Patent Publication Number: US-11381737-B2

Title: Arithmetic device and arithmetic method

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a U.S. National Phase of International Patent Application No. PCT/JP2019/011394 filed on Mar. 19, 2019, which claims priority benefit of Japanese Patent Application No. JP 2018-063208 filed in the Japan Patent Office on Mar. 28, 2018. Each of the above-referenced applications is hereby incorporated herein by reference in its entirety. 
     TECHNICAL FIELD 
     The present technology relates to an arithmetic device, an arithmetic method, and a program, and more particularly, to an arithmetic device and the like for adjusting an image quality of a captured image captured by a camera to a predetermined image quality. 
     BACKGROUND ART 
     There are cases where it is required to adjust an image quality of a captured image captured by a camera to be close to a predetermined image quality, such as an image quality of a captured image captured by a camera of a different model from a camera used for imaging. Regarding adjustment of an image quality of a captured image, for example, Patent Document 1 discloses a camera calibration device that calculates a conversion parameter on the basis of a correspondence relationship of pixels between a captured image of a defined pattern of a geometric shape imaged by a camera and a reference image constituted by the defined pattern. 
     CITATION LIST 
     Patent Document 
     
         
         Patent Document 1: Japanese Patent Application Laid-Open No. 2000-350239 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     It is an object of the present technology to provide an arithmetic device, an arithmetic method, and a program that can acquire an image quality parameter group that adjusts an image quality of a captured image captured by a camera to be close to a predetermined image quality. 
     Solutions to Problems 
     A concept of the present technology lies in 
     an arithmetic device including: 
     an image quality evaluation value acquisition unit that obtains a first image quality evaluation value on the basis of developed image data obtained by performing development processing on captured image data; and 
     an image quality parameter group acquisition unit that obtains an image quality parameter group in the development processing to decrease a difference between the first image quality evaluation value and a second image quality evaluation value serving as a reference. 
     In the present technology, the image quality evaluation value acquisition unit obtains a first image quality evaluation value on the basis of developed image data obtained by performing development processing on captured image data. Then, the image quality parameter group acquisition unit obtains an image quality parameter group in the development processing to decrease a difference between the first image quality evaluation value and a second image quality evaluation value serving as a reference. For example, the second image quality evaluation value may be obtained on the basis of developed image data obtained by performing development processing on captured image data of a second camera of the same model as or a different model from a first camera for obtaining the captured image data. 
     As described above, in the present technology, an image quality parameter group in development processing is obtained to decrease a difference between a first image quality evaluation value obtained on the basis of developed image data obtained by performing development processing on captured image data and a second image quality evaluation value serving as a reference. It is therefore possible to obtain an image quality parameter group for adjusting an image quality of developed image data obtained by performing development processing on captured image data to be close to an image quality of target developed image data, and adjust an image quality of a captured image captured by a camera to be close to a predetermined image quality. In the present technology, not an evaluation function itself for evaluating the image quality but a difference from a predetermined evaluation value (second image quality evaluation value) is optimized to adjust an image quality to be close to a predetermined image quality. 
     Furthermore, another concept of the present technology lies in 
     an arithmetic device including: 
     an image quality evaluation value acquisition unit that obtains a plurality of first image quality evaluation values on the basis of a plurality of pieces of developed image data obtained by performing development processing on every one of a plurality of pieces of captured image data; and 
     an image quality parameter group acquisition unit that obtains an image quality parameter group in the development processing to decrease a difference between every one of the plurality of first image quality evaluation values and a second image quality evaluation value obtained on the basis of the plurality of first image quality evaluation values. 
     In the present technology, the image quality evaluation value acquisition unit obtains a plurality of first image quality evaluation values on the basis of a plurality of pieces of developed image data obtained by performing development processing on every one of a plurality of pieces of captured image data. Then, the image quality parameter group acquisition unit obtains an image quality parameter group in the development processing to decrease a difference between every one of a plurality of first image quality evaluation values and a second image quality evaluation value obtained on the basis of the plurality of first image quality evaluation values. For example, the image quality parameter group acquisition unit may obtain the image quality parameter group under a constraint condition that ensures a certain image quality. Furthermore, for example, the plurality of pieces of captured image data may be captured image data of a plurality of cameras, all of which are the same model, or all or some of which are different models. 
     As described above, in the present technology, an image quality parameter group in development processing is obtained to decrease a difference between a plurality of first image quality evaluation values obtained on the basis of a plurality of pieces of developed image data obtained by performing development processing on every one of a plurality of pieces of captured image data and a second image quality evaluation value obtained on the basis of the plurality of first image quality evaluation values. It is therefore possible to obtain an image quality parameter group for adjusting image qualities of a plurality of pieces of developed image data obtained by performing development processing on every one of a plurality of pieces of captured image data to be close to each other, and adjust an image quality of a captured image captured by a camera to be close to a predetermined image quality. In the present technology, not an evaluation function itself for evaluating the image quality but a difference from a predetermined evaluation value (second image quality evaluation value) is optimized to adjust an image quality to be close to a predetermined image quality. 
     Effects of the Invention 
     According to the present technology, an image quality of a captured image captured by a camera can be adjusted to be close to a predetermined image quality. Note that the effects described here are not necessarily restrictive, and the effects of the invention may be any one of the effects described in the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a configuration example of a camera image quality adjustment system as a first embodiment. 
         FIGS. 2A and 2B  are diagrams illustrating how image qualities are adjusted between models and between individuals. 
         FIG. 3  is a block diagram illustrating a configuration example of a camera. 
         FIG. 4  is a block diagram illustrating a configuration example of a development processing unit. 
         FIG. 5  is a diagram for illustrating optimization processing of an image quality parameter group. 
         FIG. 6  is a diagram for illustrating convergence conditions in an image quality parameter automatic tuning system. 
         FIG. 7  is a block diagram illustrating a configuration example of a camera image quality adjustment system as a second embodiment. 
         FIGS. 8A and 8B  are diagrams illustrating how image qualities are adjusted between models and between individuals. 
         FIG. 9  is a diagram for illustrating optimization processing of an image quality parameter group. 
         FIG. 10  is a diagram schematically illustrating a first business model using the present technology. 
         FIG. 11  is a diagram schematically illustrating a second business model using the present technology. 
         FIG. 12  is a diagram schematically illustrating a third business model using the present technology. 
         FIG. 13  is a diagram schematically illustrating a fourth business model using the present technology. 
         FIG. 14  is a block diagram illustrating a configuration example of a personal computer. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Modes for carrying out the invention (hereinafter referred to as “embodiments”) will be described below. Note that the description will be made in the order below. 
     1. First embodiment 
     2. Second embodiment 
     3. Business models 
     4. Modified example 
     1. First Embodiment 
     [Camera Image Quality Adjustment System] 
       FIG. 1  illustrates a configuration example of a camera image quality adjustment system  100  as a first embodiment. The camera image quality adjustment system  100  includes a camera  101 , an image quality evaluation system  102 , an image quality difference index calculator  103 , and an image quality parameter automatic tuning system  104 . The camera image quality adjustment system  100  is a system for adjusting an image quality of a developed image output from the camera  101  to be close to an image quality of a developed image of a target camera. With this system, an image quality of a captured image captured by a camera can be adjusted to be close to a predetermined image quality. For example, in a case where a plurality of cameras is used to capture images, it may not be possible to create works with a sense of unity because of different image qualities of the corresponding cameras, but such a problem can be solved by the present technology in which image qualities can be adjusted between a plurality of cameras. 
       FIG. 2A  illustrates how image qualities are adjusted between models. The illustrated example illustrates source cameras  10   s   1  and  10   s   2  in addition to a target camera  10   t , in which image qualities of the source cameras  10   s   1  and  10   s   2  are adjusted to be close to an image quality of the target camera  10   t . Here, the cameras  10   t ,  10   s   1 , and  10   s   2  are cameras of different models, and a variation between models and a variation between individuals can be minimized. 
     Furthermore,  FIG. 2B  illustrates how image qualities are adjusted between individuals. The illustrated example illustrates source cameras  20   s   1  and  20   s   2  in addition to a target camera  20   t , in which image qualities of the source cameras  20   s   1  and  20   s   2  are adjusted to be close to an image quality of the target camera  20   t . Here, the cameras  20   t ,  20   s   1 , and  20   s   2  are cameras of the same model, and a variation between individuals can be minimized. Note that the examples in  FIGS. 2A and 2B  illustrate a case where the number of source cameras is two, and the same applies to a case where the number of source cameras is three or more. 
     Returning to  FIG. 1 , the camera  101  corresponds to a source camera in  FIGS. 2A and 2B .  FIG. 3  illustrates a configuration example of the camera  101 . The camera  101  includes an image input unit  111 , an image holding unit (memory)  112 , a development processing unit  113 , a development parameter holding unit (memory)  114 , an image information recording unit  115 , an external input/output control unit  116 , and a user operation unit  117 . 
     The image input unit  111  includes a lens, an imager, and the like, and the imager outputs raw image data as captured image data. The image holding unit  112  holds the raw image data output from the imager. Here, a chart (Macbeth Color Checker/ISO-12233 resolution test chart, or the like) for evaluating each of color reproducibility, resolution, noise feeling, and the like is imaged by the imager, and raw image data of each of these charts is also held in the image holding unit  112 . 
     The development processing unit  113  performs development processing on the raw image data held in the image holding unit  112 , and outputs developed image data. The image holding unit  112  also holds the developed image data output from the development processing unit  113 . The development parameter holding unit  114  holds a development image quality parameter group used by the development processing unit  113 . Here, in addition to a fixed image quality parameter group used in a normal mode, the development parameter holding unit  114  can hold a special image quality parameter group used for image quality adjustment with another camera in a special mode in this embodiment. 
     Note that it is expected that there is a plurality of other cameras with which image quality adjustment is to be performed, and the development parameter holding unit  114  can hold not only one but also a plurality of special image quality parameter groups. At this time, each of the special image quality parameter groups is held in association with, by model name, model number, or the like, the other cameras with which image quality adjustment is to be performed. 
       FIG. 4  illustrates a configuration example of the development processing unit  113 . This development processing unit  113  includes an offset removal/white balance unit, a noise reduction unit, a Bayer interpolation processing unit, a color space conversion unit, a gamma processing unit, an RGB/YCC conversion unit, an unsharp mask unit, a JPEG conversion unit, and the like. Note that the illustrated example illustrates only typical processing blocks, and there are many other processing blocks in an actual camera. Furthermore, depending on the camera, some of the processing blocks in the illustrated example may not be used in some cases. 
     Image quality parameters (development parameters) are used for processing in processing blocks, and affect color reproducibility, resolution, noise feeling, and the like of developed image data, and change an image quality of the developed image data. The image quality parameters exist for most processing blocks, and change intricately depending on characteristics and a mode of a camera. The development parameter holding unit  114  holds all of them, and switches between image quality parameter groups to be passed to the development processing unit  113  in accordance with the mode. The development parameter holding unit  114  can additionally hold a special image quality parameter group obtained through an external input/output device or the like, in addition to a fixed image quality parameter group that is fixedly held. 
     A plurality of special image quality parameter groups can be held. In automatic tuning processing, an image quality parameter group passed from outside is used to perform development, and a result of the development is sent to an external device for optimization processing. The optimized image quality parameter group is held by the development parameter holding unit  114  as a special image quality parameter group, which is used in a case where a user selects the special mode. Note that a plurality of special modes may be provided. 
     Returning to  FIG. 3 , the image information recording unit  115  records raw image data and developed image data held in the image holding unit  112  on a removable medium such as a memory stick or an SD card as needed. Furthermore, the image information recording unit  115  records an image quality parameter group held in the development parameter holding unit  114  on a removable medium such as a memory stick or an SD card as needed. 
     The external input/output control unit  116  accesses the image holding unit  112 , and reads out and transmits the raw image data or the developed image data to external equipment by wired communication or wireless communication. Furthermore, the external input/output control unit  116  receives a special image quality parameter group from external equipment by wired communication or wireless communication, and writes the special image quality parameter group on the development parameter holding unit  114 . In this case, a new special image quality parameter group can be written on the development parameter holding unit  114 , or can be written over the existing special image quality parameter group to change the special image quality parameter group. 
     The user operation unit  117  includes a button, a touch panel, and the like, and allows a user to perform a variety of operations. For example, a user can perform an operation to set an operation mode of the development processing unit  113  to the normal mode or the special mode. Furthermore, for example, a user can perform an operation to transmit raw image data or developed image data held in the image holding unit  112  to external equipment. Furthermore, for example, the user can perform an operation to receive from external equipment and write on the development parameter holding unit  114 . Moreover, for example, a user can perform an operation to delete a special image quality parameter group held in the development parameter holding unit  114 . 
     Returning to  FIG. 1 , in the automatic tuning processing for adjusting the image quality of the camera  101 , which is a source camera, to an image quality of a target camera, the image quality evaluation system  102  receives a plurality of pieces of developed image data for image quality evaluation from the camera  101 . Then, the image quality evaluation system  102  evaluates each of color reproducibility, resolution, noise feeling, and the like on the basis of the plurality of pieces of developed image data to obtain an image quality evaluation value group. 
     The image quality difference index calculator  103  calculates an image quality difference index that indicates a difference between the image quality evaluation value group obtained by the image quality evaluation system  102  and a reference image quality evaluation value group. Here, an image quality evaluation value group for the target camera is used as the reference image quality evaluation value group. In this case, the image quality evaluation value group for the target camera is acquired by evaluating each of color reproducibility, resolution, noise feeling, and the like on the basis of a plurality of pieces of developed image data for image quality evaluation from the target camera. 
     The image quality parameter automatic tuning system  104  uses a non-linear optimization algorithm such as genetic algorithms (GA) or simulated annealing (SA) to calculate an image quality parameter group in the development processing unit  113  of the camera  101  on the basis of an image quality difference index calculated by the image quality difference index calculator  103  to decrease the difference. The image quality parameter group calculated by the image quality parameter automatic tuning system  104  is reflected in the development processing unit  113  of the camera  101 . The image quality parameter automatic tuning system  104  may use a neural network, deep learning, or the like. 
     The image quality parameter automatic tuning system  104  repeatedly obtains an image quality parameter group on the basis of a new image quality difference index, optimizes the image quality parameter group, and calculates an optimum image quality parameter group. Finally, the camera  101  holds, in the development parameter holding unit  114 , the optimum image quality parameter group calculated by the image quality parameter automatic tuning system  104  as a special image quality parameter group, and uses the special image quality parameter group in the special mode for adjusting the image quality to that of the target camera. 
     The above-described optimization processing of the image quality parameter group will be further described with reference to  FIG. 5 . An image quality parameter group P is set in the development processing unit  113 , and in that state, development processing is performed on a plurality of pieces of raw image data for image quality evaluation to obtain a plurality of pieces of developed image data for image quality evaluation. Then, on the basis of this plurality of pieces of developed image data, the image quality evaluation system  102  evaluates each of color reproducibility, resolution, noise feeling, and the like to obtain an image quality evaluation value group e. 
     The image quality evaluation value group e is supplied to the image quality difference index calculator  103 . Furthermore, the image quality difference index calculator  103  is also supplied with an image quality evaluation index weight coefficient w. The image quality evaluation index weight coefficient w is a coefficient for weighting each image quality evaluation value in accordance with importance. For example, in a case where the resolution is important and the noise feeling is not important, w for the resolution is increased and w for the noise feeling is decreased. With this arrangement, the difference in image quality can be minimized with a focus on the resolution. Note that, the image quality evaluation index weight coefficient w is used in the example described here, but it is also conceivable that this coefficient is not used in some cases. 
     The image quality difference index calculator  103  calculates an image quality difference index Q on the basis of, for example, the following Mathematical Formula (1). In this Mathematical Formula (1), “e_src(j)” indicates an image quality evaluation value group of a source camera, that is, elements of the image quality evaluation value group e obtained by the image evaluation system  102 , and “e_target(j)” indicates elements of an image quality evaluation value group of a target camera. Furthermore, M indicates the number of elements of the image quality evaluation value group. Note that the mathematical formula for obtaining the image quality difference index Q is not limited to Mathematical Formula (1). For example, it is also conceivable that a sum of difference absolute values is used as the image quality difference index Q. 
     
       
         
           
             
               
                 
                   
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     The image quality difference index Q is supplied to the image quality parameter automatic tuning system  104 . Furthermore, the image quality parameter automatic tuning system  104  is also supplied with the image quality parameter group P. The image quality parameter automatic tuning system  104  calculates a next image quality parameter group P′ on the basis of P and Q. The image quality parameter automatic tuning system  104  repeats this processing to derive an image quality parameter group P that optimizes the image quality difference index Q. Note that the image quality parameter automatic tuning system  104  may reference past P and Q. 
       FIG. 6  illustrates an example of convergence conditions in the image quality parameter automatic tuning system  104 . As the convergence conditions, for example, the following (1) to (4) can be considered. (1) When the number of repetitions exceeds a certain number of times, the processing ends. (2) When a convergence speed of the image quality difference index Q falls below a designated value, the processing ends. (3) When the image quality difference index Q falls below a designated value, the processing ends. Furthermore, it is also conceivable that a combination of (1) to (3) is set as (4). For example, in a case where the processing is repeated according to (3), there is a possibility that the processing may not end, so when the number of times designated in (1) is exceeded, the processing is forcibly ended even if (3) is not satisfied. In that case, it is necessary to return, as information, the condition under which the processing has ended. 
     As described above, in the camera image quality adjustment system  100  illustrated in  FIG. 1 , the image quality parameter automatic tuning system  104  obtains an image quality parameter group that optimizes the image quality difference index obtained from the image quality evaluation value group of the camera  101  and the image quality evaluation value group of the target camera. It is therefore possible to easily obtain an image quality parameter for adjusting the image quality of the camera  101  as the source camera to be close to the image quality of the target camera. 
     Note that, the camera image quality adjustment system  100  in  FIG. 1  illustrates an example in which the development processing unit  113  included in the camera  101  is used in the automatic tuning processing. Although a detailed description is omitted, the image quality evaluation system itself includes a development processing unit, and it is also conceivable that, in automatic tuning processing, the development processing unit is used to perform development processing and optimize the image quality parameter group. In this case, a plurality of pieces of raw image data for image quality evaluation is supplied from the camera  101  to the image quality evaluation system  102 . 
     Furthermore, in the case of “between models” illustrated in  FIG. 2A , in actual operation, it is also possible to perform optimization processing on all (or typical) combinations in advance, and provide the image quality parameter group as it is at the time of provision to a user. However, in the case of this method, although a variation between camera models can be minimized, a variation between individuals is excluded from the optimization. 
     Furthermore, as described above, in the case of the method of adjusting, to the image quality of the target camera, the image qualities of other source cameras, the optimization may not yield a satisfactory result depending on settings of the target camera in some cases. For example, there is a case where the target camera is the latest model, and its level cannot be reached no matter what image quality parameter group the other source cameras may take. In order to prevent such adverse effects, for example, in a case of image quality adjustment among three cameras, a method can be considered in which, for example, optimization is performed with each of the three cameras set as target cameras, and settings of a target camera with the best result (small Q) among the three cameras are used as a final result. 
     2. Second Embodiment 
     [Camera Image Quality Adjustment System] 
       FIG. 7  illustrates a configuration example of a camera image quality adjustment system  200  as a second embodiment. The camera image quality adjustment system  200  includes cameras  201 - 1 ,  201 - 2 , and  201 - 3 , an image quality evaluation system  202 , an image quality difference index calculator  203 , and an image quality parameter automatic tuning system  204 . The camera image quality adjustment system  200  is a system for adjusting image qualities of developed images output from the cameras  201 - 1 ,  201 - 2 , and  201 - 3  to be close to each other. With this system, an image quality of a captured image captured by a camera can be adjusted to be close to a predetermined image quality. For example, in a case where a plurality of cameras is used to capture images, it may not be possible to create works with a sense of unity because of different image qualities of the corresponding cameras, but such a problem can be solved by the present technology in which image qualities can be adjusted between a plurality of cameras. 
     The camera image quality adjustment system  200  minimizes a difference in image quality as a whole by mutually changing image quality parameter groups without setting a specific target camera. For example, in a case where the target camera is the latest model and its image quality is better than any other cameras, no matter how their image quality parameter groups are changed, the other cameras cannot create the image quality of the target camera. In the camera image quality adjustment system  200 , the image qualities of all the cameras “approach each other”, and this increases a degree of optimization of the difference in image quality. Unlike the case where a target camera is set, the camera image quality adjustment system  200  does not perform optimization for each camera, but performs optimization of the image quality parameter groups of all the cameras at the same time. 
       FIG. 8A  illustrates how image qualities are adjusted between models. The illustrated example illustrates source cameras  30   s   1 ,  30   s   2 , and  30   s   3 , in which image qualities of these source cameras  30   s   1 ,  30   s   2 , and  30   s   3  are adjusted to be close to each other under a constraint condition L. Here, the source cameras  30   s   1 ,  30   s   2 , and  30   s   3  are cameras of different models, and a variation between models and a variation between individuals can be minimized. 
     Furthermore,  FIG. 8B  illustrates how image qualities are adjusted between individuals. The illustrated example illustrates source cameras  40   s   1 ,  40   s   2 , and  40   s   3 , in which image qualities of these source cameras  40   s   1 ,  40   s   2 , and  40   s   3  are adjusted to be close to each other under a constraint condition L. Here, the cameras  40   s   1 ,  40   s   2 , and  40   s   3  are cameras of the same model, and a variation between individuals can be minimized. Note that the examples in  FIGS. 8A and 8B  illustrate a case where the number of source cameras is three, and the same applies to a case where the number of source cameras is four or more. 
     Returning to  FIG. 7 , the cameras  201 - 1 ,  201 - 2 , and  201 - 3  correspond to the three source cameras in  FIGS. 8A and 8B . Although a detailed description is omitted, each camera has a configuration similar to that of the camera  101  illustrated in  FIG. 1  described above (see  FIG. 3 ). 
     In automatic tuning processing for adjusting the image qualities of the cameras  201 - 1 ,  201 - 2 , and  201 - 3 , the image quality evaluation system  202  receives a plurality of pieces of developed image data for image quality evaluation from each of the cameras  201 - 1 ,  201 - 2 , and  201 - 3 . Then, the image quality evaluation system  202  evaluates each of color reproducibility, resolution, noise feeling, and the like for each camera on the basis of the plurality of pieces of developed image data for image quality evaluation to obtain an image quality evaluation value group. 
     The image quality difference index calculator  203  calculates an image quality difference index that indicates a difference between the image quality evaluation value group of each camera obtained by the image quality evaluation system  202  and a reference image quality evaluation value group. Here, the reference image quality evaluation value group is obtained on the basis of the image quality evaluation value group of each camera. For example, an average value of image quality evaluation values of each camera is obtained for each image quality evaluation value and used as the reference image quality evaluation value. 
     The image quality parameter automatic tuning system  204  uses a non-linear optimization algorithm such as genetic algorithms (GA) or simulated annealing (SA) to calculate an image quality parameter group in the development processing unit  113  in each of the cameras  201 - 1 ,  201 - 2 , and  201 - 3  on the basis of an image quality difference index calculated by the image quality difference index calculator  203  to decrease the difference. The image quality parameter group of each camera obtained by the image quality parameter automatic tuning system  204  is reflected in the development processing unit  113  in each of the cameras  201 - 1 ,  201 - 2 , and  201 - 3 . 
     The image quality parameter automatic tuning system  204  repeatedly obtains an image quality parameter group of each camera on the basis of a new image quality difference index, optimizes the image quality parameter group of each camera, and calculates an optimum image quality parameter group. Finally, the cameras  201 - 1 ,  201 - 2 , and  201 - 3  hold, in the development parameter holding unit  114  of each camera, the optimum image quality parameter group of each camera calculated by the image quality parameter automatic tuning system  204  as a special image quality parameter group, and use the special image quality parameter group in the special mode for image quality adjustment. 
     Note that the above-described automatic tuning processing for adjusting the image qualities of the cameras  201 - 1 ,  201 - 2 , and  201 - 3  is performed under a constraint condition L that ensures a certain image quality. This is because, without such a constraint condition L, the difference in image quality may become smaller, but there is a possibility that the image quality itself is not guaranteed. For example, in a case of an image quality parameter for “painting the whole image in black”, all images are in solid black, so there is no difference in image quality. The image quality difference index therefore becomes 0, which is the best result. However, such a result does not meet users&#39; expectations. A constraint condition L is for ensuring a certain image quality, and, for example, “all image quality evaluation values are equal to or greater than a designated value” can be considered. 
     The above-described optimization processing of the image quality parameter group will be further described with reference to  FIG. 9 . Image quality parameter groups P 1 , P 2 , and P 3  are set in the development processing units  113  of the corresponding cameras, and in that state, development processing is performed on a plurality of pieces of raw image data for image quality evaluation to obtain a plurality of pieces of developed image data for image quality evaluation. Then, for each camera, on the basis of the plurality of pieces of developed image data for image quality evaluation, the image quality evaluation system  202  evaluates each of color reproducibility, resolution, noise feeling, and the like to obtain image quality evaluation value groups e 1 , e 2 , and e 3 . 
     These image quality evaluation value groups e 1 , e 2 , and e 3  are supplied to the image quality difference index calculator  203 . Furthermore, the image quality difference index calculator  203  is also supplied with an image quality evaluation index weight coefficient w. The image quality evaluation index weight coefficient w is a coefficient for weighting each image quality evaluation value in accordance with importance. For example, in a case where the resolution is important and the noise feeling is not important, w for the resolution is increased and w for the noise feeling is decreased. With this arrangement, the difference in image quality can be minimized with a focus on the resolution. Note that, the image quality evaluation index weight coefficient w is used in the example described here, but it is also conceivable that this coefficient is not used in some cases. 
     The image quality difference index calculator  203  calculates an image quality difference index Q on the basis of, for example, the following Mathematical Formula (2). In this Mathematical Formula (2), “e_src(i) (j)” indicates an image quality evaluation value group of a source camera (i), that is, elements of the image quality evaluation value group e of the source camera (i) obtained by the image evaluation system  202 , and “e bar (j)” indicates elements of a reference image quality evaluation value group. Furthermore, M indicates the number of elements of the image quality evaluation value group. Furthermore, N indicates the number of cameras, and N=3 here. Note that the mathematical formula for obtaining the image quality difference index Q is not limited to Mathematical Formula (2). 
     
       
         
           
             
               
                 
                   
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     The image quality difference index Q is supplied to the image quality parameter automatic tuning system  204 . Furthermore, the image quality parameter automatic tuning system  204  is also supplied with the image quality parameter groups P 1 , P 2 , and P 3  of the corresponding cameras. The image quality parameter automatic tuning system  204  calculates next image quality parameter groups P 1 ′, P 2 ′, and P 3 ′ on the basis of P 1 , P 2 , P 3 , and Q. The image quality parameter automatic tuning system  204  repeats this processing to derive image quality parameters P 1 , P 2 , and P 3  that optimize the image quality difference index Q under a constraint condition L that ensures a certain image quality. Note that the image quality parameter automatic tuning system  204  may reference past P and Q. 
     Note that, although a detailed description is omitted, convergence conditions in the image quality parameter automatic tuning system  204  are similar to the convergence conditions in the image quality parameter automatic tuning system  104  illustrated in  FIG. 1  described above (see  FIG. 6 ). 
     As described above, in the camera image quality adjustment system  200  illustrated in  FIG. 7 , the image quality parameter automatic tuning system  204  obtains an image quality parameter group of each camera that optimizes the image quality difference index obtained from the image quality evaluation value groups of the cameras  201 - 1 ,  201 - 2 , and  201 - 3  and the reference image quality evaluation value group obtained on the basis of these image quality evaluation value groups. It is therefore possible to easily obtain an image quality parameter for adjusting the image qualities of the cameras  201 - 1 ,  201 - 2 , and  201 - 3  to be close to each other. 
     Note that, the camera image quality adjustment system  200  in  FIG. 7  illustrates an example in which the development processing units  113  included in the cameras  201 - 1 ,  201 - 2 , and  201 - 3  are used in the automatic tuning processing. Although a detailed description is omitted, the image quality evaluation system itself includes a development processing unit, and it is also conceivable that, in automatic tuning processing, the development processing unit is used to perform development processing and optimize the image quality parameter group. In this case, a plurality of pieces of raw image data for image quality evaluation is supplied from the cameras  201 - 1 ,  201 - 2 , and  201 - 3  to the image quality evaluation system  202 . 
     Furthermore, in the case of “between models” illustrated in  FIG. 8A , in actual operation, it is also possible to perform optimization processing on all (or typical) combinations in advance, and provide the image quality parameter group as it is at the time of provision to a user. However, in the case of this method, although a variation between camera models can be minimized, a variation between individuals is excluded from the optimization. 
     3. Business Models 
     Next, first to fourth business models using the present technology will be described. 
     “First Business Model” 
       FIG. 10  schematically illustrates the first business model. 
     (1) A user takes or mails, to a center, a group of cameras on which image quality adjustment is to be performed. In this case, the user does not have to, but can specify which is to be a target camera (Target) and which is to be a source camera (Src). 
     (2) The center images a chart (Macbeth Color Checker/ISO-12233 resolution test chart, or the like) for evaluating each of color reproducibility, resolution, noise feeling, and the like with each camera to obtain a plurality of pieces of raw image data for image quality adjustment. 
     (3) The center optimizes an image quality parameter group of each camera on the basis of the plurality of pieces of raw image data for image quality adjustment for each camera. 
     (4) The center sends, to the user, each camera on which the optimized image quality parameter group has been installed as a special image quality parameter group. 
     (5) The user can use the special image quality parameter group by switching each camera to a special mode, and can capture images with the image qualities of the corresponding cameras being close to each other. 
     In this first business model, the center images a chart and performs optimization, and a variation between individuals can also be optimized. Thus, the first business model can be used for both image quality adjustment between models and image quality adjustment between individuals. 
     “Second Business Model” 
       FIG. 11  schematically illustrates the second business model. 
     (1) A user selects, on the Web, a combination of models of camera groups on which image quality adjustment is to be performed, and transmits data regarding the combination to a data center or a server site. In this case, a combination in which a target camera (Target) and a source camera (Src) are specified may be used. 
     (2) The data center or the server site may generate and save in advance an optimum image quality parameter group of each camera in all or typical combinations of models. The data center or the server site searches for and extracts the optimum image quality parameter group of each camera that matches the data regarding the combination sent from the user. 
     (3) The data center or the server site transmits the optimum image quality parameter group of each camera to the user. 
     (4) The user installs, on each camera, the optimum image quality parameter group of each camera received from the data center or the server site as a special image quality parameter group. 
     (5) The user can use the special image quality parameter group by switching each camera to a special mode, and can capture images with the image qualities of the corresponding cameras being close to each other. 
     In this second business model, there is no information regarding the individual cameras of the user, and it is not possible to optimize a variation between individuals. Thus, the second business model can be used only for image quality adjustment between models. 
     “Third Business Model” 
       FIG. 12  schematically illustrates the third business model. 
     (1) A user images a commercially available chart (Macbeth Color Checker/ISO-12233 resolution test chart, or the like) with each camera in a camera group on which image quality adjustment is to be performed. 
     (2) The user transmits raw image data captured by each camera to a data center or a server site on the Web. In this case, the user does not have to, but can specify which is to be a target camera (Target) and which is to be a source camera (Src). 
     (3) The user performs operation on the Web to cause the image quality parameter group of each camera to be optimized on the basis of a plurality of pieces of raw image data for image quality adjustment for each camera at the data center or the server site. In this case, the user does not need to be aware of executing the optimization, and the optimization may be automatically executed by the user transmitting the raw image data of each camera to the data center or the server site. 
     (4) The data center or the server site transmits the optimum image quality parameter group of each camera to the user. 
     (5) The user installs, on each camera, the optimum image quality parameter group of each camera received from the data center or the server site as a special image quality parameter group. 
     (6) The user can use the special image quality parameter group by switching each camera to a special mode, and can capture images with the image qualities of the corresponding cameras being close to each other. 
     In this third business model, there is information regarding the individual cameras of the user, and a variation between individuals can also be optimized. Thus, the third business model can be used for both image quality adjustment between models and image quality adjustment between individual. 
     Note that the Web may be used to sequentially perform designation, or a dedicated application or a dedicated web application may be used for automatic execution of (2) to (5). Furthermore, the above-described chart to be used is a commercially available chart, but it is assumed that, in a case where the user is a professional, an owned or rented chart is used. Furthermore, it is also possible to download a chart on the Web, print the chart or display the chart on a screen, and image the chart as a substitute. However, this is highly dependent on a hardware environment of the user and the like, and there is a possibility that accurate results cannot be obtained. 
     “Fourth Business Model” 
       FIG. 13  schematically illustrates the fourth business model. 
     (1) A user selects, on the Web, a combination of models of camera groups on which image quality adjustment is to be performed, and transmits data regarding the combination to a data center or a server site. At this time, the data is transmitted together with auxiliary information held by each camera at the time of factory shipment. In this case, a combination in which a target camera (Target) and a source camera (Src) are specified may be used. 
     The auxiliary information includes an individual number and information regarding a variation between individuals. The auxiliary information includes an individual number as well as information regarding a variation in image quality and the like. For example, a direction and amount of shift such as how color reproducibility has shifted compared to a model average, and a magnitude of a noise amount compared to a model average may be included. 
     It is difficult to correct all variations between individuals at the time of shipment due to cost. It is therefore assumed that cameras are provided with, at the time of shipment, auxiliary information including sufficient information to be used as an additional service afterward. Note that it is also conceivable that the data center or the server site holds additional information corresponding to auxiliary information transmitted by a user and forms information regarding a variation between individuals by associating the additional information with the individual number. There is also a method in which raw image data obtained by imaging a chart at the time of factory shipment is kept as additional information in the data center or the server site. 
     (2) The data center or the server site uses information regarding the combination of models and the auxiliary information (including the information regarding the variation between individuals) to optimize an image quality parameter group of each camera. At this time, the auxiliary information is used, and this allows the variation between individuals to be corrected at the same time. At this time, it is also possible to additionally use information held by the data center or the server site in association with the individual number. Note that this optimization at the data center or the server site may be executed by the user performing operation on the Web, or may be automatically executed by the user transmitting the information regarding the combination of models and the auxiliary information to the data center or the server site. 
     (3) The data center or the server site transmits the optimum image quality parameter group of each camera to the user. 
     (4) The user installs, on each camera, the optimum image quality parameter group of each camera received from the data center or the server site as a special image quality parameter group. 
     (5) The user can use the special image quality parameter group by switching each camera to a special mode, and can capture images with the image qualities of the corresponding cameras being close to each other. 
     In this fourth business model, in addition to the information regarding the combination of models, the auxiliary information including the information regarding the variation between individuals is transmitted to the data center or the server site, and the variation between individuals can also be optimized. Thus, the fourth business model can be used for both image quality adjustment between models and image quality adjustment between individual. 
     4. Modified Example 
     Note that, in the first and second embodiments described above, the processing by the image quality evaluation systems  102  and  202 , the image quality difference index calculators  103  and  203 , and the image quality parameter automatic tuning systems  104  and  204  can be executed by hardware, or by software. In a case where the series of processing is executed by software, a program constituting the software is installed on a computer. Here, the computer includes a computer incorporated in dedicated hardware, a general-purpose personal computer capable of executing various functions with various programs installed therein, or the like. 
     In  FIG. 14 , a central processing unit (CPU)  701  of a personal computer  700  executes various types of processing according to a program stored in a read only memory (ROM)  702  or a program loaded from a storage unit  713  into a random access memory (RAM)  703 . The RAM  703  also stores, as appropriate, data or the like necessary for the CPU  701  to execute the various types of processing. 
     The CPU  701 , the ROM  702 , and the RAM  703  are connected to each other via a bus  704 . An input/output interface  710  is also connected to the bus  704 . 
     The input/output interface  710  is connected with an input unit  711  including a keyboard and a mouse, an output unit  712  including a speaker and a display including a cathode ray tube (CRT) and a liquid crystal display (LCD), the storage unit  713  constituted by a hard disk and the like, and a communication unit  714  constituted by a modem and the like. The communication unit  714  performs communication processing via a network including the Internet. 
     The input/output interface  710  is also connected with a drive  715  as needed, where a removable medium  721  such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory is mounted as appropriate, and a computer program read from them is installed on the storage unit  713  as needed. 
     Furthermore, the preferred embodiments of the present disclosure have been described in detail with reference to the accompanying drawings, but the technical scope of the present disclosure is not limited to such an example. It is obvious that a person having ordinary knowledge in the technical field of the present disclosure can come up with various changes and modifications within the scope of the technical idea described in the claims, and such various changes and modifications are naturally understood to belong to the technical scope of the present disclosure. 
     Furthermore, the present technology may also have the following configurations. 
     (1) An arithmetic device including: 
     an image quality evaluation value acquisition unit that obtains a first image quality evaluation value on the basis of developed image data obtained by performing development processing on captured image data; and 
     an image quality parameter group acquisition unit that obtains an image quality parameter group in the development processing to decrease a difference between the first image quality evaluation value and a second image quality evaluation value serving as a reference. 
     (2) The arithmetic device according to (1), in which 
     the second image quality evaluation value is obtained on the basis of developed image data obtained by performing development processing on captured image data of a second camera of the same model as or a different model from a first camera for obtaining the captured image data. 
     (3) The arithmetic device according to (1) or (2), in which 
     the image quality evaluation value acquisition unit repeatedly obtains the developed image data by performing development processing on the basis of the image quality parameter group repeatedly obtained by the image quality parameter group acquisition unit. 
     (4) The arithmetic device according to any one of (1) to (3), in which 
     the image quality parameter group is parameters that affect an image quality of the developed image data. 
     (5) The arithmetic device according to any one of (1) to (4), in which 
     the first image quality evaluation value is an evaluation value for evaluating color reproducibility, resolution, and noise feeling of the developed image data. 
     (6) An arithmetic method including the steps of: 
     obtaining a first image quality evaluation value on the basis of developed image data obtained by performing development processing on captured image data; and 
     obtaining an image quality parameter group in the development processing to decrease a difference between the first image quality evaluation value and a second image quality evaluation value serving as a reference. 
     (7) A program that causes a computer to function as: 
     image quality evaluation value acquisition means that obtains a first image quality evaluation value on the basis of developed image data obtained by performing development processing on captured image data; and 
     image quality parameter group acquisition means that obtains an image quality parameter group in the development processing to decrease a difference between the first image quality evaluation value and a second image quality evaluation value serving as a reference. 
     (8) An arithmetic device including: 
     an image quality evaluation value acquisition unit that obtains a plurality of first image quality evaluation values on the basis of a plurality of pieces of developed image data obtained by performing development processing on every one of a plurality of pieces of captured image data; and 
     an image quality parameter group acquisition unit that obtains an image quality parameter group in the development processing to decrease a difference between every one of the plurality of first image quality evaluation values and a second image quality evaluation value obtained on the basis of the plurality of first image quality evaluation values. 
     (9) The arithmetic device according to (8), in which 
     the image quality parameter group acquisition unit obtains the image quality parameter group under a constraint condition that ensures a certain image quality. 
     (10) The arithmetic device according to (8) or (9), in which 
     the plurality of pieces of captured image data is captured image data of a plurality of cameras, all of which are the same model, or all or some of which are different models. 
     (11) An arithmetic method including the steps of: 
     obtaining a plurality of first image quality evaluation values on the basis of a plurality of pieces of developed image data obtained by performing development processing on every one of a plurality of pieces of captured image data; and 
     obtaining an image quality parameter group in the development processing on the plurality of pieces of captured image data to decrease a difference between every one of the plurality of first image quality evaluation values and a second image quality evaluation value obtained on the basis of the plurality of first image quality evaluation values. 
     (12) A program that causes a computer to function as: 
     image quality evaluation value acquisition means that obtains a first image quality evaluation value on the basis of developed image data obtained by performing development processing on captured image data; and 
     image quality parameter group acquisition means that obtains an image quality parameter group in the development processing to decrease a difference between every one of the plurality of first image quality evaluation values and a second image quality evaluation value obtained on the basis of the plurality of first image quality evaluation values. 
     REFERENCE SIGNS LIST 
     
         
           100 ,  200  Camera image quality adjustment system 
           101 ,  201 - 1 ,  201 - 2 ,  201 - 3  Camera 
           102 ,  202  Image quality evaluation system 
           103 ,  023  Image quality difference index calculator 
           104 ,  204  Image quality parameter automatic tuning system 
           111  Image input unit 
           112  Image holding unit (memory) 
           113  Development processing unit 
           114  Imaging parameter holding unit (memory) 
           115  Image information recording unit 
           116  External input/output control unit 
           117  User operation unit