non-volatile memory 11d (see reference 11d). Figure 2 )middle.

[0093] Furthermore, the image quality defect classification unit 203 classifies the image quality defects detected by the image quality defect detection unit 202 according to their categories. The category of an image quality defect is the category of the cause of that defect. For example, categories of image quality defects include defects caused by image formation and defects caused by image reading. Details of these defects will be described later. Additionally, for example, a machine learning model that learns information about detected image quality defects can be used to automatically determine the type of image quality defect, the characteristics of each defect, and the category of the defect.

[0094] The emphasis display unit 204 emphasizes the location of each image quality defect as defined by the image quality defect classification unit 203. Furthermore, the emphasis display unit 204 switches the emphasis display of the image quality defect location based on the observed event. The observed event is the condition of image quality defects detected from a graph image; examples include various types of image quality defects detected from graph images, and multiple image quality defects of a single type.

[0095] For example, suppose that multiple types of image quality defects are detected from a chart image. In this case, the emphasis display unit 204 distinguishes one type of image quality defect from the other types and emphasizes it. Alternatively, suppose that multiple types of image quality defects are detected from a chart image. In this case, the emphasis display unit 204 switches between different types of image quality defects and emphasizes them.

[0096] Furthermore, the method of highlighting can also be based on the association between image quality defects and highlighting stored in the non-volatile memory 11d, and varies depending on the characteristics of a type of image quality defect. The association between image quality defects and highlighting is information that links information about each image quality defect to the method of highlighting that defect. For example, the method of highlighting one type of image quality defect can be distinguished from the methods of highlighting other types of image quality defects. Examples of highlighting methods include shapes such as circles or rectangles, or colors used for highlighting.

[0097] The chart image output unit 205 outputs a chart image that highlights image quality defects to the user terminal 300 via the emphasis display unit 204. Alternatively, the chart image output unit 205 outputs a chart image that does not highlight image quality defects. Furthermore, the chart image output unit 205 may also output information related to detected image quality defects along with the chart image.

[0098] Figure 5 This is a flowchart illustrating the processing of server device 200. First, the graph image acquisition unit 201 of server device 200 (see...) Figure 4 From the image forming apparatus 100 (refer to) Figure 1 The image is acquired (step S501). Then, the image quality defect detection unit 202 of the server device 200 (refer to...) Figure 4 Detect image quality defects from the chart image (step S502).

[0099] Next, the image quality defect division unit 203 of the server device 200 (refer to...) Figure 4 The image quality defects detected from the chart image are classified (step S503). Then, the server device 200 determines whether to emphasize the display of image quality defects (step S504). If the image quality defects are emphasized (yes in step S504), the emphasis display unit 204 (refer to) Figure 4 The image quality defects are highlighted (step S505). Then, the chart image output unit 205 outputs the chart image highlighting the image quality defects to the user terminal 300 (see reference). Figure 1 (Step S506). If the image quality defects are not highlighted (No in step S504), the chart image output unit 205 outputs the chart image without highlighting the image quality defects to the user terminal 300 (step S506).

[0100] Figure 6 (a) to (b) represent the diagnostic results output by server device 200. Figure 6 (a1) to (a3) ​​represent the chart images. Figure 6(b) represents a table that records information related to image quality defects. Figure 6 In (a) to (b), it is assumed that d1 and d2 are detected as linear image quality defects in the graph image. Additionally, in Figure 6 In (a) to (b), it is assumed that d3 and d4 are detected as point-like image quality defects in the graph image. Furthermore, although the illustrations are omitted, multiple line-like or point-like defects are detected.

[0101] Figure 6 (a1) indicates that the image quality defects d1 and d2 are not highlighted in the chart image. Figure 6 (a2) indicates that the linear image quality defects d1 and d2 are highlighted in the chart image by emphasizing 500. Figure 6 (a3) represents the line quality defects d1 and d2, where d1 is highlighted by 500 in the chart image.

[0102] exist Figure 6 In table (b), "C" is set to "Chart Image". Furthermore, "C" is a chart image that forms a blue-green (C) image. Additionally, "Stripes" and "Dots" are set as "Details". These "Stripes" and "Dots" represent the types of image quality defects detected. In this embodiment, "Stripes" represent linear image quality defects, and "Dots" represent dot-like image quality defects. Moreover, as a first "emphasis", a button labeled "view" is provided for each type of image quality defect. Furthermore, although illustrations are omitted, Figure 6 Table (b) contains information on multiple point defects and linear defects.

[0103] In addition, Figure 6 In table (b), the locations where image quality defects occur are defined as "103mm", "254mm", "53mm", and "175mm". Furthermore, in this embodiment, the image quality defect occurring at the "103mm" location is designated as d1, and the image quality defect occurring at the "254mm" location is designated as d2. Additionally, the image quality defect occurring at the "53mm" location is designated as d3, and the image quality defect occurring at the "175mm" location is designated as d4.

[0104] Moreover, in Figure 6 In table (b), the bar chart representing the intensity of each image quality defect is set as "Score". The intensity of each image quality defect refers to an indicator based on factors such as the size or density of the defect. Figure 6 In the test, the higher the intensity of the image quality defect, the higher the score. Additionally, as a second "emphasis," each image quality defect has a "view" button.

[0105] In this embodiment, Figure 6 Any one of the charts and images in (a1) to (a3) ​​and Figure 6 Table (b) is output from server device 200 to user terminal 300. Then, the merged information is displayed on user terminal 300 (see reference). Figure 1 The display device 310 (refer to) Figure 1 On. Moreover, Figure 6 The charts (a1) to (a3) ​​can all be switched by user input.

[0106] For example, suppose in the display Figure 6 When the user selects the "view" button (as shown in (a1)) of the chart image, it is the first case of "emphasis". In this case, the image displayed is from... Figure 6 Switch the chart image of (a1) to Figure 6 The image in (a2) is shown. Thus, among various types of image quality defects, linear defects d1 and d2 are distinguished from point defects d3 and d4 and highlighted.

[0107] Additionally, for example, suppose in the display Figure 6 When the user selects the "view" button for d1 as the second "emphasis" when viewing the chart image (a1), the following will be displayed: Figure 6 Switch the chart image of (a1) to Figure 6 The image in diagram (a3). Therefore, d1 is identified from multiple image quality defects of one class, namely linear defects d1 and d2, and highlighted. Furthermore, in the display... Figure 6 When viewing the chart image (a3), you can also switch between different image quality defects to highlight them by selecting the "view" button on d2.

[0108] Figure 7 This is a diagram representing a variation of Example 1 of the diagnostic results output by server device 200. Figure 7 In this example, a chart image is used, which forms images of various colors including cyan (C), yellow (Y), magenta (M), and black (K). The cyan (C), yellow (Y), magenta (M), and black (K) chart images are examples of specific images used to identify image quality defects. Furthermore, each chart image is associated with the image forming apparatus 100 (see reference 100). Figure 1 An example of how an image forms a related image. Figure 7 In this context, we assume that linear defects, namely d5 and d6, are detected in the graph image as defects caused by image formation. Examples of defects caused by image formation include lines, streaks, unevenness, and toner contamination adhering to the paper. Defects caused by image formation may arise from surface defects of the photosensitive drum 31, toner adhesion during paper transport, and toner adhesion during image fixing.

[0109] exist Figure 7 In the chart image, d5 was detected in the blue-green (C) color (C) chart, but not in the yellow (Y), magenta (M), and black (K) chart images. Additionally, d6 was detected in all the blue-green (C), yellow (Y), magenta (M), and black (K) chart images.

[0110] In addition, Figure 7 In the chart images, emphasis display 500 is shown on each of the following colors: cyan (C), yellow (Y), magenta (M), and black (K). In the case of the cyan (C) chart image, emphasis display 500 is shown by enclosing d5 and d6 with rectangles. In the case of the yellow (Y), magenta (M), and black (K) chart images, emphasis display 500 is shown in the same position as in the cyan (C) chart image.

[0111] In this embodiment, server device 200 (refer to...) Figure 1 ) Acquire a specific image for judging image quality defects as a result of the image forming apparatus 100 (refer to Figure 1 The server device 200 then extracts image quality defects from the specific image and displays the defect identification information along with emphasis. For example, the server device 200 (refer to...) generates an image for image diagnosis. Figure 1 The defects present in the image are then identified as defects related to image formation by the image forming apparatus 100. The server apparatus 200 then displays information used to identify defects caused by image formation as identification information.

[0112] exist Figure 7 First, server device 200 (refer to...) Figure 1 Defects d5 and d6 caused by image formation are identified from the defects present in the various colored chart images. Then, server device 200 (refer to...) Figure 1 The system displays chart images of various colors as discrimination information. d5 is detected only in the blue-green (C) chart image, and not in the chart images of other colors. Therefore, it can be determined that d5 is a defect caused by the formation of the blue-green image. On the other hand, it can be determined that d6 generated in the chart images of various colors is a defect caused by the formation of images other than blue-green. In other words, the chart images of various colors are used to determine whether a defect is caused by the formation of a specific color among multiple color images, or by the formation of images other than a specific color.

[0113] In addition, Figure 7The system can display charts of various colors simultaneously, but it can also display charts of a single color individually. Furthermore, it can switch between charts of different colors. For example, a turquoise (C) chart can be displayed alone, or it can be switched to a magenta (M) chart. Additionally, in the variations 2-4 described later, the individual images can be switched.

[0114] In addition, Figure 7 In the middle, it can also be displayed together with various charts and images. Figure 6 Table (b). Furthermore, it can also be found in... Figure 6 Table (b) displays strings as discrimination information. For example, in Figure 6 In the columns of table (b), as "defect category", d5 indicates "defect caused by blue-green image formation", and d6 indicates "defect caused by image formation other than blue-green". Furthermore, in variations 2-4 described later, it can also be shown that... Figure 6 The table of (b) is used to display the string as the discrimination information.

[0115] Figure 8 of (a), Figure 8 (b) is a variation of the diagnostic results output by server device 200, Example 2. Figure 8 (a) represents a chart image with images formed on both sides. Figure 8 (b) represents a chart image with an image formed on one side. Figure 8 In this context, we assume that linear defects, d7 and d8, are detected in the graph image as defects caused by image formation. Furthermore, we will... Figure 8 (a) chart image and Figure 8 The chart image of (b) is also displayed on the user terminal 300 (see reference). Figure 1 ).

[0116] Furthermore, chart images with images formed on one side are sometimes simply referred to as "single-sided printed chart images." And chart images with images formed on both sides are sometimes simply referred to as "double-sided printed chart images." Moreover, single-sided printed chart images and double-sided printed chart images are examples of specific types of images.

[0117] exist Figure 8 In the double-sided printed chart image of (a), both d7 and d8 were detected. Additionally, in Figure 8 In the double-sided printed chart image of (a), 500 is highlighted on d7 and d8. Figure 8 In the single-sided printed chart image of (b), d7 was not detected but d8 was detected. Additionally, in Figure 8 In the single-sided printed chart image of (b), in relation to Figure 8 The double-sided printed chart image in (a) is highlighted in the same position, with 500.

[0118] In this embodiment, server device 200 (refer to...) Figure 1 The information displayed is used to identify defects caused by the secondary transfer roller 43, which transfers an image formed by multiple colors onto the paper. For example, in... Figure 8 In this context, as a distinguishing information, both double-sided and single-sided printed chart images are displayed. A d7 value is generated on the double-sided printed chart image, but not on the single-sided printed chart image. Here, on the secondary transfer roller 43 (refer to...) Figure 3 In cases of contamination or other issues, defects may occur on the side opposite to the printed surface. During double-sided printing, defects may occur on both sides. Therefore, it can be determined that d7 on double-sided printed chart images is a defect caused by the secondary transfer roller 43, which transfers the image onto the paper along with the chart. Furthermore, it can be determined that d8, which occurs on both double-sided and single-sided printed chart images, is a defect caused by factors other than the secondary transfer roller 43.

[0119] Figure 9 This is a diagram representing a variation of example 3 of the diagnostic results output by server device 200. Figure 9 In, with Figure 7 The various colored charts and images described herein are displayed along with the scanned background image. The scanned background image is, for example, displayed when the paper is not set on the image reading unit 110 (see reference). Figure 1 The image is obtained during the reading process. Figure 9 In this context, assuming d9 is detected by the image forming apparatus 100 (refer to...) Figure 1 Defects caused by reading () are detected, and d10 is detected as a defect caused by reasons other than reading. For example, defects caused by reading include contamination of the original document stage or the reading surface of the sensor, defects in the sensor itself, etc.

[0120] exist Figure 9 In the analysis, d9 was detected in the blue-green (C) chart image, but not in the yellow (Y), magenta (M), and black (K) chart images or the scanned background image. Additionally, d10 was detected in all blue-green (C), yellow (Y), magenta (M), and black (K) chart images and the scanned background image.

[0121] In addition, Figure 9In the diagram, emphasis display 500 is shown on the chart images and scanned background images in cyan (C), yellow (Y), magenta (M), and black (K) colors. In the case of the cyan (C) chart image, emphasis display 500 is shown by enclosing d9 and d10 with rectangles. In the case of the yellow (Y), magenta (M), and black (K) chart images and scanned background images, emphasis display 500 is shown in the same position as in the cyan (C) chart image.

[0122] In this embodiment, information used to determine defects caused by the reading of the image forming apparatus 100 is displayed as determination information. For example, in Figure 9 In this process, as discrimination information, various colored chart images and scanned background images are displayed together on the user terminal 300. D10 is detected in the scanned background image. Therefore, it can be determined that D10 is a defect caused by the reading of the image forming apparatus 100. Furthermore, D9 is not detected in the scanned background image. Therefore, it can be determined that D9 is not a defect caused by the reading of the image forming apparatus 100.

[0123] Figure 10 This is a diagram representing a variation of example 4 of the diagnostic results output by server device 200. Figure 10 In the example, suppose we detect point defects d11 and d12. Each d11 and d12 represents multiple image quality defects. The sizes of the individual defects in d11 vary. The individual defects in d12 are distributed periodically. Figure 10 In the text, multiple d11 values ​​are highlighted by emphasizing 500 and enclosing them in a rectangle. Additionally, in... Figure 10 In the text, the number 500 is emphasized by displaying multiple d12 values ​​surrounded by circles. Thus, in... Figure 10 In this context, it is emphasized that the display method varies depending on the characteristics of each image quality defect.

[0124] <Postscript> (((1)))

[0126] An image diagnostic system, characterized in that,

[0127] The image diagnostic system has one or more processors.

[0128] The one or more processors perform the following processing:

[0129] Acquire images formed by the image forming apparatus that are the subjects of diagnosis for image diagnosis.

[0130] Detecting image quality defects present in the image used for image diagnosis.

[0131] The location of the image quality defect detected by the event switching is highlighted.

[0132] ((2)))The image diagnostic system according to (((1)))is characterized in that,

[0133] The phenomena described are various types of image quality defects.

[0134] Among the various types of image quality defects, one type of image quality defect is distinguished from the other types and highlighted for emphasis.

[0135] ((3)))The image diagnostic system according to (((1))) or (((2))) is characterized in that,

[0136] The method of emphasizing the display varies depending on the characteristics of the image quality defects of that type.

[0137] ((4)))The image diagnostic system according to (((1)))is characterized in that,

[0138] The phenomenon described is a class of multiple image quality defects.

[0139] In the aforementioned multiple image quality defects, each image quality defect is switched to be highlighted.

[0140] ((5)))The image diagnostic system according to (((4)))is characterized in that,

[0141] The method of highlighting the image varies depending on the characteristics of each image quality defect.

[0142] ((6)))The image diagnostic system according to (((5)))is characterized in that,

[0143] The characteristic of each image quality defect is its size.

[0144] ((7)))The image diagnostic system according to (((5)))is characterized in that,

[0145] The characteristic of each image quality defect is the period during which each image quality defect is displayed.

[0146] ((8)))The image diagnostic system according to (((1)))is characterized in that,

[0147] The discrimination information used to identify the image quality defects is acquired as the image for image diagnosis formed by the image forming apparatus.

[0148] The discrimination information is displayed together with the emphasis display.

[0149] (9))The image diagnostic system according to (8)) is characterized in that,

[0150] The discrimination information is used to determine whether the defect is caused by an image of a specific color in an image of multiple colors, or by an image of another color.

[0151] ((10)))The image diagnostic system according to (((8))) or (((9))) is characterized in that,

[0152] The discrimination information is single-sided printed images and double-sided printed images.

[0153] ((11)))The image diagnostic system according to any one of ((8))) to ((10))) is characterized in that,

[0154] The discrimination information is used to determine defects caused by the reading of the image forming apparatus.

[0155] The system based on (((1))) can reduce the burden on customer engineers in image diagnosis to understand image quality defects.

[0156] According to the system (((2))), users are likely to focus on a type of image quality defect.

[0157] According to the system ((3)), it is possible to uniquely identify each type of image quality defect.

[0158] According to the system (((4))), users are likely to focus on various image quality defects.

[0159] Based on the system ((5)), it is easy to grasp the differences in the characteristics of each image quality defect.

[0160] According to the system ((6)), it can help users grasp the severity of image quality defects.

[0161] According to the system (((7))), users can easily confirm that the image quality defects are caused by the same reason.

[0162] According to the system ((8)), users can easily grasp the image quality defects associated with a specific image.

[0163] According to the system ((9)), defects caused by the formation of images of specific colors are made clearer to users.

[0164] According to the system (((10))), users can distinguish and identify defects caused by the transfer section.

[0165] According to the system (((11))), users can distinguish and identify defects caused by reading.

Claims

1. An image diagnostic system, characterized in that, The image diagnostic system has one or more processors. The one or more processors perform the following processing: Acquire images formed by the image forming apparatus that are the subjects of diagnosis for image diagnosis. Detecting image quality defects present in the image used for image diagnosis. The location of the image quality defect detected by the event switching is highlighted.

2. The image diagnostic system according to claim 1, wherein, The phenomena described are various types of image quality defects. Among the various types of image quality defects, one type of image quality defect is distinguished from the other types and highlighted for emphasis.

3. The image diagnostic system according to claim 1 or 2, wherein, The method of emphasizing the display varies depending on the characteristics of the image quality defects of that type.

4. The image diagnostic system according to claim 1, wherein, The phenomenon described is a class of multiple image quality defects. In the aforementioned multiple image quality defects, each image quality defect is switched to be highlighted.

5. The image diagnostic system according to claim 4, wherein, The method of highlighting the image varies depending on the characteristics of each image quality defect.

6. The image diagnostic system according to claim 5, wherein, The characteristic of each image quality defect is its size.

7. The image diagnostic system according to claim 5, wherein, The characteristic of each image quality defect is the period during which each image quality defect is displayed.

8. The image diagnostic system according to claim 1, wherein, The discrimination information used to identify the image quality defects is acquired as the image for image diagnosis formed by the image forming apparatus. The discrimination information is displayed together with the emphasis display.

9. The image diagnostic system according to claim 8, wherein, The discrimination information is used to determine whether the defect is caused by an image of a specific color in an image of multiple colors, or by an image of another color.

10. The image diagnostic system according to claim 8 or 9, wherein, The discrimination information is single-sided printed images and double-sided printed images.

11. The image diagnostic system according to any one of claims 8 to 10, wherein, The discrimination information is used to determine defects caused by the reading of the image forming apparatus.

12. An image diagnostic method, characterized in that, Acquire images formed by the image forming apparatus that are the subjects of diagnosis for image diagnosis. Detecting image quality defects present in the image used for image diagnosis. The location of the image quality defect detected by the event switching is highlighted.

Images