JP2010226241A - Computer program for executing image processing, image processing apparatus and image processing instruction device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To quickly provide processed thumbnail image data to a user. <P>SOLUTION: A multifunctional machine 10 transmits filter ID 100 corresponding to filter processing selected by a user to a server 50. The server 50 executes the filter processing corresponding to the filter ID 100, and transmits processed thumbnail image data 120 to the multifunctional machine 10. The server 50 predicts a filter to be selected next to the filter ID 100 by using past selection results. The server 50 executes filter processing corresponding to the predicted filter regardless of whether or not the filter processing corresponding to the filter is selected by the user in the multifunctional machine 10. If the filter processing corresponding to the predicted filter is executed, by the user, the server 50 can transmit generated thumbnail image data to the multifunctional machine 10. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an image processing technique.

  For example, Patent Document 1 discloses a system including a server and an external device. The external device transmits image data to the server and instructs the server to execute specific image processing on the image data. The server transmits the processed image data to the external device. The external device acquires the processed image data.

JP 2003-283740 A

  There is a demand for providing processed image data to a user as quickly as possible. The present specification discloses a technique capable of quickly providing processed image data to a user.

  There is a possibility that the user sequentially selects two or more types of image processing from among a plurality of types of image processing. For example, the user desires to execute the first type of image processing on the first image data, and then the second image data (which may be the same as or different from the first image data described above) The user may desire to execute the second type of image processing. In one technique disclosed by this specification, the processed image data obtained by the second type of image processing selected after the first type of image processing is quickly provided to the user. . The present technology includes the following configuration.

  One technique disclosed in this specification is a computer program that causes a computer to execute the following processes. The device on which the computer is mounted is not particularly limited. For example, the computer may be mounted on the above server used in a system including an external device and a server that executes image processing in accordance with an instruction from the external device. In addition, for example, a computer may be mounted on a device (for example, a PC, a printer, a scanner, a FAX, a multi-function device, etc.) that executes image processing in accordance with an instruction input by a user in an operation unit included in the computer. Good. In the following, a device that executes image processing itself without causing the server to perform image processing is referred to as an image processing execution device. Each of the above processes includes an image data acquisition process, a type information acquisition process, a first post-processing image data generation process, a first performance information generation process, a first prediction process, and a second post-processing image data generation process.

  In the image data acquisition process, image data to be processed is acquired. For example, a computer mounted on a server may acquire image data to be processed by receiving image data transmitted from an external device. In addition, for example, a computer mounted on an image processing execution device may select one image data from a plurality of image data stored in a storage unit (or external memory) included in the image processing execution device. The image data to be processed may be acquired by reading the image data selected by the user.

  In the type information acquisition process, type information corresponding to the type of image processing selected by the user from among N types (N is an integer of 2 or more) of image processing is acquired. For example, the computer mounted on the server may acquire the type information by receiving from the external device type information corresponding to the type of image processing selected by the user in the external device. In addition, for example, a computer mounted on an image processing execution device allows a user to select one type of image processing from among N types of image processing, and supports the type of image processing selected by the user. The type information may be acquired by reading the type information. Note that the type information is a concept including any information (for example, ID) that can specify the type of image processing. The types of image processing can be classified using various criteria. For example, when image processing such as skin color correction, trimming, and blurring exists, each of these image processing may be classified as one type of image processing. Further, for example, when image processing called skin color correction exists and there are a plurality of parameters indicating the correction amount of the skin color correction, each of the plurality of parameters may be classified as one type of image processing.

  In the first post-processing image data generation processing, the type of image processing corresponding to the type information acquired by the type information acquisition processing is executed on the image data to be processed to generate first post-processing image data. In the first performance information generation process, for each of the N types of image processing, first performance information indicating which type of image processing is selected next to the type of image processing is generated. In the first prediction process, the first prediction for predicting the type of image processing to be selected next to the type of image processing corresponding to the type information acquired by the type information acquisition process is performed using the first result information. To do. The above-mentioned “type of image processing to be selected next” may be determined in advance using the first performance information, and the determination result may be added to the first performance information. In this case, in the first prediction process, when type information is acquired by the type information acquisition process, an image process of a type determined in advance for the type information is specified (also referred to as “selection”). The first prediction described above may be executed.

  In the second post-processing image data generation processing, the second post-processing image data may be generated by executing the type of image processing predicted by the first prediction processing on the image data to be processed. Note that the image data to be processed in the second processed image data generation process may be the same as or different from the image data to be processed in the first processed image data generation process. Good. In the latter case, for example, the image data to be processed in the second processed image data generation process may be the first processed image data. In this example, in the image data acquisition process, the first post-processing image data that is the processing target image data is acquired by reading the first post-processing image data generated by the first post-processing image data generation process. That is, in the second post-processing image data generation processing, the second post-processing image data is executed by performing image processing on the first post-processing image data that is the processing target image data acquired by the image data acquisition processing. Will be generated.

  According to the above computer program, the computer selects the first type when the first type of image processing is selected by the user from among the N types of image processing and then the second type of image processing is selected by the user. Next, the first performance information indicating that the second type of image processing is selected by the user can be generated. The computer can execute the first prediction based on the first performance information that is the past selection performance of the user. That is, at another opportunity, when the first type image processing is selected by the user (that is, when the type information corresponding to the first type image processing is acquired by the type information acquisition processing), It is possible to predict that the second type of image processing is selected (first prediction processing). In this case, the computer determines whether or not the second type of image processing is actually selected by the user (that is, whether or not the type information corresponding to the second type of image processing has been acquired by the type information acquisition processing). Regardless, the second type of image processing can be performed. That is, when the first type of image processing is selected by the user, the computer executes the second type of image processing that is likely to be selected next in light of past selection results in advance. Image data after processing can be generated. In this case, if the second type of image processing is next selected by the user, the processed image data can be quickly provided to the user.

  The 1st performance information may be any form of the following (1)-(3), for example. (1) For example, the first performance information includes, for each of the N types of image processing, the first type information corresponding to the type of image processing and the type of image processing selected next to the type of image processing. The related second type information and the related information associated with each other may be included. For example, when the type Y has been selected after the type X and the type Z has been selected after the type X, the first performance information is the type as the first type information. Related information in which X and type Y as second type information are associated, and related information in which type X as first type information and type Z as second type information are associated. May be included. (2) For example, the first performance information includes, for each of N types of image processing, the first type information corresponding to the type of image processing and the type of image selected most recently after the type of image processing. Related information associated with the second type information corresponding to the process may be included. For example, when the type Y has been selected after the type X, but the type Z has been selected after the type X more recently, the first result information is the first type information. May include related information in which the type X as the second type information is associated with the type Z as the second type information (the type X as the first type information and the type Y as the second type information include Related information may not be included).

(3) For example, the first performance information includes, for each of the N types of image processing, the first type information corresponding to the type of image processing and the type of image processing selected next to the type of image processing. The corresponding second type information is associated with the first selection number information related to the number of times the type of image processing corresponding to the second type information is selected next to the type of image processing corresponding to the first type information. The first related information may be included. In this case, in the first prediction process, the second type information is associated with the first type information corresponding to the type information acquired by the type information acquisition process, and is associated with the largest first selection number information. The first prediction may be executed by specifying the second type information. According to this configuration, the first prediction can be accurately executed based on the past selection results of the user. The above-mentioned “in the first prediction process... Specifying the second type information” is a concept including the following configuration. That is, the second type information to be specified by the first prediction may be determined in advance. For example, for each of the N types of image processing, when the type information corresponding to the image processing of the processing is acquired, the second type information to be specified in the first prediction may be determined in advance. In this case, when the type information is acquired by the type information acquisition process, the first prediction may be executed by specifying the second type information determined in advance for the type information.

  In the first prediction process, when the largest first selection number information associated with the first type information corresponding to the type information acquired by the type information acquisition process is less than a predetermined number, the first prediction is executed. It does not have to be.

  The computer program may cause the computer to further execute a second performance information generation process, a second prediction process, and a third post-processing image data generation process. In the second performance information generation process, second performance information indicating that the type of image processing is selected may be generated for each of the N types of image processing. In the second prediction process, a second prediction for predicting the type of image processing to be selected using the second performance information may be executed. In the third post-processing image data generation processing, the third post-processing image data may be generated by executing the type of image processing predicted by the second prediction processing on the image data to be processed. Note that the image data to be processed in the third processed image data generation process may be the same as the image data to be processed in the first processed image data generation process or the second processed image data generation process. It may be different or different.

  According to the above computer program, the computer can predict a specific type of image processing that is likely to be selected by the user based on the second performance information that is the past selection performance of the user (second prediction). processing). Regardless of whether or not the specific type of image processing is actually selected by the user (ie, whether or not the type information corresponding to the specific type of image processing has been acquired by the type information acquisition processing). Regardless of whether or not), the above kind of image processing can be performed. The computer can generate the processed image data by executing in advance the specific type of image processing that is likely to be selected in light of past selection results. In this case, if the specific type of image processing is selected by the user, the processed image data can be quickly provided to the user.

  Note that the second prediction process and the third post-process image data generation process are executed before the first type of image processing is selected in a situation where a plurality of types of image processing can be sequentially selected by the user, for example. (For example, it may be executed at the timing when the image data to be processed is acquired). In addition, the second prediction process and the third post-processing image data generation process are, for example, when a specific type of image processing is selected by the user, and are selected next to the specific type of image processing. It may be executed when the type of image processing to be predicted cannot be predicted by the first prediction processing (or when the first prediction processing is not executed).

  In addition, 2nd performance information may be any form of the following (1)-(2), for example. (1) For example, the second performance information may include type information corresponding to the type of image processing selected in the past. In this case, the second performance information may include type information corresponding to the type of image processing selected during the period from the past predetermined date and time to the present. (2) For example, the second performance information includes, for each of N types of image processing, third type information corresponding to the type of image processing and second selection number information regarding the number of times the type of image processing has been selected. And the second related information associated with each other. In this case, in the second prediction process, the second prediction may be executed by specifying the third type information associated with the largest second selection count information. Note that “in the second prediction process,... Specifying the third type information” is a concept including the following configuration. That is, the third type information to be specified by the second prediction may be determined in advance. In this case, you may perform 2nd prediction by specifying the 3rd type information determined beforehand.

  The second selection number information is the same as the value obtained by multiplying the first selected number of times when a plurality of types of image processing are sequentially selected with respect to the same processing target image data and the same processing target image data. It may be a value corresponding to the sum of a value obtained by multiplying the number of times selected after the second time when a plurality of types of image processing are sequentially selected by the second value. In this case, the first value may be larger than the second value. According to this configuration, the weight when selected for the first time can be increased. That is, by referring to the second selection count information of each type of image processing, it is possible to specify the type of image processing that is highly likely to be selected for the first time. This configuration is, for example, when the second prediction process is executed before the first type of image processing is selected in a situation where a plurality of types of image processing can be sequentially selected by the user (for example, the processing target image) This is effective when the second prediction process is executed at the timing when the data is acquired.

  In the first prediction process, a third prediction for predicting the type of image processing to be selected by the user next to the type of image processing predicted by the second prediction process is performed using the first performance information. May be executed. According to this configuration, the computer can predict the type of image processing that is likely to be selected by the user next to the type of image processing predicted by the second prediction process, by the third prediction process. The computer can execute the type of image processing regardless of whether or not the type of image processing predicted by the third prediction process is actually selected by the user.

  For example, when the information amount of the second track record information is small, or when the number of times of selection of each type in the second track record information is almost equal, the second prediction cannot be accurately executed based on the second track record information. there is a possibility. In such a case, the following processes may be executed. That is, the computer program may cause the computer to further execute a pre-processed divided image data generation process, a post-processed divided image data generation process, and a combined image data generation process. In the pre-process divided image data generation processing, when the second performance information satisfies a predetermined condition, N pieces of divided image data may be generated by dividing the image data to be processed into N pieces. In the post-processing divided image data generation processing, each of the N types of image processing is executed on one different divided image data among the N pieces of divided image data to generate N post-processing divided image data. May be. In the combined image data generation process, one combined image data may be generated by combining the N post-processing divided image data generated by the divided image data generation process. According to this configuration, when there is a possibility that the computer cannot accurately execute the second prediction based on the second performance information (that is, when the second performance information satisfies a predetermined condition), Combined image data including post-processed divided image data obtained by this kind of image processing can be generated. Note that this description does not mean prohibiting the execution of the second prediction. For example, the second prediction may be performed after the combined image data is generated.

  The computer may be mounted on a server that is communicably connected to an external device. In this case, in the image data acquisition process, the image data to be processed may be acquired by receiving the image data to be processed transmitted from the external device. In the type information acquisition process, the type information may be acquired by receiving type information corresponding to the type of image processing selected by the external device and transmitted from the external device. The computer program described above includes a first transmission process for transmitting the first post-processing image data generated by the first post-processing image data generation process to the external device, and a second generation generated by the second post-processing image data generation process. You may make a computer perform further the 2nd transmission process which transmits the image data after a process to an external device. According to this configuration, the processed image data can be quickly provided to the user of the external device. A system constituted by the external device and the server is also new and useful.

  In the second transmission process, when the type of image processing predicted by the first prediction process is actually selected by the external device (that is, type information corresponding to the type of image process is acquired by the type information acquisition process). The post-second processing image data may be transmitted to the external device. On the other hand, in the second transmission process, the second processed image data may be transmitted to the external device immediately after the second processed image data is generated by the second processed image data generation process. Note that “immediately after the second post-processing image data is generated by the second post-processing image data generation processing” described above, “the type of image processing predicted by the first prediction processing is actually selected in the external device. It may be rephrased as “regardless of whether or not the image processing of the type predicted by the first prediction process is actually selected in the external device”. According to this configuration, when the type of image processing predicted by the first prediction process is actually selected by the external device, the external device can provide the processed image data acquired from the server to the user. (E.g., output (display, print, etc.)). The processed image data can be quickly provided to the user.

  Note that an image processing apparatus, a control method, and the like realized by the above computer program are also novel and useful. The technology disclosed in this specification can also be expressed as an image processing instruction device that causes a server to execute image processing. The image processing instruction device includes a first instruction unit and a first instruction unit in addition to each unit that executes the image data acquisition process, the type information acquisition process, the first performance information generation process, and the first prediction process. The first instruction means, comprising a reception means, a second instruction means, and a second reception means, executes the type of image processing corresponding to the type information acquired by the type information acquisition means for the image data to be processed. Then, the server is instructed to generate the first processed image data. The first receiving means receives the first post-processing image data transmitted from the server. The second instruction means instructs the server to execute the type of image processing predicted by the first prediction means on the image data to be processed to generate second processed image data. The second receiving means receives the second processed image data transmitted from the server. Also with this configuration, the processed image data can be quickly provided (for example, output) to the user. A control method and a computer program for realizing the above-described image processing instruction apparatus are also new and useful. Furthermore, a system constituted by the image processing instruction device and the server is also new and useful.

1 shows an example of the configuration of an image processing system. The sequence diagram of the process which a multi-function device and a server perform is shown. FIG. 3 shows a sequence diagram continued from FIG. 2. An example of a priority information table is shown. An example of the next selection prediction table is shown. An example of a filter execution table is shown. The flowchart of the main process of a server is shown. The flowchart of the prediction process of a server is shown. The flowchart of a server table update process is shown. The flowchart of the adjustment process of a server is shown. The flowchart of a server's prediction process is shown (2nd Example).

Here, some of the techniques described in the following examples are listed.
(Mode 1) The predetermined number may be set based on an average value of all the first selection number information associated with the first type information corresponding to the type information acquired by the type information acquisition process. . For example, the predetermined number may be the average value or a value obtained by multiplying (adding, subtracting, or dividing) the predetermined value by the average value.

(Mode 2) In the pre-process divided image data generation process, the image data to be processed may be divided into a matrix of E rows and F columns (where E × F = N as described above).

(Mode 3) A computer program that causes a computer to execute the following processes is also useful.
(1) Image data acquisition processing for acquiring image data to be processed.
(2) For each of N types (N is an integer of 2 or more) of image processing, performance information generation processing for generating performance information indicating that the type of image processing has been selected.
(3) Prediction processing for predicting the type of image processing to be selected using performance information.
(4) A post-processing image data generation process that generates post-processing image data by executing the type of image processing predicted by the prediction process on the processing target image data.

(Mode 4) A computer program that causes a computer to execute the following processes is also useful.
(1) Image data acquisition processing for acquiring image data to be processed.
(2) Pre-process divided image data generation processing for generating N pieces of divided image data by dividing the image data to be processed into N pieces (N is an integer of 2 or more).
(3) The post-processed divided image data for generating N post-processed divided image data by executing each of the N types of image processing on different one of the N pieces of divided image data. Generation process.
(4) Combined image data generation processing for generating one combined image data by combining the N post-processing divided image data generated by the divided image data generation processing.

(First embodiment)
(System configuration)
Embodiments will be described with reference to the drawings. FIG. 1 shows a schematic diagram of an image processing system 2 of the present embodiment. The image processing system 2 includes the Internet 4, a plurality of multi-function devices 10 and 40, a server 50, and the like. A plurality of multi-function devices 10 and 40 and a server 50 are connected to the Internet 4.

(Configuration of multi-function device 10)
The configuration of the multi-function device 10 will be described in detail. The multi-function device 40 has the same configuration as the multi-function device 10. The multi-function device 10 includes a control unit 12, a display unit 14, an operation unit 16, a USB interface 18, a network interface 20, a printing unit 22, a storage unit 24, and the like. The control unit 12 executes processing according to the program 30 stored in the storage unit 24. The display unit 14 displays various information. The operation unit 16 includes a plurality of keys. The user can input various instructions to the multi-function device 10 by operating the operation unit 16. A USB memory (not shown) or the like is connected to the USB interface 18. The network interface 20 is connected to the Internet 4. The printing unit 22 prints image data.

  The storage unit 24 can store image data 26 to be processed. For example, the user can select one image data from a plurality of image data stored in the USB memory by inserting the USB memory into the USB interface 18 and operating the operation unit 16. The image data selected here is stored as image data 26 to be processed. The storage unit 24 can store a filter type list 28. The filter type list 28 is information indicating a plurality of types of filter processing (image processing) that can be executed by the server 50. The filter type list 28 may be stored in the storage unit 24 in advance or may be acquired from the server 50. For example, the server 50 can execute filter processing such as skin color correction, trimming, blurring, and red-eye correction. The filter type list 28 includes the names and filter IDs of these filter processes. The storage unit 24 further stores a program 30 to be executed by the control unit 12. Further, the storage unit 24 has a storage area 32 for storing information other than the information 26, 28, and 30 described above.

(Processes executed by the multi-function device 10)
Next, processing executed by the multi-function device 10 will be described. 2 and 3 show sequence diagrams of processing executed by the multi-function device 10 and the server 50. FIG. The multi-function device 10 executes a preview screen display process (S4) when a predetermined operation is performed on the operation unit 16 (S2). 2 indicates an example of display contents displayed on the display unit 14 in the preview screen display process (S4). In the preview screen display process of the present embodiment, thumbnail image data 80, 82, 84 of a plurality of image data stored in the USB memory is displayed.

  The user can execute an image selection operation for selecting one thumbnail image data from among the plurality of thumbnail image data 80, 82, 84 (S6). In the present embodiment, the following description will be continued by taking as an example the case where the thumbnail image data 80 is selected. The multi-function device 10 executes an image data transmission process for transmitting the thumbnail image data 80 selected by the user to the server 50 (S8). As a result, the server 50 starts a prediction process described later (S10). When the image selection operation (S6) is executed, the multi-function device 10 executes a selection screen display process (S12). 2 indicates an example of display contents displayed on the display unit 14 in the selection screen display process (S12). In the selection screen display process of the present embodiment, the thumbnail image data 80 selected by the image selection operation (S6) and the names X, Y, etc. of the plural types of filter processes included in the filter type list 28 (see FIG. 1) are included. Is displayed.

  The user can execute a filter selection operation for selecting one type of filter processing from among a plurality of types of filter processing names X and Y (S14). The multi-function device 10 executes filter information transmission processing for transmitting to the server 50 the filter ID 100 (which may be a name instead of ID) corresponding to the filter processing selected by the user (S16). As a result, the server 50 executes an adjustment process described later (S18). The server 50 generates the processed thumbnail image data 120 by executing the type of filter processing (filter processing corresponding to the filter ID 100) selected by the filter selection operation (S14) on the thumbnail image data 80. To do. The server 50 transmits the thumbnail image data 120 to the multi-function device 10. As will be described in detail later, the thumbnail image data 120 can be generated in advance before the server 50 receives the filter ID 100. In this case, the server 50 transmits the generated thumbnail image data 120 to the multi-function device 10 when the filter ID 100 is received. The multi-function device 10 executes a selection screen update process for displaying the thumbnail image data 120 on the display unit 14 instead of the thumbnail image data 80 (S20). 2 indicates an example of display content displayed on the display unit 14 in the selection screen update process (S20).

  The multi-function device 10 and the server 50 execute the filter information transmission process (S16), the adjustment process (S18), and the selection screen update process (S20) each time the filter selection operation (S14) is executed. . For example, as shown in FIG. 3, the user can further execute a filter selection operation for selecting one type of filter processing on the screen 14c updated in the selection screen update processing in S20 (S22). In this case, the multi-function device 10 executes a filter information transmission process for transmitting the filter ID 102 corresponding to the filter process selected by the user to the server 50 (S24). As a result, the server 50 executes an adjustment process described later (S26). The server 50 transmits the processed thumbnail image data 122 obtained by the filter processing corresponding to the filter ID 102 to the multi-function device 10. As will be described in detail later, the thumbnail image data 122 can be generated in advance before the server 50 receives the filter ID 102. The multi-function device 10 executes a selection screen update process for displaying the thumbnail image data 122 on the display unit 14 instead of the thumbnail image data 120 (S28). Reference numeral 14d in FIG. 3 shows an example of display contents displayed on the display unit 14 in the selection screen update process (S28).

  Although not shown in the sequence diagrams of FIGS. 2 and 3, the user of the multi-function device 10 can select the filter ID and press the print determination button. In this case, the multi-function device 10 transmits the filter ID selected by the user and the original image data corresponding to the thumbnail image data 80 selected in the image selection operation (S6) of FIG. . The original image data is image data representing an image corresponding to the thumbnail image data 80 and is image data having a data size larger than that of the thumbnail image data 80. The server 50 generates post-processing image data by performing a filtering process corresponding to the filter ID selected by the user on the original image data. The server 50 transmits the processed image data to the multi-function device 10. As a result, the multi-function device 10 prints the processed image data.

(Configuration of server 50)
Next, the configuration of the server 50 will be described with reference to FIG. The server 50 includes a control unit 52, a network interface 54, and a storage unit 56. The control unit 52 executes processing according to the program 66 stored in the storage unit 56. The network interface 54 is connected to the Internet 4.

  The storage unit 56 stores a priority information table 58. FIG. 4 shows an example of the priority information table 58. The priority information table 58 is created for each user. For example, when the multi-function device 10 is used by the user U1 and the multi-function device 40 is used by the user U2, the respective priority information tables 58 of the users U1 and U2 are generated. A plurality of users may exist for one multi-function device. For example, when the multi-function device 40 is used by the users U2 and U3, the respective priority information tables 58 of the users U2 and U3 may be generated. When such a form is adopted, the server 50 preliminarily stores information indicating which user is using the multi-function device 40 when the multi-function device 40 executes the processes shown in FIGS. 2 and 3. Need to get.

  In the present embodiment, the following description will be continued by taking as an example the case where the priority information table 58a corresponding to the user U1 in FIG. 4 is a priority information table corresponding to the multi-function device 10. The priority information table 58a includes a plurality of combination information 130 to 136. Each of the combination information 130 to 136 is information in which the filter ID 140, the first selection number 142, the second and subsequent selection numbers 144, and the priority 146 are associated with each other. The filter ID 140 is an ID that identifies the type of filter processing. In this embodiment, there are four types of filter processing, and there are filter IDs 100 to 106 corresponding to the respective types. The first selection number 142 is a case where the filter selection operation (see S14 in FIG. 2 and S22 in FIG. 3) is executed a plurality of times for the thumbnail image data selected by the image selection operation (see S6 in FIG. 2). Indicates the number of times selected for the first time. The second and subsequent selection times 144 are determined by the filter selection operation (see S14 in FIG. 2 and S22 in FIG. 3) multiple times for thumbnail image data selected by the image selection operation (see S6 in FIG. 2). The number of times selected after the second time when executed is shown.

  For example, in the example of FIGS. 2 and 3, for the thumbnail image data 80, the filter process corresponding to the filter ID 100 is selected for the first time (see S14 in FIG. 2), and then the filter process corresponding to the filter ID 102 is selected. (See S22 in FIG. 3). In this case, “1” is added to the first selection number 142 of the filter ID 100, and “1” is added to the second and subsequent selection times 144 of the filter ID 102. The priority 146 is a value corresponding to the sum of the value obtained by multiplying the initial selection count 142 by “3” and the second and subsequent selection counts 144. In other words, in this embodiment, the initial selection number 142 has a larger influence (weight) on the priority 146 than the second and subsequent selection numbers 144.

  The storage unit 56 further stores a next selection prediction table 60 (see FIG. 1). FIG. 5 shows an example of the next selection prediction table 60. The next selection prediction table 60 is created for each user. In the present embodiment, the following description will be continued by taking as an example the case where the next selection prediction table 60a corresponding to the user U1 in FIG. 5 is the next selection prediction table corresponding to the multi-function device 10. In the next selection prediction table 60a, the number of times each filter ID 100 to 106 arranged in the leftmost column 150 is selected by the user, and then each filter ID 100 to 106 arranged in the uppermost column 152 is selected by the user. Indicates. Accordingly, in FIG. 5, for example, the number of times the filter ID 100 is selected and then the filter ID 102 is selected is five as shown in the column 154. Also, for example, the number of times the filter ID 100 is selected and then the filter ID 104 is selected is zero.

  In the next selection prediction table 60a, the prediction filter 160 is associated with each of the filter IDs 100 to 106. For example, the filter ID 102 is associated with the filter ID 100 as the prediction filter 160. The prediction filter 160 (that is, the filter ID 102) associated with the filter ID 100 is determined as follows. First, “5” (5 + 0 + 0), which is the total number of times the filter ID 100 is selected, is divided by “3”, which is the number of the other filter IDs 102 to 106, thereby obtaining an average value of “1.66.・ ”Is calculated. Next, the reference value “3.33...” Is calculated by multiplying the calculated average value by 2. Next, a filter ID 102 that is a filter ID having a number of times larger than the calculated reference value is specified. The filter ID 102 specified in this way is determined as the prediction filter 160. That is, it can be said that the filter ID 102 that is the prediction filter 160 related to the filter ID 100 is the filter ID that is most likely to be selected next when the filter ID 100 is selected. If there are a plurality of filter IDs having a number of times greater than the reference value, the filter ID having the largest number of times is determined as the prediction filter 160 from among the filter IDs.

  The prediction filter 160 is similarly determined for each of the filter IDs 102 to 106 other than the filter ID 100. For example, in FIG. 5, the prediction filter 160 associated with the filter ID 104 is the filter ID 106. In FIG. 5, the filter IDs 102 and 106 are not associated with the prediction filter 160. This means that the prediction filter 160 cannot be determined using the above method. That is, it means that there is no filter ID having a number of times larger than the reference value.

  As shown in FIG. 1, the storage unit 56 further has a work area 62. The work area 62 stores data generated in the process in which the control unit 52 executes processing. For example, the work area 62 stores a filter execution table 64. FIG. 6 shows an example of the filter execution table 64. The filter execution table 64 includes a plurality of combination information 170 to 176. Each combination information 170 to 176 is information in which the filter ID 180, the priority 182, the prediction filter 184, and the status 186 are associated with each other. The priority 182 corresponds to the priority 146 shown in FIG. 4, and the prediction filter 184 corresponds to the prediction filter 160 shown in FIG. The status 186 is set to one of “0”, “1”, and “2”. “0” indicates that the filtering process is not being executed, “1” indicates that the filtering process is being executed, and “2” indicates that the filtering process has been completed.

  As shown in FIG. 1, the storage unit 56 further stores a program 66 to be executed by the control unit 52. The program 66 may be installed in the server 50 from a program storage medium, or may be downloaded from the Internet 4 to the server 50, for example. Also. The storage unit 56 has a storage area 68 for storing information other than the information 58, 60, 64, 66 described above.

(Processing executed by the server 50)
Then, the content of the process which the control part 52 of the server 50 performs is demonstrated. FIG. 7 shows a flowchart of the main process executed by the control unit 52. The control unit 52 monitors the reception of thumbnail image data (S40). For example, when the thumbnail image data 80 transmitted from the multi-function device 10 is received in the image data transmission process of S8 of FIG. 2, the control unit 52 determines YES in S40. In this case, the control unit 52 starts the prediction process (S42).

  FIG. 8 shows a flowchart of the prediction process. The control unit 52 generates a filter execution table (S70). For example, when the thumbnail image data 80 (see FIG. 2) transmitted from the multi-function device 10 is received in S40 of FIG. 7, the control unit 52 performs the filter execution table 64 (see FIG. 6) corresponding to the multi-function device 10. ) Is generated. First, the control unit 52 writes the filter IDs 100 to 106 in the column of the filter ID 180. Next, the control unit 52 specifies the priority 146 of each of the filter IDs 100 to 106 from the priority information table 58a (see FIG. 4) of the user U1 corresponding to the multi-function device 10. The control unit 52 writes the identified priorities 146 in the priority 182 column of the filter execution table 64. Subsequently, the control part 52 specifies the prediction filter 160 of each filter ID100-106 from the next selection prediction table 60a (refer FIG. 5) of the user U1 corresponding to the multi-function device 10. FIG. The control unit 52 writes each identified prediction filter 160 in the column of the prediction filter 184 of the filter execution table 64. Further, the control unit 52 writes “0” in the status 186 column of all the filter IDs 100 to 106 in the filter execution table 64.

  Next, the control unit 52 refers to the filter execution table 64 to select a filter ID having a status 186 of “0” and a highest priority 182 (S72). For example, when the status 186 of all the filter IDs 100 to 106 is “0” in the filter execution table 64 of FIG. 6, the control unit 52 specifies the filter ID 100. Subsequently, the control unit 52 writes “1” in the status 186 column of the filter ID (eg, filter ID 100) selected in S72 (S74). Next, the control unit 52 starts filter processing corresponding to the filter ID selected in S72 (for example, filter ID 100) on the thumbnail image data 80 (see FIG. 2) received in S40 of FIG. 7 (S76). ).

  The control unit 52 monitors completion of the filter process started in S76 (S78). In the case of YES here, the control unit 52 writes “2” in the column of the status 186 (see FIG. 6) of the filter ID (for example, filter ID 100) selected in S72 (S80). Next, the control unit 52 uses the processed thumbnail image data obtained by the filter process (filter process completed in S78) corresponding to the filter ID selected in S72 (for example, the filter ID 100) as the work area 62 (see FIG. 1). (S82).

  Next, the control unit 52 specifies the prediction filter 160 of the filter ID (for example, filter ID 100) selected in S72 by referring to the filter execution table 64 (see FIG. 6). For example, when the filter process corresponding to the filter ID 100 is completed, the control unit 52 specifies the filter ID 102. Further, the control unit 52 determines whether or not the status 186 of the specified filter ID (for example, the filter ID 102) is “0” (S84). In the case of YES here, the control unit 52 selects the specified filter ID (for example, the filter ID 102) (S86), and proceeds to S74. As a result, the processing after S74 is executed for the filter ID selected in S86 (for example, the filter ID 102). On the other hand, in the case of NO in S84, if the filter ID whose status 186 is “0” exists in the filter execution table 64 (YES in S94), the control unit 52 proceeds to S72. As a result, the control unit 52 selects the filter ID (for example, the filter ID 104) having the highest priority 182 and the filter ID having the status 186 of “0”. As a result, the processing after S74 is executed for the filter ID (for example, filter ID 104) selected in S72. If a filter ID having a status 186 of “0” does not exist in the filter execution table 64 (NO in S94), that is, if all filter processes have been executed, this process ends.

  On the other hand, when the filter process is started in S76, the control unit 52 monitors whether cancellation is instructed from an adjustment process described later (S88). In the case of YES here, the control unit 52 cancels (stops) the filter process started in S76, and “0” is displayed in the status 186 of the filter ID (filter ID selected in S72 or S86) corresponding to the filter process. Is written (S90). Next, the control unit 52 selects the filter ID notified from the adjustment process (S92), and proceeds to S74. As a result, the processing after S74 is executed for the filter ID selected in S92.

  As shown in FIG. 7, the control unit 52 monitors the reception of the filter ID (S44). For example, when the filter IDs 100 and 102 transmitted from the multi-function device 10 are received in the filter information transmission process of S16 of FIG. 2 or S24 of FIG. 3, the control unit 52 determines YES in S44. In this case, the control unit 52 starts a table update process (S46).

  FIG. 9 shows a flowchart of the table update process. The control unit 52 determines whether or not the filter ID received in S44 of FIG. 7 is received for the first time after the thumbnail image data 80 to be processed (see FIG. 2) is received (S110). . For example, when the filter ID 100 transmitted from the multi-function device 10 is received in the filter information transmission process of S16 of FIG. 2, the control unit 52 determines NO in S110 because the filter ID 100 is received for the first time. Judge. On the other hand, for example, when the filter ID 102 transmitted from the multi-function device 10 is received in the filter information transmission process of S24 of FIG. 3, the filter ID 102 is received for the second time or later. 52 determines YES in S110.

  In the case of NO in S110, the control unit 52 updates the priority information table 58a (see FIG. 4) (S112). More specifically, the control unit 52 adds “1” to the initial selection number 142 of the filter ID (for example, filter ID 100) received in S44 of FIG. 7 in the priority information table 58a. On the other hand, in the case of YES in S112, the control unit 52 updates the priority information table 58a (see FIG. 4) and also updates the next selection prediction table 60a (see FIG. 5) (S114, S116). Specifically, in the priority information table 58a, the control unit 52 adds “1” to the selection count 144 after the second time of the filter ID (for example, the filter ID 102) received in S44 of FIG. 7 (S114). . Further, in the next selection prediction table 60a, the control unit 52 filters the filter ID (for example, filter ID 100) received last time in S44 of FIG. 7 and the filter ID (for example, filter ID 102) received this time in S44 of FIG. “1” is added to the combination. For example, if the filter ID 100 was received last time in S44 of FIG. 7 and the filter ID 102 was received this time in S44 of FIG. 7, “1” is set to “5” in the column 154 of FIG. Are added (S116).

  Although not shown in the flowchart, when the priority information table 58a is updated, the control unit 52 calculates the priority 146 (see FIG. 4) of the priority information table 58a at a predetermined timing. Execute. Further, when the next selection prediction table 60a is updated, the control unit 52 executes a process of specifying the prediction filter 160 (see FIG. 5) of the next selection prediction table 60a at a predetermined timing. The predetermined timing described above may be, for example, a timing immediately after the communication with the multi-function device 10 is completed, or may be a timing when the tables 58a and 60a are updated.

  As shown in FIG. 7, when the control unit 52 starts the table update process (S46), the status of the filter ID received in S44 is subsequently referred to by referring to the filter execution table 64 (see FIG. 6). It is determined whether or not 186 is “2” (S48). In the case of NO here, the control unit 52 starts the adjustment process (S50).

  FIG. 10 shows a flowchart of the adjustment process. The control unit 52 determines whether or not the status 186 of the filter ID received in S44 of FIG. 7 is “0” (S130). In the case of NO here, the status 186 of the filter ID received in S44 of FIG. 7 is “1”, and the filter processing corresponding to the filter ID is being executed in the prediction processing (see FIG. 8). means. In this case, the control unit 52 ends the adjustment process.

  On the other hand, if YES in S130, it means that the filter process corresponding to the filter ID other than the filter ID received in S44 of FIG. 7 is being executed in the prediction process (see FIG. 8). In this case, the control unit 52 instructs the prediction process to be canceled (S132). Further, the control unit 52 notifies the prediction process of the filter ID received in S44 of FIG. 7 (S134). As a result, in the prediction process of FIG. 8, it is determined as YES in S88, the currently executed filter process is canceled, and the filter process corresponding to the filter ID notified from the adjustment process is executed (S92, S74, S76).

  As shown in FIG. 7, when starting the adjustment process (S50), the control unit 52 waits until the filter process is completed in the prediction process according to the filter ID received in S44 (S52). When the filter process corresponding to the filter ID received in S44 is completed, the control unit 52 performs the processed thumbnail image data (thumbnail image data stored in S82 of FIG. 8) obtained by the filter process. It transmits to the multi-function device 10 (S54). In the case of YES in S48, the process proceeds to S54. Also in this case, the control unit 52 transmits the processed thumbnail image data (thumbnail image data stored in S82) obtained by the filter processing corresponding to the filter ID received in S44 to the multi-function device 10. By executing S54, the multi-function device 10 can display the processed thumbnail image data on the display unit 14 (see S20 in FIG. 2 and S28 in FIG. 3).

  The image processing system 2 of the present embodiment has been described in detail. The server 50 can generate the next selection prediction table 60a by executing the table update process of FIG. For example, when the filter ID 100 is selected by the user of the multi-function device 10, the server 50 can predict the filter ID 102 that is likely to be selected next by referring to the next selection prediction table 60a. (S84 and S86 in FIG. 8). The server 50 executes the filter process corresponding to the filter ID 102 regardless of whether or not the predicted filter ID 102 is actually selected by the user (S76 in FIG. 8). Therefore, if the filter ID 102 is selected by the user after the filter ID 100 (that is, when the filter ID 102 is received in S44 of FIG. 7), the server 50 completes the filter processing corresponding to the filter ID 102. There is a possibility. If the filter processing corresponding to the filter ID 102 has been completed, the server 50 can immediately transmit the thumbnail image data in response to the reception of the filter ID 102 in S44 of FIG. The processed thumbnail image data can be quickly provided to the user of the multi-function device 10.

  Further, the server 50 can generate the priority information table 58a by executing the table update process of FIG. For example, when the thumbnail image data 80 transmitted from the multi-function device 10 is received in the image data transmission process (S8) of FIG. 2, the server 50 is selected by referring to the priority information table 58a. A filter ID (for example, filter ID 100) having a high possibility can be predicted (S72 in FIG. 8). As described above, the priority 146 (see FIG. 4) has a large weight of the first selection number 142. Therefore, the server 50 can accurately predict a filter ID (for example, filter ID 100) that is likely to be selected for the first time. The server 50 executes the filter process corresponding to the filter ID 100 regardless of whether or not the predicted filter ID 100 is actually selected by the user (S76 in FIG. 8). Therefore, if the filter ID 100 is first selected by the user after the thumbnail image data 80 is received (that is, if the filter ID 100 is received in S44 of FIG. 7), the server 50 sets the filter ID 100 to the filter ID 100. Corresponding filtering may have been completed. If the filter processing corresponding to the filter ID 100 is completed, the server 50 can immediately transmit the thumbnail image data in response to the reception of the filter ID 100 in S44 of FIG. The processed thumbnail image data can be quickly provided to the user of the multi-function device 10.

  In the present embodiment, when the prediction filter 160 (see FIG. 5) to be selected next to the filter ID selected by the user does not exist in the next selection prediction table 60a (NO in S84 of FIG. 8). In addition, the process of predicting the filter ID using the priority information table 58a is executed. Even when the prediction filter 160 (see FIG. 5) does not exist, it is possible to predict a filter ID that is likely to be selected next.

(Second embodiment)
Differences from the first embodiment will be described. In the first embodiment, the server 50 transmits the processed thumbnail image data to the multi-function device 10 in accordance with the selection of the filter process in the multi-function device 10 (that is, reception of the filter ID). The server 50 of this embodiment is different from the first embodiment in this point. That is, when the filtering process is completed in S78 of FIG. 8, the server 50 according to the present embodiment performs the processed thumbnail image data obtained by the filtering process regardless of the selection of the filtering process in the multi-function device 10. Is transmitted to the multi-function device 10. The multi-function device 10 caches thumbnail image data transmitted from the server 50 in the storage area 32 (see FIG. 1). When the filtering process is selected by the user and the thumbnail image data obtained by the filtering process is cached, the multi-function device 10 displays the thumbnail image data on the display unit 14a. If the thumbnail image data is not cached, the multi-function device 10 waits until the thumbnail image data is received from the server 50.

  According to the present embodiment, when the thumbnail image data obtained by the filtering process selected by the user is cached, the multi-function device 10 can quickly display the thumbnail image data on the display unit 14a. .

(Third embodiment)
Differences from the first and second embodiments will be described. The server 50 of the present embodiment is different from the first and second embodiments in the content of the prediction process (see FIG. 8). FIG. 11 shows a flowchart of the prediction process of the present embodiment. The control unit 52 of the server 50 generates a filter execution table 64 (see FIG. 6). This is the same as in the first and second embodiments. The control unit 52 calculates the average value of the priorities 182 of all the filter IDs 100 to 106 in the filter execution table 64, calculates the value T1 by subtracting the first predetermined value from the average value, and sets the second value to the average value. A value T2 is calculated by adding a predetermined value (may be the same value as the first predetermined value or a different value). Next, the control unit 52 determines whether or not the priorities 182 of all the filter IDs 100 to 106 in the filter execution table 64 are included in the range between the above T1 and T2 (S152). In the case of NO here, the process proceeds to S72 of FIG.

  On the other hand, in the case of YES in S152, the control unit 52 refers to the filter execution table 64 to count the number of filter IDs whose status 186 is “0” (S154). Hereinafter, the number counted here is assumed to be “C”. For example, in the example of FIG. 6, since there are four filter IDs, C = 4 is obtained in S154. Next, the control unit 52 divides the thumbnail image data received in S40 of FIG. 7 into C image data counted in S154 (S156). For example, the control unit 52 divides the thumbnail image data into a matrix of E rows and F columns (E × F = C). As a result, C pieces of divided image data are obtained.

  Subsequently, the control unit 52 starts filter processing corresponding to the C filter IDs 100 to 106 for each of the C divided image data (S158). For example, when the first to fourth divided image data are obtained, the control unit 52 starts a filter process corresponding to the filter ID 100 for the first divided image data. Further, the control unit 52 starts filter processing corresponding to the filter IDs 102, 104, and 106 for the other second to fourth divided image data.

  The control unit 52 monitors the completion of the filter processing for the C divided image data (S160). In the case of YES here, the control unit 52 generates one processed thumbnail image data by combining the C divided image data (S162). Next, the control unit 52 transmits the processed thumbnail image data to the multi-function device 10 (S164). As a result, the multi-function device 10 causes the display unit 14 to display the processed thumbnail image data. When S164 ends, the process proceeds to S72 of FIG. Further, the control unit 52 monitors whether cancellation is instructed from the adjustment process of FIG. 10 (S166). In the case of YES here, the control unit 52 stops the filter process started in S158 and proceeds to S92 in FIG.

  In the present embodiment, the server 50 performs filter processing corresponding to a plurality of filter IDs when there is no filter ID having the protruding priority 182 (that is, when it cannot be accurately predicted based on the priority 182). Is executed to generate thumbnail image data after processing, and the thumbnail image data is transmitted to the multi-function device 10. Thumbnail image data obtained by filter processing corresponding to a plurality of filter IDs can be provided to the user. The user can know what kind of processing is performed by each filter process by looking at the thumbnail image data.

(Fourth embodiment)
Differences from the first to third embodiments will be described. In the present embodiment, the multi-function device 10 generates the priority information table 58 and the next selection prediction table 60, and executes each process of FIGS. However, the following points are different in each processing of FIGS. In S40 of FIG. 7, the multi-function device 10 does not monitor the reception of image data, but monitors the execution of the image selection operation (S6) of FIG. In S44 of FIG. 7, the multi-function device 10 does not monitor the reception of the filter ID but monitors the execution of the filter selection operations (S14, S22) of FIGS.

  In S <b> 76 of FIG. 8, the multi-function device 10 instructs the server 50 not to start the filter process but to start the filter process. In this instruction, the server 50 is instructed to start the filter process corresponding to the filter ID selected in S72, S86, or S92. In S78 of FIG. 8, the multi-function device 10 monitors whether the server 50 has completed the filtering process and receives the processed thumbnail image data obtained by the filtering process from the server 50. Further, in S52 of FIG. 7, the multi-function device 10 stands by until the filter processing is completed in the server 50 and the processed thumbnail image data obtained by the filter processing is received from the server 50. In S54 of FIG. 7, the multi-function device 10 does not transmit the processed thumbnail image data, but the selection screen update process for displaying the processed thumbnail image data on the display unit 14 (S20 of FIG. 2, FIG. 3). (Refer to S28).

  Also according to this embodiment, the processed thumbnail image data can be quickly provided to the user of the multi-function device 10.

  Specific examples of the present invention have been described in detail above, but these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and changes of the specific examples illustrated above. The modifications of the above embodiment are listed below.

(1) In the above embodiment, the prediction filter 160 is previously determined in the next selection prediction table 60a (see FIG. 5). That is, the server 50 specifies the filter ID to be selected next by specifying the prediction filter 160 predetermined for the filter ID corresponding to the filter process selected by the user in S84 and S86 of FIG. Predict. However, the server 50 may not determine the prediction filter 160 in advance in the next selection prediction table 60a. In this case, each time S <b> 82 in FIG. 8 is finished, the server 50 selects the filter process selected by the user (or the filter process selected in S <b> 72) using the method described in the above embodiment. The filtering process (filter ID) to be performed may be determined.

(2) In the above embodiment, the server 50 executes the filter process, but the multi-function device 10 may execute the filter process. For example, in the fourth embodiment, the configuration in which the multi-function device 10 instructs the server 50 to perform the filtering process can be replaced with a configuration in which the multi-function device 10 performs the filtering process by itself. In the case of this configuration, the server 50 can be omitted.

(3) In the above embodiment, the server 50 executes each filter process selected in the multi-function device 10 for the same thumbnail image data 80. However, each filter process selected in the multi-function device 10 may be executed for different thumbnail image data. For example, when the filter ID 100 transmitted from the multi-function device 10 is received, the server 50 performs a filter process corresponding to the filter ID 100 on the thumbnail image data 80, and then selected next to the filter ID 100. When the filter ID 102 to be predicted is predicted, the filter process corresponding to the filter ID 102 may be executed on the thumbnail image data obtained by the filter process corresponding to the filter ID 100.

(4) In the above embodiment, the multi-function device 10 transmits the thumbnail image data 80 to the server 50, and the server 50 executes the filtering process on the thumbnail image data 80. However, the multi-function device 10 may transmit original image data (original image data of the thumbnail image data 80) to the server 50, and the server 50 may perform a filtering process on the original image data.

  The technical elements described in this specification or the drawings exhibit technical usefulness alone or in various combinations, and are not limited to the combinations described in the claims at the time of filing. In addition, the technology illustrated in the present specification or the drawings achieves a plurality of objects at the same time, and has technical utility by achieving one of the objects.

  2: image processing system, 4: internet, 10, 40: multi-function device, 12: control unit, 14: display unit, 14a to 14d: screen, 16: operation unit, 18: USB interface, 20: network interface, 22 : Printing unit, 24: Storage unit, 28: Filter type list, 30: Program, 50: Server, 52: Control unit, 54: Network interface, 56: Storage unit, 58, 58a: Priority information table, 60, 60a : Next selection prediction table, 64: Filter execution table, 66: Program, 80: Thumbnail image data, 100 to 106: Filter ID, 120, 122: Thumbnail image data after processing

Claims (14)

Image data acquisition processing for acquiring image data to be processed;
Type information acquisition processing for acquiring type information corresponding to the type of image processing selected by the user from among N types of image processing (N is an integer of 2 or more);
First post-processing image data generation processing for generating first post-processing image data by executing image processing of the type corresponding to the type information acquired by the type information acquisition processing on the image data to be processed When,
For each of the N types of image processing, first performance information generation processing for generating first performance information indicating which type of image processing is selected next to the type of image processing;
A first prediction is executed for predicting, using the first performance information, the type of image processing to be selected next to the type of image processing corresponding to the type information acquired by the type information acquisition processing. The prediction process,
A second post-processing image data generation process for generating the second post-processing image data by executing the type of image processing predicted by the first prediction process on the processing target image data;
A computer program that causes a computer to execute. The first record information is about each of the N types of image processing.
First type information corresponding to the type of image processing;
Second type information corresponding to the type of image processing selected next to the type of image processing;
First selection number information relating to the number of times an image processing of a type corresponding to the second type information is selected after an image processing of a type corresponding to the first type information;
The computer program according to claim 1, further comprising first related information associated with each other. In the first prediction process, the second type information associated with the first type information corresponding to the type information acquired by the type information acquisition process is the largest first selection count information. The computer program according to claim 2, wherein the first prediction is executed by specifying the second type information associated with the second type information. In the first prediction process, when the largest first selection number information associated with the first type information corresponding to the type information acquired by the type information acquisition process is less than a predetermined number, The computer program according to claim 2 or 3, wherein the first prediction is not executed. For each of the N types of image processing, second performance information generation processing for generating second performance information indicating that the type of image processing has been selected;
A second prediction process for executing a second prediction for predicting the type of image processing to be selected using the second performance information;
A third post-processing image data generation process for generating the third post-processing image data by performing the type of image processing predicted by the second prediction process on the processing target image data;
The computer program according to claim 1, further causing the computer to execute. The second performance information is about each of the N types of image processing.
Third type information corresponding to the type of image processing;
Second selection number information regarding the number of times the type of image processing has been selected;
The computer program according to claim 5, further comprising second related information associated with. 7. The computer according to claim 6, wherein in the second prediction process, the second prediction is executed by specifying the third type information associated with the largest second selection count information. program. The second selection number information is
A value obtained by multiplying a first value by the number of times selected for the first time when a plurality of types of image processing are sequentially selected with respect to the same image data to be processed;
A value obtained by multiplying the second value by the number of times selected after the second time when a plurality of types of image processing are sequentially selected with respect to the same image data to be processed;
Is equivalent to the sum of
The computer program according to claim 6 or 7, wherein the first value is larger than the second value. In the first prediction process, a third prediction for predicting a type of image processing to be selected by the user next to the type of image processing predicted by the second prediction process is performed using the first performance information. The computer program according to claim 5, wherein the computer program is executed. When the second performance information satisfies a predetermined condition, a pre-process divided image data generation process for generating the N divided image data by dividing the image data to be processed into the N pieces;
Each of the N types of image processing is executed on one different divided image data among the N divided image data to generate the post-processed divided image data. Generation process,
A combined image data generation process for generating one combined image data by combining the N post-processed divided image data generated by the divided image data generation process;
The computer program according to claim 5, further causing the computer to execute. The computer is mounted on a server communicably connected to an external device,
In the image data acquisition process, the image data to be processed is acquired by receiving the image data to be processed transmitted from the external device,
In the type information acquisition process, the type information is acquired by receiving the type information corresponding to the type of image processing selected in the external device and transmitted from the external device. ,
First transmission processing for transmitting the first post-processing image data generated by the first post-processing image data generation processing to the external device;
A second transmission process for transmitting the second post-processing image data generated by the second post-processing image data generation process to the external device;
The computer program according to claim 1, further causing the computer to execute. In the second transmission processing, the second post-processing image data is transmitted to the external device immediately after the second post-processing image data is generated by the second post-processing image data generation processing. The computer program according to claim 11. Image data acquisition means for acquiring image data to be processed;
Type information acquisition means for acquiring type information corresponding to the type of image processing selected by the user from among N types of image processing (N is an integer of 2 or more);
First post-processing image data generation means for generating first post-processing image data by executing image processing of a type corresponding to the type information acquired by the type information acquisition means on the processing target image data. When,
For each of the N types of image processing, a first result information generating unit that generates first result information indicating which type of image processing is selected next to the type of image processing;
A first prediction is executed for predicting, using the first performance information, the type of image processing to be selected next to the type of image processing corresponding to the type information acquired by the type information acquisition means. Prediction means,
Second post-processing image data generation means for generating the second post-processing image data by executing the type of image processing predicted by the first prediction means on the processing target image data;
An image processing apparatus comprising: An image processing instruction device for causing a server to execute image processing,
Image data acquisition means for acquiring image data to be processed;
Type information acquisition means for acquiring type information corresponding to the type of image processing selected by the user from among N types of image processing (N is an integer of 2 or more);
Instructs the server to execute image processing of a type corresponding to the type information acquired by the type information acquisition unit on the processing target image data to generate first processed image data. First instruction means;
First receiving means for receiving the first processed image data transmitted from the server;
For each of the N types of image processing, a first result information generating unit that generates first result information indicating which type of image processing is selected next to the type of image processing;
A first prediction is executed for predicting, using the first performance information, the type of image processing to be selected next to the type of image processing corresponding to the type information acquired by the type information acquisition means. Prediction means,
Second instruction means for instructing the server to perform image processing of the type predicted by the first prediction means on the image data to be processed to generate second processed image data;
Second receiving means for receiving the second processed image data transmitted from the server;
An image processing instruction apparatus.

Images