JP2017083544A - Image processing device, image processing method, and program - Google Patents

Abstract

To provide an image processing apparatus capable of reducing costs by reducing the response time of a service person when a failure occurs, the use of media and toner, and reducing costs.
An image output unit 115 for outputting an image, an acquisition unit 119 for acquiring image feature information of image data obtained by reading an image output by the image output unit, and an image diagnostic unit 126 are provided. An image processing apparatus is provided. The image diagnosis unit 126 includes an estimation unit that estimates a fault location of the image processing apparatus using the image feature information acquired by the acquisition unit, a determination unit that determines an image condition using a result estimated by the estimation unit, Setting means for setting an image to be output by the image output means based on image conditions.
[Selection] Figure 9

Description

  The present invention relates to an image processing apparatus, an image processing method, and a program.

When an image processing apparatus such as a printer is used under stress for a long time, there is a possibility that an “abnormal image” in which an image different from normal is output due to deterioration or the like. An “abnormal image” generated due to deterioration or the like is difficult to automatically detect using a sensor or the like, and often responds after receiving an indication from the user. However, it is difficult to describe the “abnormal image” in words. For example, even if “streak is present”, the cause cannot be specified unless detailed information such as the color, direction, and size of the streak is known. For this reason, when receiving an indication of “abnormal image” from the user, it is necessary for the service person to go to the site and check the output image including the “abnormal image”. Then, the service part concerned is identified by predicting the failure point, and once returning to the service base, the service part is obtained, and then the user is revisited to take action. Such an exchange not only costs the serviceman to move, but also makes the machine unusable until the response is completed, resulting in downtime and greatly reducing user productivity.
Therefore, Patent Document 1 discloses an “image diagnosis” technique in which an image is output by a printer, a scanned image is acquired, a feature amount is calculated, and a fault location is diagnosed using an inference engine. Further, Patent Document 2 outputs an image for gradation confirmation with a printer, measures the patch density in the image, and when the measured patch density exceeds the threshold value, only the image with the patch density that exceeds the threshold value. An output tone correction technique is disclosed.

Japanese Patent No. 4687614 JP 2008-185863 A

In Patent Document 1, a service person who performs “confirmation of correspondence” needs to output an image of the same type as the image used in the image diagnosis again with the printer, acquire the scanned image, and calculate the feature amount again. There is. However, for example, when dealing with an abnormal image that appears prominently in a low-density region of a specific color, the image output at the time of “correspondence confirmation” does not require output of an image other than the specific color, but no abnormal image occurs. The entire image including the image is output.
Further, for example, an abnormal image that appears prominently in a low-density region of a specific color may occur in a high-density region. When this phenomenon is confirmed, even if the patch density of the image in the high density area exceeds the threshold value, only the image in the low density area of the specific color needs to be output. However, in Patent Document 2, since all images having patch densities exceeding the threshold value are output, an image of an unnecessary high density region is output.
If an image other than an image in which an abnormal image is remarkably displayed is output at the time of confirming the correspondence, the cost increases due to the handling time of the service person and the use of toner and media more than necessary.

  SUMMARY OF THE INVENTION An object of the present invention is to provide an image processing apparatus capable of reducing the response time of a service person when a failure occurs, the use of media and toner, and the cost can be suppressed.

  The image processing apparatus of the present invention includes an image output unit that outputs an image, an acquisition unit that acquires image feature information of image data obtained by reading the image output by the image output unit, and the acquisition unit. Estimating means for estimating a fault location of the image processing apparatus using the acquired image feature information, determining means for determining an image condition using the result estimated by the estimating means, and the image based on the image condition Setting means for setting an image to be output by the output means.

  According to the image processing apparatus of the present invention, it is possible to reduce the time required for a serviceman when a failure occurs, the use of media and toner, and the cost.

It is a block diagram of an image diagnostic system. It is a figure showing a flow of image formation processing. It is the figure which showed the flow of the image diagnostic process. It is the figure which showed the example of the chart for image diagnosis, and the chart for correspondence confirmation. It is the figure which showed the table of the patch structure information of the chart for a diagnosis. It is the figure which showed the table which described the phenomenon, the corresponding content, the image feature, etc. It is the figure which showed the flow of the failure location estimation process. It is a figure showing an example of UI displayed at the time of image diagnosis and image quality confirmation. It is the figure which showed the flow of the correspondence confirmation process. It is the figure which showed the table of the patch structure information of the chart for correspondence confirmation. It is the figure which showed the flow of the image selection process. It is the figure which showed the table which described the image generation information of Example 2. FIG. FIG. 10 is a diagram illustrating a flow of image generation processing according to the second embodiment. FIG. 10 is a diagram illustrating an example of a UI displayed when confirming the correspondence content of the third embodiment. It is the figure which showed the flow of the correspondence confirmation process of Example 3. FIG.

Example 1
In the first embodiment, a method for diagnosing a fault location by image diagnosis and selecting an image to be output by confirmation of correspondence from the characteristics of the diagnosis result will be described.
FIG. 1 is a configuration diagram of a system in the present embodiment. An MFP (Multi Function Printer) 101 that uses cyan, magenta, yellow, and black (hereinafter, C, M, Y, and K) toners is connected to other network-compatible devices via a network 123. The PC 124 is connected to the MFP 101 via the network 123. A printer driver 125 in the PC 124 transmits print data and the like to the MFP 101.

The MFP 101 will be described in detail. The network I / F 122 receives print data and the like. The controller 102 includes a CPU 103, a renderer 112, and an image processing unit 114. The interpreter 104 of the CPU 103 interprets the PDL (page description language) portion of the received print data and generates intermediate language data 105.
The CMS 106 performs color conversion using the source profile 107 and the destination profile 108 to generate intermediate language data (after CMS) 111. Here, CMS is an abbreviation for Color Management System, and color conversion is performed using profile information, which will be described later. The source profile 107 converts a device-dependent color space such as RGB or CMYK into a device-independent color space such as L ^* a ^* b ^* (hereinafter referred to as Lab) or XYZ determined by the CIE (International Lighting Commission). It is a profile to do. XYZ is a device-independent color space like Lab, and expresses colors with three types of stimulus values. The destination profile 108 is a profile for converting a device-independent color space into a CMYK color space depending on the device (printer 115).

  The CMS 109 performs color conversion using the device link profile 110 and generates intermediate language data (after CMS) 111. Here, the device link profile 110 is a profile for directly converting a device-dependent color space such as RGB or CMYK into a CMYK color space depending on the device (printer 115). Which CMS is selected depends on the setting in the printer driver 125. In this embodiment, the CMSs (106 and 109) are divided according to the types of profiles (107, 108 and 110), but a plurality of types of profiles may be handled by one CMS. The type of profile is not limited to the example given in this embodiment, and any type of profile may be used as long as the device-dependent CMYK color space of the printer 115 is used.

The renderer 112 generates a raster image 113 from the generated intermediate language data (after CMS) 111. The image processing unit 114 performs image processing on the raster image 113 and the image read by the scanner 119. Details of the image processing unit 114 will be described later.
The image diagnosis unit 126 performs an image diagnosis process for estimating a fault location by outputting a chart and executing an analysis process when an abnormal image occurs. Details of the processing will be described later.

A printer 115 (image output means) connected to the controller 102 is a printer that forms output data on paper using colored toners such as C, M, Y, and K. The printer 115 is controlled by the CPU 127 and includes a paper feeding unit 116 that feeds paper and a paper discharge unit 117 that discharges paper on which output data is formed.
The display device 118 is a UI (user interface) that displays instructions to the user and the state of the MFP 101. In addition to copying, transmission processing, etc., it is used in image diagnostic processing described later. The scanner 119 is a scanner including an auto document feeder. The scanner 119 irradiates a bundle or one original image with a light source (not shown), and forms an original reflection image on a solid-state image sensor such as a CCD (Charge Coupled Device) sensor via a lens. Then, a raster-like image reading signal is obtained as image data from the solid-state imaging device. The input device 120 is an interface for receiving input from a user. Since some input devices are touch panels, they are integrated with the display device 118. The storage device 121 stores data processed by the controller 102, data received by the controller 102, and the like.

FIG. 2 shows a flow of image processing performed by the image processing unit 114 on the raster image 113 and the image read by the scanner 119. A processing flow of the image processing unit 114 will be described with reference to FIG.
2 is realized by executing an ASIC (Application Specific Integrated Circuit) (not shown) in the image processing unit 114.

In step S 201, it is determined whether the received image data is scan data read by the scanner 119 or a raster image 113 sent from the printer driver 125. If it is not scan data, it is a raster image 113 that has been bitmap-developed by the renderer 112. Therefore, the process proceeds to step S211 as the CMYK image 210 converted to CMYK depending on the printer device by the CMS, and the subsequent processing is performed. In the case of scan data, since the scan data is an RGB image 202, color conversion processing is performed in step S203 to generate a common RGB image 204. Here, the common RGB image 204 is defined in a device-independent RGB color space, and can be converted into a device-independent color space such as Lab by calculation.
In parallel, character determination processing is performed in step S205 to generate character determination data 206. Here, the character determination data 206 is generated by detecting the edge of the image.

In step S207, the common RGB image 204 is filtered. Here, the character determination data 206 is used to perform different filter processing for the character portion and other portions. In step S208, background removal processing is performed to remove ground color components.
In step S209, color conversion processing is performed to generate a CMYK image 210. In step S211, the gradation characteristics of each single color of C, M, Y, and K are corrected using the 1D-LUT. The 1D-LUT is a one-dimensional LUT (Look Up Table) that corrects each color of C, M, Y, and K.
Finally, in step S212, the image processing unit 114 performs halftone processing such as screen processing and error diffusion processing to create a CMYK image (binary) 213.

<About diagnostic imaging processing>
FIG. 3 is a diagram showing the flow of the image diagnosis process.
The image diagnosis process is a process executed by a user or the like when an abnormal image occurs, and is controlled by the image diagnosis unit 126. Of the following processing flow, the processing from step S301 to step S309 is implemented by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. In addition, the display device 118 displays an instruction to the user on the UI, and receives the user's instruction from the input device 120.

In step S301, diagnostic chart data 302 for image diagnosis is acquired. As an example, the diagnostic chart data 302 will be described with reference to FIGS.
A chart 401 in FIG. 4A is a chart for detecting a streak image (hereinafter referred to as a vertical streak) with respect to the sheet conveyance direction. The main causes of vertical streaks include uneven developer coating in the developing portion, charging failure in the charging portion, deformation of the transfer belt, and poor cleaning of the transfer belt and drum.
The patch 402, the patch 403, the patch 404, and the patch 405 are based on the number of lines or density area of the object determined in advance based on the chart configuration information as shown in FIG. 5 for any color of C, M, Y, and K. It is configured. For example, when diagnosing a vertical stripe of C, the patch 402 becomes a low density signal value at C, and is a patch composed of a screen with a low number of lines in the halftone process of step S212. Similarly, the patch 403 is a screen with a low line number at C and a high line number, the patch 404 is a screen with a high signal value at C and a low line number, and the patch 405 is a line with a high signal value at C and a high line number. A number of screens and patches 406 are composed of paper white areas on which nothing is printed.

  In the present embodiment, the chart composed of C has been described, but a chart composed of other single colors M, Y, and K is also output. Further, although the configuration using a single color has been described, a red (R) patch composed of M and Y, or a green (G) or blue (B) patch may be used. Further, in order to diagnose vertical stripes in the direction perpendicular to the sheet conveyance direction, the signal values from the patches 402 to 405 and the screen processing are made the same to form a uniform chart in the plane. A chart may be output for each density region and halftone process. In this embodiment, the low density signal value is 15% and the high density signal area is 80%. However, it may be changed depending on the model.

In step S303, the chart data acquired in step S301 is output by a printer to obtain an output chart 304 that is a diagnostic image. In step S305, the output chart 304 is read by the scanner 119, and diagnostic image data 306 is acquired.
In step S307, analysis processing is executed on the diagnostic image data 306 using the diagnostic analysis parameter 308 to calculate an image feature amount 310 that is image feature information. The diagnostic analysis parameter 308 in this embodiment describes the analysis threshold in the image, the determination threshold value when calculating the feature amount, etc. in order to analyze the read diagnostic image data 306. . It is assumed that the diagnostic analysis parameter 308 may be switched according to the color of the read diagnostic image data 306, the density range described in FIG.

  As an example of a method for analyzing whether vertical stripes are generated for each patch, first, an average luminance value is calculated with respect to the sheet conveyance direction, and an average luminance value signal in a direction perpendicular to the sheet conveyance direction is calculated. When the luminance average value signal exceeds the threshold value of the diagnostic analysis parameter 308, it is determined as a streaked region, and the streak position, streak density, the difference value from the average value, and the streak width are set as the image feature amount. There is a method of calculating as 310. In this embodiment, when the density of the region where the vertical stripe is generated is higher than the average value signal, the black stripe is used, and when the density is low, the white stripe is used. Although the vertical streak is analyzed by the above method, the present invention is not limited to this method, and any method may be used as long as it can analyze the vertical streak and calculate the feature amount.

  Next, in step S309, the failure location is estimated using the image feature quantity 310 calculated in step S307 and the failure location estimation database (DB) 311, and the failure location estimation result 312 is determined. An example of the failure location estimation DB 311 in the present embodiment will be described with reference to FIG. FIG. 6 shows, for each phenomenon in which vertical streaks occur, information on the corresponding contents for solving the phenomenon, streak density, color in which streaks occur, density range in which streaks are likely to occur, and the number of screen lines in which streaks are likely to occur. It is described as a table.

The poorly charged white streaks are those in which the external additive of the toner adheres to the charging member, whereby the resistance of the charging member increases only in that region, and as a result, the high resistance region appears as a white streak. In particular, vertical stripes are likely to occur in a low density region or a region with a high screen line number. This can be solved by exchanging the process cartridge of the color in which streaks are generated.
The development-defect white streaks appear as white streaks as a result of the clogging of the dust and the hair with the developing sleeve of the developing device that hinders the flow of the developer. Since the density does not exceed the predetermined density, the area where streaks are likely to occur is a high density area. This can be solved by exchanging the process cartridge of the color in which streaks are generated.
The defective drum cleaning streak is a black streak caused by the fact that a part of the drum cleaner is missing on the photosensitive drum and the toner after the primary transfer cannot be scraped off on the drum. Black streaks of a color where the tram cleaner is missing from the blank area are generated. This can be solved by exchanging the process cartridge of the color in which streaks are generated.
The transfer belt cleaning failure is caused by black streaks due to a part of the transfer cleaner being chipped and the toner after secondary transfer not being scraped off on the intermediate transfer belt. Black streaks in which all the colors C, M, Y, and K are mixed with respect to the blank paper area are generated. This can be solved by replacing the transfer belt cleaner.
The transfer belt deformed white streaks are caused by plastic deformation of the intermediate transfer belt into a convex shape after a long period of use. Since a toner image is not formed on the paper in the deformed area, white streaks are generated. In particular, all the colors C, M, Y, and K appear as white stripes in the low density region. This can be solved by replacing the transfer unit.

  Details of the failure location estimation processing in step S309 will be described with reference to FIG. The failure location estimation process estimates the failure location based on the feature of the abnormal image described in the failure location estimation DB 311 and is controlled by the image diagnosis unit 126. Of the following processing flow, the processing from step S701 to step S716 is implemented by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121.

In step S701, the image feature amount 310 calculated in step S307 is acquired.
Next, in step S702, it is determined whether the feature amount acquired in step S701 has a streak. If there is no streak, it is determined in step S703 that there is no streak. On the other hand, if it is determined that there is a streak in the feature amount acquired in step S701, a color in which a streak is generated at the same position in the feature amount is extracted in step S704.

  In step S705, it is determined whether black streaks have occurred in the paper white area. If streaks have occurred, it is determined in step S706 whether black streaks have occurred in a specific color. If it is determined in step S706 that black streaks are generated in a specific color, it is determined in step S707 that the drum cleaning failure of the specific color is present. On the other hand, if it is determined in step S706 that black streaks are generated as mixed colors, it is determined in step S708 that the transfer belt cleaning is defective. If it is determined in step S705 that no black stripe has occurred, it is determined in step S709 whether a white stripe has occurred in a specific color. If it is determined in step S705 that the white streak is not a specific color but a mixed color streak, it is determined in step S710 that the transfer belt is a deformed white streak. On the other hand, if it is determined in step S709 that the white stripe is generated in a specific color, the process proceeds to step S711.

In step S711, a difference value between the average value in the high density region and the low density region is compared. If the difference value of the high density region is smaller in step S711, it is determined as a defective charging white stripe in step S712. On the other hand, if the difference value of the high density region becomes larger in step S711, the process proceeds to step S713.
In step S713, a difference value between the average value in the low line number region and the high line number region is compared. If the low density region is smaller in step S713, it is determined as a defective charging white stripe in step S712. If it is determined in step S713 that the low density area is larger, it is determined in step S714 that the development coat has a defective white stripe.

In step S715, the determined result is stored in failure location estimation result 312. In step S716, it is confirmed whether or not the streak occurrence position is determined for all colors. If it is confirmed in step S716 that the occurrence positions of all the streaks have not been determined, in step S704, the failure location is estimated at the occurrence position of the next streak until the occurrence positions of all the streaks are determined. On the other hand, if it is confirmed in step S716 that the occurrence positions of all the stripes have been determined, the process ends. Depending on the analyzed feature quantity, there may be a plurality of failure locations. When a plurality of failure locations are estimated, a plurality of diagnosis results are stored.
In this embodiment, the failure location is estimated from the characteristics of the phenomenon in which vertical stripes occur. However, classification using the Bayesian network or machine learning is performed from the feature amount, and the failure location is estimated from the classification result. A method or the like may be used.

  Next, in step S313, the result of the failure location estimation process in step S309 is displayed on the display device 118. FIG. 8A shows an example of a UI that displays a failure location estimation result in this embodiment. A UI 801 shows an example of an abnormal image determination result. Here, as understood by the user, the message, the information encoded in such a way that the service and the like can be quantitatively determined, and the exchange unit are displayed as specific words. If it is determined in step S309 that there is no abnormal image, a content that there is no problem with the printer itself is displayed. As described above, since the contents of the abnormal image can be understood from the specific / quantitative information, it is possible to reduce the load corresponding to the abnormal image and the response time.

<About confirmation processing>
FIG. 9 is a diagram showing the flow of the correspondence confirmation process. The correspondence confirmation process is a process executed by a service person when confirming whether the abnormal image has been resolved, and is controlled by the image diagnosis unit 126. Of the following processing flow, the processing from step S <b> 901 to step S <b> 917 is realized by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. In addition, the display device 118 displays an instruction to the user on the UI, and receives the user's instruction from the input device 120.

When the service person starts the correspondence confirmation process, the image diagnosis unit 126 acquires the failure location estimation result 312 in step S901.
In step S902, image condition information 903 is acquired. The image condition information 903 in this embodiment extracts, for each phenomenon in which vertical streaks occur in FIG. 6, information on the color in which streaks occur, the density range in which streaks are likely to occur, the number of screen lines in which streaks are likely to occur, and the like. It is described as a table.

In step S904, the correspondence confirmation image database 905 is acquired. Details of the correspondence confirmation image database 905 in the present embodiment will be described with reference to FIGS.
FIG. 10 shows an example of a correspondence confirmation image database, in which for each chart number, a chart to be used, a color to be used, a color to be used, a patch density range, and information on halftone processing to be performed on the patch are described as a table. is there. From the failure location estimation result, a chart in which the characteristics of the phenomenon in which vertical stripes occur and the characteristics described in the table match is selected as a chart to be used when checking the correspondence.

  A chart 407 in FIG. 4B is a chart for confirming a streak image with respect to the sheet conveyance direction as one of the correspondence confirmation image charts. The patch 408 is configured based on any one of C, M, Y, and K selected in the chart configuration information of FIG. 6 and the number of lines and density range of the object. When checking using all the colors, the chart 401 in FIG. 4A is used. When checking the correspondence using a specific color, the chart 407 in FIG. 4B is used. When checking using all colors, the patch 402 is C, the patch 403 is M, the patch 404 is Y, the patch 405 is K, and the patch 406 is white. By selecting the correspondence confirmation chart data from the result of the failure estimation, it is not necessary to output an unnecessary chart.

In step S906, using the failure location estimation result 312, the image condition information 903, and the correspondence confirmation image database 905, the correspondence confirmation chart data 908 output in the correspondence confirmation and the correspondence confirmation analysis parameter 907 used for the analysis are stored. Perform image selection processing. Details of the image selection process will be described later.
In step S909, the correspondence confirmation chart selected in step S906 is output by the printer, and an output chart 910 is obtained. In step S911, the output chart 910 is read by the scanner 119, and the correspondence confirmation image data 912 is acquired.
In step S <b> 913, analysis processing is performed on the correspondence confirmation image data 912 using the correspondence confirmation analysis parameter 907 to calculate an image feature amount 914. At this time, the correspondence confirmation analysis parameter 907 is a parameter that can be analyzed in the correspondence confirmation chart selected in step S906, and may be different from the analysis parameter 308 for diagnosis.

  In step S915, it is determined whether the vertical streak has been eliminated by the image feature amount 914 calculated by image analysis. If it is determined that no vertical stripe has occurred, that is, the vertical stripe has been eliminated, in step S916, the fact that the correspondence has been completed is displayed on the display device 118, and the correspondence confirmation process is terminated. On the other hand, if it is determined in step S915 that vertical streaks have occurred, that is, the image abnormality has not been resolved, the display device 118 displays that the abnormal image has not been improved in step S917, and Notify service personnel of the contents. An example of the UI is shown in FIG. UI 802 shows an example of displaying that an abnormal image is not improved. Here, information and exchange units coded so that messages, services, and the like can be quantitatively determined are displayed as specific words so that the service person can understand.

<Image selection process when checking compatibility>
Details of the image selection processing for selecting the correspondence confirmation image in S906 will be described with reference to FIGS. The image selection process is a process of selecting an image to be used for checking the correspondence using the failure location estimation result 312, the image condition information 903, and the correspondence checking image database 905, and is controlled by the image diagnostic unit 126. Of the following processing flow, the processing from step S <b> 1101 to step S <b> 1114 is realized by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121.

In step S1101, it is determined from the failure location estimation result 312 whether or not the colors causing the vertical stripes are all colors. If it is determined that all the colors are generated, a chart pattern of all colors is set in step S1102. In this case, any one of charts No. 1 to No. 4 in FIG. 10 is set. If it is determined in step S1101 that it does not occur for all colors, the process proceeds to step S1103.
In step S1103, it is determined from the failure location estimation result 312 whether vertical stripes occur in the white paper area. If it is determined in step S1103 that vertical stripes occur in the white area, chart No. 21 in FIG. 10 is set, and the density area and halftone pattern are not set. If it is determined in step S1103 that the white area does not occur, a chart pattern of a specific color in which vertical stripes are generated is set in step S1105. In this case, any one of charts Nos. 5 to 20 in FIG. 10 is set.

  In step S1106, it is determined from the failure location estimation result 312 whether vertical streaks occur in the high density area. If it is determined that the vertical stripes occur in the high density area, a chart pattern that becomes a patch in the high density area is determined in step S1107. Set. If it is determined in step S1106 that the image does not occur in the high density region, a chart pattern that becomes a low density region patch is set in step S1108.

In step S1109, it is determined from the result of the failure location estimation whether the screen line number where the vertical stripe is generated is a high line number region. If it is determined that the vertical stripe occurs in the high line number region, a chart pattern in which the object attribute is the high line number is set in step S1110. On the other hand, if it is determined in step S1109 that no vertical stripe occurs in the high line number region, a chart pattern in which the object attribute is a low line number is set in step S1111.
In step S1112, a chart pattern to be output is determined from the chart patterns set in steps S1101 to S1111 and output as correspondence confirmation chart data 908. As an example of this embodiment, when a poorly charged white streak occurs in cyan, an image having the characteristics of chart No. 6 in FIG. 10 is determined as the correspondence confirmation chart data 908.

In step S1113, the image analysis parameters corresponding to the chart pattern determined in step S1112 are set, and the correspondence confirmation analysis parameters 907 are stored. As a result, the image analysis in step S913 can be accurately performed on the chart pattern determined in step S1112.
In step S1114, it is confirmed whether or not all the correspondence contents have been processed. If not, all the processing from step S1101 is repeated until the chart pattern is determined from all the fault location results. If it can be confirmed in step S1114 that all the corresponding contents have been processed, the image selection process ends.

  In this example, although vertical streaks occurred, image analysis was performed, and a failure location was diagnosed from the analyzed feature amount. Then, based on the characteristics of the abnormal image diagnosed at the time of confirming the correspondence, an image that makes it easy to confirm whether the abnormal image has been resolved is selected as the correspondence confirmation image. The present invention is not limited to this embodiment, and any abnormal image other than vertical stripes may be used as long as it can select a correspondence confirmation image using characteristics of a phenomenon in which the abnormal image occurs.

  As described above, since the image for confirming the correspondence is output according to the characteristics of the phenomenon in the occurrence of the diagnosed abnormal image, it is possible to output only the necessary image. As a result, it is possible to reduce costs associated with service personnel's response time and use of excessive toner and media.

(Example 2)
In the first embodiment, the method of reducing the image by selecting the image to be output at the time of checking the correspondence according to the characteristic of the phenomenon of the diagnosed abnormal image has been described. In the second embodiment, an example in which the number of charts can be reduced even when a plurality of abnormal images are generated by generating and outputting a confirmation image from the characteristics of the abnormal image will be described.
Since the flowcharts of the image diagnosis process and the correspondence confirmation process are the same as those in the first embodiment, description thereof is omitted. In the present embodiment, the image selection process in step S906 in FIG.

  The image selection process in step S906 in the present embodiment will be described with reference to FIGS. The image selection process according to the present embodiment is a process for generating an image used for checking the correspondence using the failure location estimation result 312 and the image condition information 903, and is controlled by the image diagnosis unit 126. Of the following processing flow, the processing from step S1301 to step S1317 is implemented by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121.

Steps S1301 to S1311 are the same as steps S1101 to S1111 described with reference to FIG.
In step S1312, the chart pattern information set up to step S1311 is set and saved as image generation information 1313. The image generation information 1313 in the present embodiment will be described with reference to FIG.
The image generation information 1313 is a table that describes image information for generating a correspondence confirmation image. The chart configuration of the image for confirmation of correspondence is as shown in FIG. 4A. For each patch, a phenomenon in which vertical streaks occur, a color in which streaks occur, a density range in which streaks are likely to occur, and streaks are likely to occur. Information about halftone processing is described as a table. In the case of a phenomenon that occurs in all colors, a plurality of chart areas are used.

  In the present embodiment, a description will be given of a description method when “charging defective white streaks” and “transfer belt deformation white streaks” occur simultaneously. Since it is sufficient to confirm only the color where the vertical streak has occurred for “badly charged white streaks”, image information is set using only the patch 402. Since “transfer belt deformation white streaks” generate vertical streaks in the low density region of all colors, image information is set in four patches. At this time, C is set for the patch 403, M is set for the 404, Y is set for the 405, and K is set for the 406. When there are a plurality of vertical streaking phenomena and the setting cannot be made with one chart, the image generation information 1313 for the second page is created.

In step S1314, it is determined whether all correspondence contents have been processed. If it is determined that not all processing has been performed, the processing is repeated from step S1301 until chart patterns are determined from all failure location results. On the other hand, if it is determined in step S1314 that all correspondence contents have been processed, the process advances to step S1315 to integrate the image generation information 1313 set in step S1312, and generate correspondence confirmation chart data 908.
In step S1317, the image analysis parameters corresponding to the chart pattern generated in step S1315 are set, and the correspondence confirmation analysis parameters 907 are stored. Thus, the correspondence confirmation chart data 908 generated in step S1315 can be analyzed with high accuracy by the image analysis in step S913.

  In Example 2, image analysis was performed to determine whether vertical streaks occurred, and a failure location was diagnosed from the analyzed feature amount. Then, based on the characteristics of the abnormal image diagnosed at the time of checking the correspondence, an image that makes it easy to confirm whether the abnormal image has been resolved is generated. The present invention is not limited to this embodiment, and any method can be used as long as it can generate an image for confirmation of correspondence using characteristics of a phenomenon in which an abnormal image occurs even with an abnormal image other than vertical stripes.

  As described above, by generating the confirmation image from the characteristics of the abnormal image, it is possible to reduce the number of output images when confirming the correspondence even when a plurality of abnormal images are generated.

(Example 3)
In the second embodiment, a method has been described in which a confirmation image is generated from the characteristics of an abnormal image to reduce the number of output images at the time of checking the correspondence even when a plurality of abnormal images are generated. In the third embodiment, an example will be described in which a serviceman can select a confirmation image based on the characteristics of the contents corresponding to the failure.
Since the flowchart of the image selection process is the same as that of the first embodiment, description thereof is omitted. In the present embodiment, the correspondence confirmation process in FIG. 3 is different from that in the first embodiment.

  The correspondence confirmation process in the third embodiment will be described with reference to FIGS. 14 and 15. The correspondence confirmation process is a process executed by a service person when confirming whether the abnormal image has been resolved after parts replacement or the like, and is controlled by the image diagnosis unit 126. Of the following processing flow, the processing from step S <b> 901 to step S <b> 917 is realized by the CPU 103 in the controller 102, and the acquired data is stored in the storage device 121. In addition, the display device 118 displays an instruction to the user on the UI, and receives the user's instruction from the input device 120.

  First, in step S1501, a screen for inputting parts exchanged correspondingly by the service person is displayed on the display device 118, and the service person inputs the contents of the exchange. An example of the UI displayed at this time is shown in FIG. A UI 1401 shows an example of a UI for inputting correspondence contents. The list box 1402 is a list of contents corresponding to the service person. When the service person selects the corresponding content from the list box 1402 and presses the button 1403, the selected content is stored in the storage device 121. The contents of the list are “C process cartridge replacement”, “M process cartridge replacement”, “Y process cartridge replacement”, “K process cartridge replacement”, and “transfer belt cleaner replacement”. , “Replacement of transfer unit” and the like. As a result, it is possible to input specifically exchanged contents.

  In step S1502, image condition information 1503 is acquired. Details of the image condition information 1503 will be described with reference to FIG. The image condition information 1503 according to the third embodiment is obtained by extracting information on the density range where lines are likely to be generated and the number of lines where lines are likely to be generated for each corresponding content in FIG. If the replacement of the process cartridge is selected in the corresponding content input in step S1501, the replaced color is set as the generated color. In addition, when replacement of the transfer belt cleaner or replacement of the transfer unit is selected, all colors are set as generated colors.

  Since step S1504 is the same as the process of step S904 of the first embodiment, description thereof is omitted. Step S1506 is the same as the image selection processing in S906 of the second embodiment, and a description thereof will be omitted. By using the image selection process of the second embodiment, it is possible to generate the correspondence confirmation chart data 1509 so as to fit on one page even when there are a plurality of features such as process cartridge replacement. Steps S1508 to S1517 are the same as steps S909 to S916 in the first embodiment, and thus description thereof is omitted.

  In the third embodiment, an image that allows easy confirmation of whether or not an abnormal image has been resolved is selected based on the characteristics of the content that the serviceman supports. Any method may be used as long as it is a method that can select an image for confirmation of correspondence using the feature of the corresponding content, not only the abnormal image of the vertical stripe described in the third embodiment, but also an abnormal image other than the vertical stripe. Further, there may be a plurality of selections of the corresponding contents in step S1501.

  As described above, it is possible to select the confirmation chart from the contents corresponding to the service person. As a result, an image for confirming the correspondence according to the characteristics of the phenomenon in the occurrence of the diagnosed abnormal image is output, so that only a necessary image can be output. As a result, it is possible to reduce costs associated with service personnel's response time and use of excessive toner and media.

(Other examples)
The present invention supplies 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 one or more processors in a computer of the system or apparatus read and execute the program This process can be realized. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

  As mentioned above, although the preferable Example of this invention was described, this invention is not limited to these Examples, A various deformation | transformation and change are possible within the range of the summary.

Claims (12)

Image output means for outputting an image;
Obtaining means for obtaining image feature information of image data obtained by reading the image output by the image output means;
Using the image feature information acquired by the acquisition means, an estimation means for estimating a failure location of the image processing apparatus;
Determining means for determining an image condition using the result estimated by the estimating means;
Setting means for setting an image to be output by the image output means based on the image condition;
An image processing apparatus comprising: The image processing apparatus further includes confirmation means for confirming whether the abnormality has been solved by using the image and the analysis parameter set by the setting means,
The image processing apparatus according to claim 1, wherein the setting unit sets the analysis parameter corresponding to the image to be output based on the image condition.   The image processing apparatus according to claim 1, wherein the image feature information is at least one of information on color of an image, information on density, and information on the number of screen lines. .   The determination means uses, for each abnormal image phenomenon, a table in which at least one of the color at which the phenomenon occurs, the density at which the phenomenon easily occurs, and the number of screen lines at which the phenomenon easily occurs is associated in advance. The image processing apparatus according to claim 1, wherein a condition is determined.   The setting means should output the image to be set using a table in which at least one of the color at which the phenomenon occurs, the density at which the phenomenon occurs, and the number of screen lines at which the phenomenon occurs easily corresponds. The image processing apparatus according to claim 1, wherein an image is set.   The setting means sets the color of the patch to a specific color or white or all colors based on the color where the phenomenon occurs, and either high density or low density based on the density where the phenomenon occurs Set the image to be output by setting the signal value of the crab patch and setting the attribute of the patch object to either the high line number or the low line number based on the number of screen lines where the phenomenon occurs The image processing apparatus according to claim 1, wherein the image processing apparatus is an image processing apparatus.   The image processing apparatus according to claim 1, further comprising an integration unit that integrates a plurality of types of images set by the setting unit into a single sheet. Processing equipment.   4. The image processing according to claim 1, wherein the determination unit determines the image condition using information on a replaced part based on the estimated result. 5. apparatus.   The determining means uses, for each replaced part, a table in which at least one of the color at which the phenomenon occurs, the density at which the phenomenon is likely to occur, and the number of screen lines at which the phenomenon is likely to occur is previously associated, The image processing apparatus according to claim 8, wherein an image condition is determined.   The image processing apparatus according to claim 2, further comprising a display unit configured to display a result estimated by the estimation unit or a result confirmed by the confirmation unit. A control method for an image processing apparatus, comprising:
Outputting a diagnostic image; and
Obtaining image feature information of diagnostic image data obtained by reading the diagnostic image;
Using the image feature information of the diagnostic image data, estimating a fault location of the image processing apparatus;
Determining image conditions using the estimated results;
Setting a confirmation image based on the image condition;
Outputting the confirmation image;
A step of confirming whether the abnormality has been resolved for the confirmation image;
A control method for an image processing apparatus, comprising:   A program for causing a computer to execute the control method according to claim 11.