Information processing system, information processing method, and computer readable medium

Abstract

The invention discloses an information processing system, an information processing method, and a computer readable medium, the information processing system includes one or a plurality of processors that cause a storage device to hold a weight value for each evaluation item related to image quality, acquire image data of an evaluation object, and store the acquired image data in the storage device. And an evaluation unit that evaluates the image data for each of the evaluation items on the basis of the evaluation criteria for each of the evaluation items, and evaluates the image quality of the image related to the image data on the basis of the evaluation results in the evaluation for each of the evaluation items and the weight values for the evaluation items held by the storage device.

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method and a computer readable medium. Background Art

[0002] Determining an evaluation criterion and evaluating the quality of an image is being promoted. The following technology is disclosed in Japanese Patent Application Laid-Open No. 10-63859: learning the relationship between the characteristics of a partial image at a position on an image that a subject pays attention to when determining a comprehensive image quality score and the image quality evaluation value calculated based on the characteristics, and based on the learning result, specifying the position of the partial image to be evaluated that is related to the image quality evaluation item required to determine the comprehensive image quality score for the evaluated image, calculating the image quality evaluation value of the image quality evaluation item required to determine the comprehensive image quality score for the partial image information at the specified position, and calculating the comprehensive image quality score for the evaluated image based on the calculated image quality evaluation value and the learning result. Summary of the invention

[0003] In image diagnosis, image quality is evaluated based on a preset standard for each item. However, the evaluation items that are important to users may differ, and the image quality evaluation may need to reflect the user's preference.

[0004] An object of the present invention is to realize a system capable of evaluating image quality reflecting user preferences, as compared with a configuration that evaluates image quality based on a unified criterion.

[0005] According to a first aspect of the present invention, there is provided an information processing system comprising one or more processors, wherein the processors enable a storage device to maintain a weight value of each evaluation item related to image quality, obtain image data of an evaluation object, evaluate the image data based on an evaluation criteria of each evaluation item, and evaluate the image quality of an image related to the image data based on an evaluation result of the evaluation of each evaluation item and the weight value for the evaluation item maintained by the storage device.

[0006] According to the second aspect of the present invention, in the information processing system of the first aspect, the storage device maintains a table associating the sets of weight values ​​for multiple evaluation items and the output characteristics of the image, and the processor obtains information specifying the output characteristics of the device that outputs the image data based on the user's operation, and based on the table, associates the set of weight values ​​corresponding to the output characteristics of the device with the device specified by the information, and for the image output by the device, applies the set of weight values ​​associated with the device to the evaluation results of each of the evaluation items to evaluate the image quality.

[0007] According to the third aspect of the present invention, in the information processing system of the second aspect, the processor accepts individual changes in the weight values ​​of one or more evaluation items in the set of weight values ​​in each device associated with the set of weight values ​​based on the user's operation, and updates the set of weight values.

[0008] According to the fourth aspect of the present invention, in the information processing system of the second aspect, the processor accepts changes to adjust each weight value contained in the set of weight values ​​in each device associated with the set of weight values ​​within a predetermined range based on the user's operation, and updates the set of weight values.

[0009] According to the fifth aspect of the present invention, in the information processing system of any one of the first to fourth aspects, the processor obtains historical information related to image output from a device that outputs the image data, updates the weight value applied to the evaluation of the image output by the device based on the information related to image quality contained in the acquired historical information, and for the image output by the device, applies the updated weight value to the evaluation result of each of the evaluation items to evaluate the image quality.

[0010] According to the sixth aspect of the present invention, in the information processing system of the fifth aspect, when the image quality-related settings in the device meet predetermined conditions in multiple image outputs based on the device, the processor updates the weight value based on the historical information in the multiple image outputs.

[0011] According to the seventh aspect of the present invention, a computer-readable medium is provided, storing a program that enables a computer to perform processing, wherein the processing includes: a process of causing a storage device to maintain a weight value of each evaluation item related to image quality; a process of acquiring image data of an evaluation object; a process of evaluating the image data for each evaluation item based on an evaluation benchmark of each evaluation item; and a process of evaluating the image quality of an image related to the image data based on an evaluation result of the evaluation of each evaluation item and the weight value for the evaluation item maintained by the storage device.

[0012] According to the eighth aspect of the present invention, there is provided an information processing method, comprising: a process of causing a storage device to maintain a weight value of each evaluation item related to image quality; a process of acquiring image data of an evaluation object; a process of evaluating the image data for each evaluation item based on an evaluation criteria of each evaluation item; and a process of evaluating the image quality of an image related to the image data based on an evaluation result of the evaluation of each evaluation item and the weight value for the evaluation item maintained by the storage device.

[0013] (Effect)

[0014] According to the first and eighth aspects, compared with a configuration in which image quality is evaluated based on a unified criterion, it is possible to evaluate image quality in a manner that reflects the user's preference.

[0015] According to the second aspect, compared with a configuration in which a user individually inputs a weight value for each evaluation item, the time required for setting the weight value can be reduced.

[0016] According to the third aspect, compared with a configuration in which image quality is evaluated based on a weight value set according to output characteristics, it is possible to perform evaluation corresponding to individual settings for each device.

[0017] According to the fourth aspect, compared with a configuration in which image quality is evaluated based on a weight value set according to output characteristics, it is possible to perform evaluation corresponding to the setting of each device.

[0018] According to the fifth aspect, compared with a configuration in which a user inputs a weight value for each evaluation item according to the use of a device, the time required for setting the weight value can be reduced.

[0019] According to the sixth aspect, unlike a configuration in which weight values ​​are updated based on a single image output, the user's preference regarding image quality can be reflected.

[0020] According to the seventh aspect, compared with a configuration in which image quality is evaluated based on a unified standard, it is possible to evaluate image quality in a manner that reflects the user's preference. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a diagram showing the structure of a diagnostic system to which this embodiment is applied;

[0022] Figure 2 is a diagram showing the structure of an image processing device;

[0023] Figure 3 is a diagram showing an example of the structure of a computer that implements a diagnostic server;

[0024] Figure 4 is a diagram showing a configuration example of a computer realizing a terminal device;

[0025] Figure 5 is a diagram showing the steps of an evaluation performed by a diagnostic server;

[0026] Figure 6 This is a diagram that explains the set of weight values ​​determined according to the output characteristics. Figure 6 (A) is a diagram showing an example of information input by a user. Figure 6 (B) is a diagram showing an example of a correspondence table between input information and JOB (job) types. Figure 6(C) is a diagram showing an example of a setting table of weight values ​​in a JOB type;

[0027] Figure 7 is a diagram showing an example of setting weight values ​​set for individual image processing devices, Figure 7 (A) is a diagram showing weight values ​​set based on selected items. Figure 7 (B) is a diagram showing a situation where the weight value of an individual evaluation item is changed;

[0028] Figure 8 is a diagram showing an example of an adjustment table of weight values;

[0029] Fig. 9 is a diagram showing a state where the weight value for each evaluation item is adjusted;

[0030] Fig.10 is a diagram showing an example of setting a weight value for each image processing device;

[0031] Fig.11 This is a diagram showing an example of a weight value set prepared in accordance with an action mode. DETAILED DESCRIPTION

[0032] Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0033] <System Structure>

[0034] Figure 1 1 is a diagram showing the structure of a diagnostic system to which the present embodiment is applied. The diagnostic system includes an image processing device 100, a diagnostic server 200, and a terminal device 300. The image processing device 100 is a device that forms an image on a recording material such as paper using an image forming material such as a toner and outputs the image. In addition, the image processing device 100 has a function of reading the output image and sending it to the diagnostic server 200. The diagnostic server 200 is a server that evaluates the image received from the image processing device 100 and performs a diagnosis related to the image quality. The terminal device 300 is an information processing device for obtaining the diagnostic result of the diagnostic server 200 and presenting it to a manager or the like.

[0035] The image processing device 100 and the diagnostic server 200, and the diagnostic server 200 and the terminal device 300 are connected via a network, respectively. The network is not particularly limited as long as it can be used for data communication between devices. For example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, etc. can also be used. Whether the communication lines used for data communication are wired or wireless, they can be used together. It can also be configured to use relay devices such as gateway devices, routers, and access points to connect each device via multiple networks or communication lines. The connection between the diagnostic server 200 and the terminal device 300 can use the same network as the network used in the connection between the image processing device 100 and the diagnostic server 200, or a different network. In addition, when the diagnostic server 200 is composed of a local server machine and is configured at the same location as the terminal device 300, the diagnostic server 200 and the terminal device 300 can also be directly connected.

[0036] <Configuration of Image Processing Device 100 >

[0037] Figure 2 1 is a diagram showing the configuration of the image processing apparatus 100 . The image processing apparatus 100 includes an image forming unit 110 , an image reading unit 120 , a display device 130 , an operation device 140 , a communication interface 150 , a storage device 160 , and a control device 170 .

[0038] The image forming unit 110 forms an image based on image data on a recording material using an image forming material. As a method of forming an image on a recording material, for example, an electrophotographic method of transferring a toner attached to a photosensitive body onto a recording material to form an image, an inkjet method of ejecting ink onto a recording material to form an image, etc. are used.

[0039] The image reading unit 120 is composed of a so-called scanner, and optically reads an image on a placed original document to generate data of the read image. As a method of reading an image, for example, a CCD method in which reflected light from a light source irradiating the original document is reduced by a lens and received by a CCD (Charge Coupled Devices), or a CIS (Contact Image Sensor) method in which reflected light from a LED (Light Emitting Diode) light source sequentially irradiating the original document is received by a CIS (Contact Image Sensor).

[0040] The display device 130 displays information images that present various information to the user of the image processing device 100, preview images of images to be processed such as read or output, operation images for the user to operate, etc. The display device 130 is composed of, for example, a liquid crystal display. The display device 130 may be combined with the operation device 140 to be used as a user interface unit for the user to input / output information to the image processing device 100.

[0041] The operating device 140 is a device for the user to perform operations such as inputting commands or data. The operating device 140 is composed of, for example, a touch sensor, which outputs a control signal corresponding to a position pressed or touched by a hard key, a finger, etc. It can also be configured as a touch panel composed of a touch sensor and a liquid crystal display constituting the display device 130.

[0042] The communication interface 150 is an interface for sending and receiving commands and data with an external device. As the communication interface 150, an interface corresponding to the communication method with the external device is used. The connection with the external device can be made via a network or by direct connection. The communication line can be a wired line or a wireless line. In addition, when the image processing device 100 has a facsimile function, the communication interface 150 includes an interface for a telephone line.

[0043] The storage device 160 stores programs or data executed by the control device 170, log data generated by various operations such as image data read by the image reading unit 120, and other various data. The storage device 160 is implemented by a storage device such as a magnetic disk device or an SSD (Solid State Drive).

[0044] The control device 170 includes a processor as a computing unit and a memory as a storage unit, and performs various data processing and control of the image processing device 100 by reading the program stored in the storage device 160 into the memory and executing it. As a processor, in addition to a CPU (Central Processing Unit), an MPU (MicroProcessing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), etc. are used. As a memory, for example, a DRAM (Dynamic Random Access Memory) is used.

[0045] <Configuration of Diagnostic Server 200 >

[0046] Figure 32 is a diagram showing a configuration example of a computer that implements the diagnostic server 200. The diagnostic server 200 includes one or more processors 201 as a computing unit and a main storage device (main memory) 202 and an auxiliary storage device 203 as a storage unit. The processor 201 implements various functions of the diagnostic server 200 by reading a program stored in the auxiliary storage device 203 into the main storage device 202 and executing it. As the processor 201, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), etc. are used. As the main storage device 202, for example, a RAM (Random Access Memory) is used. As the auxiliary storage device 203, for example, a magnetic disk device, an SSD (Solid State Drive), etc. are used. In addition, the diagnostic server 200 includes a communication interface 204 for connecting to the image processing device 100 and the terminal device 300 via a network.

[0047] The diagnostic server 200 evaluates the image acquired from the image processing device 100 to perform a diagnosis by executing a program through the processor 201. The diagnostic server 200 is an example of an information processing system. The details of the evaluation and diagnosis of the image performed by the diagnostic server 200 will be described later. Figure 3 The structure of the computer shown is only an example, and the computer implementing the diagnostic server 200 is not limited to Figure 3 For example, a non-volatile memory such as a flash memory or a ROM (Read Only Memory) may be used as a storage device. Figure 3 The individual computers shown may be configured by distributing functions among a plurality of computers, or may be implemented as a so-called cloud server constructed using resources on a network.

[0048] <Configuration of Terminal Device 300>

[0049] Figure 4This is a diagram showing a configuration example of a computer that implements the terminal device 300. The terminal device 300 includes one or more processors 301 as a computing unit, and a main storage device (main memory) 302 and an auxiliary storage device 303 as a storage unit. The processor 301 implements various functions of the terminal device 300 by reading a program stored in the auxiliary storage device 303 into the main storage device 302 and executing the program. As the processor 301, for example, a CPU, an MPU, a GPU, a DSP, etc. are used. As the main storage device 302, for example, a RAM is used. As the auxiliary storage device 303, for example, a magnetic disk device or an SSD is used.

[0050] In addition, the terminal device 300 includes a display device 304 for displaying various screens and an input device 305 for performing input operations based on the user. The display device 304 is composed of, for example, a liquid crystal display. The input device 305 is composed of, for example, a touch sensor or a keyboard. It can also be configured as a touch panel that combines the touch sensor of the input device 305 and the liquid crystal display of the display device 304. In addition, the terminal device 300 includes a communication interface 306 for connecting to the diagnostic server 200 via a network. As the terminal device 300, for example, a smart phone, a tablet terminal, a laptop computer, etc. can also be used. In addition, Figure 4 The structure of the computer shown is only an example, and the computer used as the terminal device 300 is not limited to Figure 4 For example, a structure including a nonvolatile memory such as a flash memory or a ROM as a storage device may be adopted.

[0051] <Evaluation of images by diagnostic server 200>

[0052] Next, the evaluation and diagnosis of the image by the diagnostic server 200 will be described. As a preliminary operation, the image formed on the recording material by the image forming unit 110 of the image processing device 100 is output, and the output image is read by the image reading unit 120 of the image processing device 100, and the read data (image data) of the output image is obtained. Then, the image data is sent from the image processing device 100 to the diagnostic server 200 as the object of evaluation and diagnosis by the diagnostic server 200.

[0053] Figure 5 : This is a diagram showing the steps of evaluation based on the diagnostic server 200. First, the image is evaluated based on the diagnostic server 200 for evaluation items related to image quality that are divided into multiple categories. Then, based on the evaluation results of each evaluation item, each category is evaluated, and based on the evaluation results of each category, the image quality of the entire image is evaluated. The evaluation of each evaluation item is performed using a score (hereinafter referred to as an "evaluation score") that is specified according to an evaluation standard that is set in advance for each evaluation item. Figure 5In the example shown, "density", "stain in the plane", "periodic stain", "burst streak", "periodic streak", "contamination", "white spot", "roughness" and the like are set as evaluation items, and "density", "stain in the plane", "defect", "noise" and the like are set as types. The evaluation item "density" is included in the type "density". The evaluation item "stain in the plane" and the evaluation item "periodic stain" are included in the type "stain in the plane". The evaluation items "burst streak", "periodic streak", "contamination" and "white spot" are included in the type "defect". The evaluation item "roughness" is included in the type "noise".

[0054] As described above, first, the diagnostic server 200 specifies the evaluation score of each evaluation item for the image of the diagnostic object based on the evaluation criteria of each evaluation item. There is no particular limitation on the evaluation items or the evaluation criteria. The specific setting of the evaluation items and the evaluation criteria can be set by the provider of the diagnostic system or by the user of the diagnostic system. In addition, the user can also change the initial settings made by the provider of the diagnostic system. In addition, the classification of types can also be set to a structure set by the provider or user of the diagnostic system. The diagnostic server 200 calculates the evaluation score of each type based on the evaluation score of each evaluation item specified based on the evaluation criteria, and then calculates the evaluation score of the overall image quality of the image of the diagnostic object based on the evaluation score of each type.

[0055] Here, use Figure 5 The example shown in the figure illustrates an example of a method for calculating the evaluation score of a type based on the evaluation score of the evaluation item. As an example, refer to the evaluation items "sudden streaks", "periodic streaks", "contamination", and "white spots" included in the type "defect". Figure 5 In the example shown, the evaluation score of "burst streaks" is 88 points, the evaluation score of "periodic streaks" is 91 points, the evaluation score of "contamination" is 41 points, and the evaluation score of "white spots" is 87 points. In addition, the highest score of the evaluation score is set to 100 points. First, the value obtained by subtracting the evaluation score of each evaluation item from 100 points, which is the highest score, is calculated, and the obtained value is multiplied by the weight value pre-set for each evaluation item. Here, for simplicity, the weight value is set to 1.0. Figure 5 In the example shown, the calculated values ​​of the evaluation items are 12 for "burst streaks", 9 for "periodic streaks", 59 for "contamination", and 13 for "white spots". The value obtained by subtracting the maximum value of these calculated values ​​from 100 is used as the evaluation score of the type including these evaluation items. In the above example, 59 is the maximum value, so the evaluation score of the type "defect" is 41 (=100-59) points.

[0056] The same operation is performed for other types to calculate the evaluation score reflecting the weight corresponding to the evaluation score of each evaluation item. Figure 5 In the example shown, the evaluation score of the type "density" is 84 points, the evaluation score of "in-plane stain" is 82 points, the evaluation score of "defect" is 41 points, and the evaluation score of "noise" is 86 points.

[0057] In addition, based on the evaluation scores of each type and the weights corresponding to the evaluation scores, the evaluation score of the entire image is calculated. Figure 5 In the example shown, the weight value is set to 1.0, and the maximum value of the calculated value is used to calculate the evaluation score of the image by the same calculation method as the calculation method of calculating the evaluation score of the type based on the evaluation score of the above-mentioned evaluation item. That is, the calculated value of the type "density" is 16 (= (100-84) × 1.0), the calculated value of "in-plane stain" is 18, the calculated value of "defect" is 59, and the calculated value of "noise" is 14. The maximum value is the calculated value of 59 for the type "defect", so the evaluation score of the image becomes 41 (= 100-59).

[0058] The weight values ​​used in the above-mentioned calculation method can also be changed according to the designation made by the user and the implementation method of the image output. Thus, for the evaluation score of the image, the user's preference or the actual trend of the image output can be reflected. The details of the specific method of the weight value are described later. In addition, the above-mentioned calculation method is only an example of a method for specifying the evaluation score of a type based on the evaluation score of the evaluation item and a method for specifying the evaluation score of an image based on the evaluation score of the type, and these calculation methods are not limited to the above-mentioned method. In addition, in the above-mentioned example, the evaluation score of the type is specified based on the maximum value of the calculated value of the evaluation item, and the evaluation score of the image is specified based on the maximum value of the calculated value of the type. In contrast, the weights can also be set according to the order of the size of the calculated value or the value itself, so that the evaluation scores of multiple evaluation items are reflected in the evaluation score of the type, and the evaluation scores of multiple types are reflected in the evaluation score of the image.

[0059] <Weighting setting for each evaluation item>

[0060] The weighting can be set by various methods. Here, a method is described in which a set of pre-set weight values ​​is selected according to the output characteristics of the job executed by the image processing device 100, and the user changes the weight value individually for each evaluation item as needed. The user can also set weight values ​​for all evaluation items, but by using such a set of weight values, the effort required for setting the weight values ​​can be reduced.

[0061] Figure 6 is a diagram illustrating a set of weight values ​​determined according to output characteristics. Figure 6(A) is a diagram showing an example of information input by a user. Figure 6 (B) is a diagram showing an example of a correspondence table between input information and job types. Figure 6 (C) is a diagram showing an example of a setting table of weight values ​​in a job type. Figure 6 The input information of (A) is registered in the diagnostic server 200 by, for example, input by a user operating the terminal device 300 . Figure 6 (B) and Figure 6 The table (C) is stored in, for example, a storage device (eg, Figure 3 In the auxiliary storage device 203 shown.

[0062] exist Figure 6 In the example shown in (A), "industry / business type", "product", "user name", "location", and "machine number" are set as the items of input information. "Industry / business type" is information indicating the industry and business type in which the image processing device 100 is used. Figure 6 In the example shown in (A), printing, architecture, design offices, etc. are registered. "Product" is information indicating the type of product as the image output by the image processing device 100. Figure 6 In the example shown in (A), business, magazines, drawings, pictures, etc. are registered. The "user name" is information indicating the owner of the image processing device 100. Figure 6 In the example shown in (A), A printing, B architectural office, C design office, etc. are registered. "Location" is information indicating the configuration location of the image processing device 100. Figure 6 In the example shown in (A), S Factory, T Factory, Head Office, U Factory, etc. are registered. "Machine No." is identification information of each image processing apparatus 100, and S-1, T-1, 1, U-1, etc. are registered.

[0063] The input information input by the user includes selection items selected from pre-prepared information and free input items input individually by the user. Selection items are items pre-set by the provider of the diagnostic system, etc., and are input by the user selecting any item. Free input items are items input by individual input operations performed by the user. Figure 6 In the example shown in (A), "Industry / Business Type" and "Product" are optional items, and "User Name", "Location", and "Number" are free input items. Figure 6In the example shown in (A), as the selection item "Industry / Format-Product", "Printing-Business", "Printing-Magazine", "Architecture-Drawings", "Design Office-Drawings" and the like are prepared in advance, and the user selects an item that suits him / herself from these items and registers. In addition, the user enters the information of "User Name", "Location", and "Machine Number" and registers.

[0064] The selection items of the input information are associated with the job type. The job type refers to the information of the type of a set of specific weight values. Here, as an example, three job types are set: low quality, medium quality, and high quality. Figure 6 In the correspondence table shown in (B), the selected items "printing - business", "printing - documents / charts", "design office - business" and the like are associated with low image quality, and the selected items "printing - magazine", "printing - drawings", "printing - drawings", "design office - magazine" and the like are associated with high image quality. When the diagnostic server 200 receives the selection of the selected item, it refers to the following Figure 6 The correspondence table shown in (B) specifies the job type corresponding to the selected option.

[0065] In each job type, a weight value is set for each evaluation item. Figure 6 In the setting table shown in (C), weight values ​​of the evaluation items "concentration", "in-surface stains", "periodic stains", "burst stripes", "periodic stripes", "pollution", "white spots" and "roughness" are set for each JOB type of low image quality, medium image quality and high image quality.

[0066] Next, the free input items are described. Each image processing device 100 can be identified by the information of the free input items input by the user. The diagnostic server 200 associates and stores the image processing device 100 identified by the selection item and the information of the free input items with the weight value of each evaluation item set for the image processing device 100. The corresponding information is stored, for example, in a storage device (e.g., Figure 3 In the auxiliary storage device 203 shown.

[0067] Figure 7 is a diagram showing an example of setting weight values ​​set for individual image processing devices 100. Figure 7 (A) is a diagram showing weight values ​​set based on selected items. Figure 7 (B) is a diagram showing a situation where the weight value of an individual evaluation item is changed. Figure 7In the example shown in (A), according to the input information of the selection item and the free input item, the identification information of the image processing device 100 is "Printing-Business-A Printing-S Factory-S-1". The input information of the selection item is "Printing-Business", so the JOB type is "Low Quality". Figure 6 In the setting table shown in (C), the weight value of each evaluation item is "concentration": 1.0, "in-plane stain": 0.7, "periodic stain": 0.7, "sudden streak": 0.7, "periodic streak": 0.7, "contamination": 0.6, "white spot": 1.0, and "roughness": 1.0.

[0068] Here, the user wants to make a stricter evaluation of the evaluation item "contamination" for the S-1 machine located at the location "S Factory" of the user name "A Printing". In this case, the user can Figure 7 The weight value set shown in (A) changes the weight value of the individual evaluation item "pollution". Figure 7 In the example shown in (B), the weight value of the evaluation item "pollution" is increased from Figure 7 The 0.6 shown in (A) is changed to 1.3.

[0069] Next, the adjustment of the set of weight values ​​is described. Figure 7 The change of the weight value described above is performed for each evaluation item individually. In contrast, there are cases where the image quality of the image is to be evaluated strictly as a whole, and conversely, there are cases where the image quality of the image is to be evaluated loosely. In this case, it is possible to consider changing the weight value of each evaluation item as a whole within a pre-set range to adjust the weighting in the image processing device 100 of the object.

[0070] Figure 8 is a diagram showing an example of an adjustment table of weight values. Figure 8 As an example, the value after adjusting the weight value set for low image quality (hereinafter referred to as "adjusted value") is shown. The weight value is adjusted by Figure 6 The weight value set of the setting table shown in (C) is used as the standard, and is performed within a pre-set fixed range. Here, the standard weight value set can be adjusted at a level of -5 to +5. In addition, Figure 8 In the example shown, the weight value sets of each evaluation item in the case of level +5, standard (level 0), and level -5 are shown, but for levels between level +5 and standard, and between standard and level -5, for example, a weight value set based on an adjustment value is also set for each level value 1. In the example shown in the figure, when the level is a positive value, the value of each weight value becomes larger than the standard, and when the level is a negative value, the value of each weight value becomes smaller than the standard.

[0071] Fig. 9 This is a diagram showing the situation where the weight values ​​for each evaluation item are adjusted. As an example, consider the situation where the weight value of the S-1 machine located at the "S factory" where the user "A prints" is adjusted to a lower level. Set the standard weight value set of the S-1 machine to Figure 7 The value shown in (A) is adjusted to level -2. Fig. 9 , adjust the weight of each evaluation item to be relative to Figure 7 The values ​​shown in (A) are lower values.

[0072] In addition, the method for setting the adjustment value of each level is not particularly limited. For example, the value preset for each level can be stored in the storage device of the diagnostic server 200 (for example, Figure 3 In the auxiliary storage device 203 shown in FIG. 1 , the weight value set and the selected level can also be calculated by a pre-set calculation formula. In addition, the weight value set and the selected level can also be calculated by a pre-set calculation formula according to the standard weight value set and the selected level. Figure 8 and Fig. 9 After adjusting the weight value set as described above, each evaluation item is further adjusted as shown in FIG. Figure 7 The weight values ​​are changed individually as described above.

[0073] As described above, the diagnostic server 200 calculates the evaluation score for each type and the evaluation score for the image using the weight values ​​of the evaluation items set by each user for each image processing apparatus 100. Therefore, even when images having the same evaluation scores for the evaluation items are output by different image processing apparatuses 100, the evaluation scores of the images are different according to the settings of these weight values.

[0074] Fig.10 FIG. 1 is a diagram showing an example of setting a weight value for each image processing device 100. Fig.10 In the figure, it is shown that Figure 6 An example of a set of weight values ​​set by the image processing device 100 for each specific input information shown in (A). Fig.10 , the weight value set of each image processing device 100 is different depending on whether the JOB type is low quality, medium quality, or high quality. In addition, the T-1 machine of A Printing and the No. 1 machine of C Design Office are both set to high quality, but the No. 1 machine of C Design Office is adjusted to level +1, so in some evaluation items, the weight value becomes larger than the weight value of the T-1 machine of A Printing. In addition, the U-1 machine of U Factory of A Printing sets the weight value of each evaluation item by manual input, so it becomes a characteristic weight value set in which the weight values ​​of the evaluation items other than "pollution" are large values, and only the weight value of "pollution" is very small.

[0075] As an example, consider the case where the same image is output by the S-1 machine configured in the S factory for printing A, the T-1 machine configured in the T factory, and the U-1 machine configured in the U factory. The evaluation score of each evaluation item of the image is set to Figure 5 The evaluation scores are shown. That is, "density" is 84 points, "in-plane stain" is 93 points, "periodic stain" is 82 points, "sudden streaks" is 88 points, "periodic streaks" is 91 points, "contamination" is 41 points, "white spots" is 87 points, and "roughness" is 86 points.

[0076] In this case, when using Fig.10 The weight value set shown is referenced by Figure 5 When the evaluation score for each type is calculated using the calculation method described above, in the S-1 unit, the type "density" is 84 points, "stains within the surface" is 87.4 points, "defects" is 64.6 points, and "noise" is 86 points. Similarly, in the T-1 unit, the type "density" is 84 points, "stains within the surface" is 82 points, "defects" is 41 points, and "noise" is 86 points. In the U-1 unit, the type "density" is 84 points, "stains within the surface" is 78.4 points, "defects" is 82.3 points, and "noise" is 86 points.

[0077] Furthermore, when the evaluation score of the image is calculated based on the evaluation score of each of these types, it is 64.6 points in S-1, 41 points in T-1, and 78.4 points in U-1. In this way, even for the same image (image with the same evaluation score for each evaluation item), the evaluation value of the image obtained is different based on the difference in the weight value set set for each image processing device 100. Therefore, even if the image used in the diagnosis is the same, the diagnosis result of the image based on the evaluation value becomes an individual diagnosis result for each image processing device 100.

[0078] Here, Fig.10 The relationship between the weight value set and the evaluation result is further discussed in detail. Figure 5 The evaluation score of each evaluation item is shown, and the evaluation item "pollution" has the lowest score of 41 points. Fig.10 In the weight value set shown, when referring to the weight values ​​of the evaluation item "pollution" of each of the above-mentioned S-1, T-1, and U-1, the S-1 is 0.6, and the U-1 is 0.3, which are small values. On the other hand, the T-1 is 1.0, which is a large value. This means that the S-1 and U-1 do not attach importance to the presence or absence of pollution in the image, while the T-1 attaches importance to the presence or absence of pollution in the image. As a result, Fig.10The evaluation score of the image calculated using the weight value set shown in the figure becomes a low evaluation score (41 points) in the T-1 unit which attaches importance to pollution, and becomes an evaluation score higher than the evaluation score of the T-1 unit in the S-1 unit and the U-1 unit which do not attach as much importance to pollution as the T-1 unit (64.6 points for the S-1 unit and 78.4 points for the U-1 unit).

[0079] As described above, by individually setting the weight value of each evaluation item for each image processing apparatus 100 , it is possible to obtain an evaluation result reflecting the preference of the user of the image processing apparatus 100 in the evaluation of the image by the diagnostic server 200 .

[0080] <Setting of Weight Values ​​Based on History Information of Image Processing Device 100>

[0081] In the above-mentioned embodiment, the weight value of each evaluation item is set based on the user's input. In contrast, it is conceivable to automatically set the weight value of each evaluation item based on the user's usage of the image processing device 100. When the image processing device 100 performs output processing of an image, it obtains historical information related to the output. The historical information includes, for example, information on the image quality correction mode and output mode selected when the image is formed in the image processing device 100. The image quality correction mode refers to an action mode for correcting the image quality of the output image according to the type of the image. In the image quality correction mode, for example, by specifying a photo, a picture, a text, etc. as the type of the image, the grayscale number and contrast of the output object image are changed. The output mode refers to an action mode for controlling the image quality of the output image according to the setting of the image processing device 100. In the output mode, for example, by specifying standard, high quality, high definition, etc. as the type of output, the resolution and the grayscale number of the color are controlled in the output action of the image.

[0082] In the image processing device 100, when output is performed using these image quality correction modes and output modes, the image quality is changed according to the selected action mode, so that in the output image, there may be evaluation items that are prominent in the diagnosis of the image and evaluation items that are not prominent. Therefore, it is assumed that when a certain user continuously uses a specific image quality correction mode or output mode, there is a preference for giving more or less importance to a specific evaluation item regarding the image quality of the output image. Therefore, it is considered to prepare a weight value set in which the weight value of a specific evaluation item is set to a large value or a small value corresponding to these image quality correction modes and output modes, and apply it according to the trend of the action mode used in the image processing device 100.

[0083] Fig.11 is a diagram showing an example of a set of weight values ​​prepared corresponding to an action mode. Fig.11In the example shown, a weight value set for each evaluation item as a calculation parameter is set corresponding to the setting of the image quality correction mode and the output mode. Figure 6 (C) Figure 7 The same evaluation items are shown in the following. Fig.11 In the example shown, "Standard", "Photo (People)", "Photo (Landscape)", "Picture", "Office", "Text", etc. are registered as image quality correction modes. In addition, "Standard", "High Quality", "High Definition", etc. are registered as output modes. Furthermore, images output in combinations of these image quality correction modes and output modes are classified as "General", "Photo (People)", "Photo (Landscape)", "Graphic", "Table / Chart", and "Character / Thin Line" as weighted types. Fig.11 In the example, the weighting type is information about the type of a set of specific weight values.

[0084] exist Fig.11 In the example shown, when the image quality correction mode is "Standard" and the output mode is "Standard", the weighting type is "General", and the weight values ​​of all evaluation items are 1 for the weight value set. When the weight values ​​in this case are set as the reference, for example, when the image quality correction mode is "Photo (People)" and the output mode is "High Quality", the weight values ​​of the evaluation items "Periodic Stains", "Burst Streaks", "Periodic Streaks", and "Roughness" become larger values, and the weight value of "White Spots" becomes smaller value. In addition, when the image quality correction mode is "Picture" and the output mode is "High Definition", the weight values ​​of the evaluation items "Burst Streaks", "Periodic Streaks", "Pollution", "White Spots", and "Roughness" become smaller values. In this way, individual weight value sets are set according to the image quality correction mode and the output mode.

[0085] When the image processing device 100 performs an action such as outputting an image, historical information related to the executed processing is obtained. When the above-mentioned image quality correction mode and output mode are used, information on the used action mode is included in the historical information. The historical information is sent from the image processing device 100 to the diagnostic server 200, and the diagnostic server 200 specifies the action mode used in the image processing device 100 based on the obtained historical information, and selects a weight value set to be applied according to the specific action mode.

[0086] Here, it is conceivable that the diagnostic server 200 sets a change condition for a weight value set based on the trend of the action mode used in the image processing device 100, and selects the weight value set to be applied when the change condition is satisfied. For example, in a certain period or a certain number of image outputs based on the image processing device 100, the proportion of using a specific action mode greater than a preset threshold value may be used as a change condition, and a weight value set prepared corresponding to the specific action mode may be applied. In addition, in a certain period or a certain number of image outputs based on the image processing device 100, the continuous use of a specific action mode may be used as a change condition, and a weight value set prepared corresponding to the specific action mode may be applied.

[0087] As an example of a method for applying a weight value set, consider applying Fig.11 The weight value set of the weighting type "normal" (the image quality correction mode is "standard" and the output mode is "standard") is selected, and then the weight value set is applied according to the usage trend of the action mode in the image processing device 100. For example, in a certain image processing device 100, images with text drawn on them are frequently output, which satisfies the change condition of the weight value set. In this case, the diagnosis server 200 changes the weight value set applied to the image processing device 100 from Fig.11 The weight value set of the weighting type "General" is changed to the weight value set of the weighting type "Character / Thin Line".

[0088] Here, Fig.11 The relationship between the weight value set shown and the evaluation result is explained. As an example, consider the case where two image processing devices 100 that are mostly used in action modes with different weighting types output the same image. One image processing device 100 (set as "machine A") is mostly used in the image quality correction mode of "office" and the output mode of "high definition". Fig.11 In the example shown, the combination of the action modes is a weighting type of "table / graph". Another image processing device 100 (set as "unit B") is often used with the image quality correction mode of "photo (person)" and the output mode of "high quality". Fig.11 In the example shown, the combination of the action modes is weighted as "photo (person)".

[0089] Consider the case where the same image is output by machine A and machine B. The evaluation score of each evaluation item of the image is set to Figure 5 The evaluation scores are shown. That is, "density" is 84 points, "in-plane stain" is 93 points, "periodic stain" is 82 points, "sudden streaks" is 88 points, "periodic streaks" is 91 points, "contamination" is 41 points, "white spots" is 87 points, and "roughness" is 86 points.

[0090] When Unit A meets the change condition of the weight value set, the weight value set of the weight value type "Table / Chart" is applied. When Unit B meets the change condition of the weight value set, the weight value set of the weight value type "Photo (People)" is applied. Therefore, when using Fig.11 The weight value set shown is referenced by Figure 5 When the evaluation score for each type is calculated using the calculation method described above, in machine A, the type "density" is 84 points, "stains within the surface" is 84.6 points, "defects" is 58.7 points, and "noise" is 90.2 points. Similarly, in machine B, the type "density" is 84 points, "stains within the surface" is 73.6 points, "defects" is 41 points, and "noise" is 83.2 points.

[0091] Furthermore, when the evaluation score of the image is calculated based on the evaluation score of each of these types, it is 58.7 points in machine A and 41 points in machine B. In this way, even for the same image (an image with the same evaluation score for each evaluation item), the evaluation value of the image obtained is different based on the difference in the weight value set set according to the trend of the action mode used in image processing. Therefore, even if the image used in the diagnosis is the same, the diagnosis result of the image based on the evaluation value becomes an individual diagnosis result for each image processing device 100.

[0092] Although the embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the above embodiments. Various changes or structural substitutions that do not depart from the scope of the technical concept of the present invention are included in the present invention.

[0093] <Note> (((1)))

[0095] An information processing system, characterized in that

[0096] The invention is provided with one or more processors, wherein the processors enable a storage device to maintain a weight value of each evaluation item related to image quality, obtain image data of an evaluation object, evaluate the image data for each evaluation item based on an evaluation criterion of each evaluation item, and evaluate the image quality of an image related to the image data based on an evaluation result of the evaluation of each evaluation item and the weight value for the evaluation item maintained by the storage device. (((2)))

[0098] The information processing system according to (((1))) is characterized in that:

[0099] The storage device maintains a table associating the set of weight values ​​for multiple evaluation items with the output characteristics of the image. The processor obtains information that specifies the output characteristics of the device that outputs the image data based on the user's operation, and based on the table, associates the set of weight values ​​corresponding to the output characteristics of the device to the device specified by the information. For the image output by the device, the set of weight values ​​associated with the device is applied to the evaluation result of each of the evaluation items to evaluate the image quality. (((3)))

[0101] The information processing system according to (((2))) is characterized in that:

[0102] The processor receives, based on a user operation, individual changes to weight values ​​of one or more evaluation items in the set of weight values ​​in each device associated with the set of weight values, and updates the set of weight values. (((4)))

[0104] The information processing system described in (((2))) is characterized in that

[0105] The processor accepts a change to adjust each weight value included in the set of weight values ​​in each device associated with the set of weight values ​​within a preset range according to a user operation, and updates the set of weight values. (((5)))

[0107] The information processing system according to any one of (((1))) to (((4))), characterized in that:

[0108] The processor obtains historical information related to image output from a device that outputs the image data, updates the weight value applied to the evaluation of the image output by the device based on the information related to the image quality contained in the obtained historical information, and applies the updated weight value to the evaluation result of each evaluation item for the image output by the device to evaluate the image quality. (((6)))

[0110] The information processing system according to (((5))) is characterized in that:

[0111] The processor

[0112] When the image quality-related settings in the device satisfy a preset condition in multiple image outputs based on the device, the weight value is updated based on the historical information in the multiple image outputs. (((7)))

[0114] A program, characterized in that

[0115] Enable a computer to implement: a function of enabling a storage device to maintain a weight value of each evaluation item related to image quality; a function of acquiring image data of an evaluation object; a function of evaluating the image data for each evaluation item based on an evaluation benchmark of each evaluation item; and a function of evaluating the image quality of an image related to the image data based on an evaluation result of the evaluation of each evaluation item and a weight value for the evaluation item maintained by the storage device.

[0116] According to the information processing system of (((1))), compared with a configuration in which image quality is evaluated based on a unified criterion, it is possible to evaluate image quality in a manner that reflects the preference of the user.

[0117] According to the information processing system of (((2))), compared with a configuration in which a user individually inputs a weight value for each evaluation item, the time required for setting the weight value can be reduced.

[0118] According to the information processing system of (((3))), compared with a configuration in which image quality is evaluated based on a weight value set according to output characteristics, it is possible to perform evaluation corresponding to individual settings of each device.

[0119] According to the information processing system of (((4))), compared with a configuration in which image quality is evaluated based on a weight value set according to output characteristics, it is possible to perform evaluation corresponding to the setting of each device.

[0120] According to the information processing system of (((5))), compared with a configuration in which a user inputs a weight value for each evaluation item according to the use of a device, the time required for setting the weight value can be reduced.

[0121] According to the diagnosis system of (((6))), unlike a configuration in which weight values ​​are updated based on a single image output, the user's preference regarding image quality can be reflected.

[0122] According to the program of (((7))), in a computer having the program installed therein, image quality that reflects the user's preference can be evaluated by comparison with a configuration that evaluates image quality based on a unified standard.

Claims

1. An information processing system, characterized in that: Having one or more processors, The processor The storage device is configured to store the weight value of each evaluation item related to the image quality. Get the image data of the evaluation object, performing evaluation on the image data for each of the evaluation items based on an evaluation criterion for each of the evaluation items, The image quality of the image related to the image data is evaluated based on the evaluation result of each of the evaluation items and the weight value for the evaluation item stored in the storage device.

2. The information processing system according to claim 1, wherein: The storage device holding a table associating sets of the weight values ​​for a plurality of the evaluation items with output characteristics of an image, The processor acquiring information specifying the output characteristics of the device that outputs the image data according to a user operation, Associating the set of weight values ​​corresponding to the output characteristics of the device to the device specified by the information based on the table, For an image output by the device, the set of weight values ​​associated with the device is applied to an evaluation result of each of the evaluation items to evaluate the image quality.

3. The information processing system according to claim 2, wherein: The processor In accordance with a user operation, individual changes to the weight values ​​of one or more evaluation items in the set of weight values ​​in each device associated with the set of weight values ​​are accepted, and the set of weight values ​​is updated.

4. The information processing system according to claim 2, wherein: The processor According to the user's operation, a change of adjusting each weight value included in the set of weight values ​​in each device associated with the set of weight values ​​within a preset range is accepted, and the set of weight values ​​is updated.

5. The information processing system according to any one of claims 1 to 4, wherein: The processor acquiring history information related to image output from a device that outputs the image data, updating the weight value applied to the evaluation of the image output by the device based on the information related to the image quality contained in the acquired history information, For an image output by the device, the updated weight value is applied to an evaluation result of each evaluation item to evaluate image quality.

6. The information processing system according to claim 5, wherein: The processor When the image quality-related settings in the device satisfy a preset condition in multiple image outputs based on the device, the weight value is updated based on the historical information in the multiple image outputs.

7. A computer-readable medium storing a program for causing a computer to execute a process, characterized in that: The process has: The step of causing a storage device to store a weight value of each evaluation item related to image quality; A process of acquiring image data of an evaluation object; A step of evaluating the image data for each of the evaluation items based on an evaluation criterion for each of the evaluation items; as well as A step of evaluating the image quality of the image related to the image data based on the evaluation result of each of the evaluation items and the weight value for the evaluation item stored in the storage device.

8. An information processing method, characterized in that: have: The step of causing a storage device to store a weight value of each evaluation item related to image quality; A process of acquiring image data of an evaluation object; A step of evaluating the image data for each of the evaluation items based on an evaluation criterion for each of the evaluation items; as well as A step of evaluating the image quality of the image related to the image data based on the evaluation result of each of the evaluation items and the weight value for the evaluation item stored in the storage device.

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