Patent Publication Number: US-10764508-B2

Title: Image processing apparatus, control method thereof, and storage medium

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The aspect of the embodiment relates to an image processing apparatus, a control method of the image processing apparatus, and a storage medium, and particularly relates to gradation conversion processing of a moving image signal. 
     Description of the Related Art 
     In a monitoring system, a monitoring camera distributes an entire image through a network, so that a user can monitor the distributed image. Further, there is also provided a monitoring system which enables a user to monitor a part of an image cut out (cropped) from the distributed image. The user can freely specify a region to be cropped. At this time, if the distributed image presents a scene with a great luminance difference, overexposure or underexposure may occur in the image monitored by the user. 
     Conventionally, as a method of executing gradation conversion processing for solving the above-described issue, there has been known high dynamic range processing (hereinafter, called as “HDR processing”) for acquiring a high dynamic range image by combining a plurality of images obtained in different exposure conditions. However, as a difference in imaging timing between the plurality of images arises, this method is thought to be unsuitable for capturing a scene including moving objects. Further, there may be an issue in that a luminance difference occurs in the image according to a position of the moving object, and an issue in that a double image is generated because of failure in image composition. Furthermore, there is also an issue in that a frame rate decreases because image composition is executed by acquiring a plurality of images. 
     With respect to the above-described issues, Japanese Patent Application Laid-Open No. 03-126377 discusses a method for executing gradation conversion by generating a gamma curve such that overexposure or underexposure can be suppressed in a luminance region with high frequencies, based on a frequency of each luminance in a cumulative histogram of one frame. The gamma conversion processing discussed in Japanese Patent Application Laid-Open No. 03-126377 is an image processing method to be executed through processing in one frame according to a characteristic of a luminance histogram of an input image, and gradation conversion can be executed without losing visibility of a moving object as it does not require composition of a plurality of images. 
     However, through a conventional method of generating a gamma curve from the cumulative histogram as discussed in Japanese Patent Application Laid-Open No. 03-126377, a gradation range near the gradation value having the high frequency is improved. Therefore, in the above-described use case of the monitoring camera, there might be an issue in which the user cannot see the image with appropriate gradation when a plurality of users monitors the images. For example, even if a user desires to see the image (image being cropped by the user), i.e., a gradation characteristic of the gradation value is to be improved, it is not possible to improve the gradation characteristic if a frequency of the gradation value is less than a frequency of a gradation value of the image being cropped by another user. 
     Therefore, a method of improving gradation characteristics of a plurality of images (crop images) even if a plurality of users individually performs cropping (cutting) on a distributed image is sought. 
     SUMMARY OF THE INVENTION 
     An apparatus includes a cropping unit configured to crop a plurality of arbitrary image regions from an input image, a histogram generation unit configured to generate a histogram of each of the arbitrary image regions cropped by the cropping unit, a gradation range calculation unit configured to calculate a target gradation range of each of the image regions from the histogram of each of the image regions, and a gradation conversion unit configured to execute gradation conversion of the input image based on the target gradation range of each of the image regions calculated by the gradation range calculation unit. 
     Further features of the disclosure will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating a configuration example of an imaging system according to the aspect of the embodiments. 
         FIG. 2  is a block diagram illustrating a configuration example of an imaging apparatus according to the aspect of the embodiments. 
         FIG. 3  is a block diagram illustrating a configuration example of an image processing unit according to a first exemplary embodiment of the disclosure. 
         FIG. 4  is a block diagram illustrating a configuration example of a development processing unit according to a first exemplary embodiment of the disclosure. 
         FIG. 5  is a block diagram illustrating a configuration example of a gamma processing unit according to the first exemplary embodiment of the disclosure. 
         FIG. 6  is a flowchart illustrating an example of gamma processing according to the first exemplary embodiment of the disclosure. 
         FIG. 7  is a diagram illustrating an example of processing for acquiring a target gradation range from a clip image according to the first exemplary embodiment of the disclosure. 
         FIG. 8  is a graph illustrating a state where target gradation ranges of a plurality of users overlap each other according to the first exemplary embodiment of the disclosure. 
         FIG. 9  is a block diagram illustrating a configuration example of a gamma curve changing unit according to the first exemplary embodiment of the disclosure. 
         FIG. 10  is a block diagram illustrating a configuration example of a changing range calculation unit according to the first exemplary embodiment of the disclosure. 
         FIG. 11  is a flowchart illustrating an example of gamma curve changing processing according to the first exemplary embodiment of the disclosure. 
         FIG. 12  is a graph illustrating a state where target gradation ranges of a plurality of users do not overlap each other according to the first exemplary embodiment of the disclosure. 
         FIG. 13  is a graph illustrating an example of change of a gamma curve in a case where target gradation ranges do not overlap each other according to the first exemplary embodiment of the disclosure. 
         FIG. 14  is a line graph illustrating a relationship between a noise level and a threshold value according to the first exemplary embodiment of the disclosure. 
         FIG. 15  a graph illustrating an example of change of a gamma curve in a case where target gradation ranges overlap each other according to the first exemplary embodiment of the disclosure. 
         FIG. 16  is a graph illustrating an example of change of a gamma curve in a case where target gradation ranges overlap each other over a wide area according to the first exemplary embodiment of the disclosure. 
         FIG. 17  is a diagram illustrating a relationship among a light amount, an aperture, and a shutter speed according to a second exemplary embodiment of the disclosure. 
         FIG. 18  is a block diagram illustrating a configuration example of a changing range calculation unit according to the second exemplary embodiment of the disclosure. 
         FIG. 19  is a block diagram illustrating a configuration example of a range-by-range gamma curve changing unit according to a third exemplary embodiment of the disclosure. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, an exemplary embodiment of the disclosure will be described in detail with reference to the appended drawings. Although an example in which a monitoring camera is used as an imaging apparatus will be described below, the purpose of use of the imaging apparatus is not limited to monitoring. Further, the disclosure should not be limited to an imaging apparatus and is also applicable to image processing executed by a personal computer (PC) using software. 
       FIG. 1  is a block diagram illustrating a configuration example of an imaging system according to the aspect of the embodiments. The imaging system in  FIG. 1  is configured of a monitoring camera  101  serving as an apparatus that captures an image and processes the captured image and a client apparatus  102  connected to the monitoring camera  101  via an IP network in a mutually communicable state. 
     The monitoring camera  101  distributes, to the client apparatus  102 , image information of an image acquired by capturing an object and processed. 
     The client apparatus  102  displays the image of the image information distributed from the monitoring camera  101 . Further, the client apparatus  102  can accept operation executed by a user, so that the client apparatus  102  can execute control of changing an imaging direction or a focal length of the monitoring camera  101 , switching an auto-focus/manual-focus (AF/MF) mode thereof, or cropping out an image, via the network through the user operation. In the present exemplary embodiment, although the monitoring camera  101  is used as an image processing apparatus, the configuration may be such that an image signal acquired by the monitoring camera  101  is transmitted to the client apparatus  102  without executing image processing on the image signal, and the client apparatus  102  executes the image processing on the received image signal. 
       FIG. 2  is a block diagram illustrating a configuration example of the imaging apparatus (monitoring camera  101 ) according to the aspect of the embodiments. Details of an internal configuration example of the monitoring camera  101  will be described below. 
     An imaging optical system  201  includes an optical lens, an optical filter, an aperture, and a shutter, and collects optical information of an object. For example, the optical lens may be a zoom lens that moves in the optical axis direction to change a focal length or a focus lens that moves in the optical axis direction to adjust a focal point. Examples of the optical filter include a visible light cutting filter, an infrared cutting filter, and a neutral density (ND) filter. In addition, in the configuration example illustrated in  FIG. 2 , although the monitoring camera  101  is a lens-integrated imaging apparatus integrally including the imaging optical system  201 , the monitoring camera  101  may be an interchangeable-lens imaging apparatus including an interchangeable lens. 
     An imaging element unit  202  acquires color information by combining an element that converts the optical information collected by the imaging optical system  201  into a current value with a color filter, and outputs a formed object image as an electric signal (analog signal). Further, the imaging element unit  202  serves as an image sensor capable of setting an arbitrary exposure time with respect to all of pixels. 
     A central processing unit (CPU)  203  controls an entirety of the monitoring camera  101  to control image-capturing processing, image processing, and image output processing. The CPU  203  sequentially reads and interprets a command stored in a read only memory (ROM)  204  or a random access memory (ROM)  205 , and executes processing according to a result of reading and interpretation. 
     An imaging system control unit  206  controls the imaging optical system  201  to adjust a focus, open a shutter, and adjust an aperture. For example, the imaging system control unit  206  acquires a setting value from an image processing unit  209  to drive the optical lens or switch the optical filter according to the setting value. In addition, the configuration may be such that the setting value is calculated by the CPU  203 , and the imaging system control unit  206  drives the imaging optical system  201  based on an instruction from the CPU  203  and the calculation result thereof. 
     A control unit  207  controls the entirety of the monitoring camera  101  according to an instruction from the client apparatus  102 . Alternatively, the configuration may be such that the control unit  207  receives the instruction from the client apparatus  102  and transmits the instruction to the CPU  203  or the imaging system control unit  206 , and the CPU  203  or the imaging system control unit  206  controls the monitoring camera  101 . 
     An analog/digital (A/D) conversion unit  208  converts the light amount of the object detected by the imaging optical system  201  into a digital signal value. The converted digital signal (image signal) is transmitted to the image processing unit  209 . 
     The image processing unit  209  executes image processing such as development processing, filter processing, sensor correction, or noise removal with respect to the received digital signal (image signal). Further, for example, when exposure adjustment is to be executed, the image processing unit  209  transmits luminance information of the image to the imaging system control unit  206  (or the CPU  203 ), calculates a setting value for acquiring an image with appropriate exposure based on the illuminance information, and executes exposure adjustment by driving the imaging optical system  201 . Furthermore, adjustment based on the image information, e.g., auto-focus control, is executed in the same manner. It is assumed that exposure adjustment and auto-focus control are executed by a known technique, and details thereof will be omitted. The image generated by the image processing unit  209  is transmitted to an encoder unit  210 . Details of image processing executed by the image processing unit  209  will be described below. 
     The encoder unit  210  executes processing of converting image data processed by the image processing unit  209  into data in a file format such as Motion Jpeg or H.264. The converted image information is transmitted to the client apparatus  102  via a network (not illustrated). 
     Hereinafter, a first exemplary embodiment will be described.  FIG. 3  is a block diagram illustrating a configuration example of an image processing unit (image processing unit  209 ) according to the present exemplary embodiment of the disclosure. The image processing unit  209  of the present exemplary embodiment includes an image input unit  301 , a pre-processing unit  302 , a development processing unit  303 , a post-processing unit  304 , and an image output unit  305 . 
     The image input unit  301  receives image data that is captured by the imaging element unit  202  and converted into a digital signal value through the A/D conversion unit  208 . 
     The pre-processing unit  302  executes correction processing such as removal processing of fixed pattern noise caused by a sensor or gain adjustment processing. 
       FIG. 4  is a block diagram illustrating a configuration example of a development processing unit (development processing unit  303 ) of the present exemplary embodiment of the disclosure. The development processing unit  303  executes predetermined processing on the image data received from the pre-processing unit  302  through a de-mosaic processing unit  401 , a white-balance processing unit  402 , a gamma processing unit  403 , and a sharpness processing unit  404 . The gamma processing unit  403  will be described below in detail. It is assumed that processing executed by the other processing units is executed using a known technique, and thus details thereof will be omitted. 
     For example, the post-processing unit  304  executes post-processing for reducing the randomly generated noise by performing, on the image processed through development processing, noise-reduction (NR) filtering in a spatial direction and a temporal direction. 
     The image output unit  305  outputs the image signal processed through the development processing to the encoder unit  210 , and the image signal is encoded by the encoder unit  210  and output to the client apparatus  102 . 
     Hereinafter, gamma processing of the present exemplary embodiment of the disclosure will be described in detail with reference to  FIGS. 5 to 9 . 
       FIG. 5  is a block diagram illustrating a configuration example of a gamma processing unit (gamma processing unit  403 ) of the first exemplary embodiment of the disclosure. The gamma processing unit  403  according to the present exemplary embodiment executes gradation conversion with respect to an image signal. The gamma processing unit  403  includes an input signal acquisition unit  501 , a crop region acquisition unit  502 , a histogram calculation unit  503 , a target range acquisition unit  504 , an overlapping ratio calculation unit  505 , a gamma curve changing unit  506 , and a gamma processing application unit  507 . Processing executed by the respective processing units will be described in detail with reference to a flowchart of the gamma processing illustrated in  FIG. 6 . 
     Hereinafter, a processing flow of the gamma processing unit  403  of the present exemplary embodiment will be described with reference to a flowchart in  FIG. 6 .  FIG. 6  is a flowchart illustrating an example of the gamma processing according to the present exemplary embodiment of the disclosure. The flowchart in  FIG. 6  illustrates a processing procedure executed by the CPU  203  by controlling each processing block. The CPU  203  loads the program stored in the memory (the ROM  204  or the RAM  205 ) included in the CPU  203  and executes the loaded program to realize the above-described processing. 
     First, in step S 601 , the input signal acquisition unit  501  acquires a signal of the entire image processed by the de-mosaic processing unit  401  and the white-balance processing unit  402  in the development processing unit  303 . Alternatively, the input signal acquisition unit  501  may acquire the entire image processed by the de-mosaic processing unit  401 , and the white balance processing may be executed thereon after the gamma processing according to the present exemplary embodiment is executed by the gamma processing unit  403 . 
     In step S 602 , the crop region acquisition unit  502  acquires an image of an image region where the user executes cropping (image cropping) from among the entire image. The crop region acquisition unit  502  acquires information about the crop region being cropped, from the client apparatus  102  via the network to extract, and extracts and acquires only an image signal corresponding to that image region. In the present exemplary embodiment, the crop region acquisition unit  502  uses the region information received by the control unit  207  to acquire the image signal of a portion corresponding to the region information. In a case where a plurality of users monitors images of different image regions, the crop region acquisition unit  502  acquires image signals of all of the crop regions. On the other hand, in a case where none of the users executes a crop image monitoring, i.e., only the entire image is monitored, the processing in steps S 603  to S 606  is not executed, and the processing proceeds to step S 607 . 
     Then, in step S 603 , the histogram calculation unit  503  generates a histogram of the crop image (a signal of a crop image region) received from the crop region acquisition unit  502 . When a plurality of crop images (signals of crop image regions) is received, the histogram calculation unit  503  generates a plurality of histograms for the respective image regions. 
     In step S 604 , the target range acquisition unit  504  detects a target gradation range for each user from the histogram received from the histogram calculation unit  503 . The target gradation range defined in the disclosure refers to a gradation range where improvement of a gradation characteristic is considered to be particularly desirable from among the gradation ranges of the images cropped by respective users. Specifically, the conditions that a ratio of frequencies of gradation values within a predetermined range regarded as a target gradation range to the entire frequency should be a ratio of a threshold value or more, and that a width of the gradation range should fall within a predetermined range are to be satisfied. 
     For example, a gradation range as a target of determination on whether the gradation range is the target gradation range is expressed as gradation values “i” to “j”, whereas a sum of frequencies of the gradation values “i” to “j” is expressed as “sum(i, j)”. Further, a total gradation width in the crop image (crop image region) is expressed as “all”, and a sum of the frequencies of the entire luminance histogram is expressed as “sum(all)”. Then, the gradation values “i” to “j” is regarded to represent a target gradation range if the following formulas  1  and  2  are satisfied when predetermined values are expressed as “th 1 ” and “th 2 ”.
 
sum( i, j )&gt;sum(all)× th 1  Formula 1
 
 j−i &lt;all× th 2  Formula 2
 
     The formula 1 will be described using an example where the user monitors a crop image having a size of 320×180 cropped from an image in full high definition (HD) (1920×1080), as more specifically illustrated in  FIG. 7 . In such a case, the threshold value th 1  should be 1% of a total frequency of the luminance histogram of the crop image (i.e., a total number of pixels in the image region acquired from the crop region acquisition unit  502 ). At this time, if it is assumed that the target gradation range ranges from the gradation values of 12 to 64, the following formula 3 is satisfied.
 
sum (12.64)&gt;320×180×1/100  Formula 3
 
     Further, with respect to the formula 2, if it is assumed that the total gradation width is 10-bit, and the threshold value th 2  is 10%, the gradation values of “i” to of the target gradation range should satisfy the following formula 4.
 
 j−i&lt; 2 10 ×10/100  Formula 4
 
     In addition, the threshold values th 1  and th 2  are not limited to the above-described particular percentages, and any values are possible as long as the target gradation range of the user can be detected. In order to satisfy the formula 1, frequencies with respect to the other luminance ranges has to be higher as a value of the threshold value th 1  becomes greater. In other words, the threshold value th 1  is increased if an object which occupies a larger area within the crop image is specified as a target range. A possibility of satisfying the formula 2 will be greater as a value of the threshold value th 2  is increased, and gradation taken as a target range will be increased. A value of the threshold value th 2  is set to be smaller with respect to a scene having an object in which the user would like to particularly focus on an arbitrary narrow range. On the contrary, a value of the threshold value th 2  is set to be greater with respect to a scene having an object in which the user would like to focus on a range of a certain width. A plurality of target gradation ranges may be included in a single crop image. Further, a calculation method of the target gradation range is not limited to the above-described method, and any calculation method may be employed as long as the target gradation range can be calculated at each of the crop images. 
     Next, in step S 605 , based on a plurality of target gradation ranges acquired from the target range acquisition unit  504 , the overlapping ratio calculation unit  505  calculates a ratio of a gradation width where the target gradation ranges of respective users overlap each other. Specifically, as illustrated in  FIG. 8 , the target gradation ranges of the users A and B overlap with each other at the gradation values “k” to “1”, whereas the target gradation ranges of the users B and C overlap with each other at the gradation values “m” to “n”. In this case, if the total gradation width is 10-bit, an overlapping ratio “overlap ratio” is defined by the following formula 5. 
     
       
         
           
             
               
                 
                   overlap_ratio 
                   = 
                   
                     
                       
                         ( 
                         
                           l 
                           - 
                           k 
                         
                         ) 
                       
                       + 
                       
                         ( 
                         
                           n 
                           - 
                           m 
                         
                         ) 
                       
                     
                     
                       2 
                       10 
                     
                   
                 
               
               
                 
                   Formula 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   5 
                 
               
             
           
         
       
     
     In addition, a value of the overlapping ratio ranges from 0.0 to 1.0, and the overlapping ratio calculated by the formula 5 is input to the gamma curve changing unit  506 . 
     In step S 606 , the gamma curve changing unit  506  changes a gamma curve according to the overlapping ratio acquired from the overlapping ratio calculation unit  505 . 
       FIG. 9  is a block diagram illustrating a configuration example of the gamma curve changing unit  506  of the present exemplary embodiment of the disclosure. The gamma curve changing unit  506  includes a changing range calculation unit  901  and a range-by-range gamma curve changing unit  902 . Through a comparison between the overlapping ratio and a predetermined threshold value (hereinafter, a threshold value used by the gamma curve changing unit  506  is called as “th 3 ”), the changing range calculation unit  901  calculates a gradation range in which a predetermined gamma curve is to be changed. The range-by-range gamma curve changing unit  902  changes the gamma curve based on the gradation range which is calculated by the changing range calculation unit  901  and is to be changed. 
       FIG. 10  is a block diagram illustrating a configuration example of the changing range calculation unit  901  of the present exemplary embodiment of the disclosure. The changing range calculation unit  901  includes a noise level calculation unit  1001  and a threshold value calculation unit  1002 . The noise level calculation unit  1001  calculates a noise level indicating dispersion of noise superimposed on the entire image. Based on the noise level calculated by the noise level calculation unit  1001 , the threshold value calculation unit  1002  calculates the threshold value th 3  to be compared to the overlapping ratio. 
     Hereinafter, a processing flow in which the processing in step S 606  is divided into steps will be described with reference to  FIG. 11 .  FIG. 11  is a flowchart illustrating an example of gamma curve changing processing according to the present exemplary embodiment of the disclosure. The flowchart in  FIG. 11  illustrates a processing procedure executed by the CPU  203  by controlling each processing block. The CPU  203  loads a program stored in the memory (the ROM  204  or the RAM  205 ) included in the CPU  203  and executes the loaded program to realize the above-described processing. 
     First, in step S 1101 , the changing range calculation unit  901  determines whether there is an overlap between the target gradation ranges of a plurality of users (i.e., whether an overlapping ratio is greater than 0).  FIG. 12  is a graph illustrating an example where there is no overlap between the target gradation ranges of a plurality of users according to the exemplary embodiment of the disclosure. As illustrated in  FIG. 12 , if the overlapping ratio is 0 (NO in step S 1101 ), i.e., if there is no overlap between the target gradation ranges of any users, the processing proceeds to step S 1102 . On the other hand, as illustrated in  FIG. 8 , if the overlapping ratio is a value greater than 0 (YES in step S 1101 ), the processing proceeds to step S 1105 . 
     In step S 1102 , the gamma curve changing unit  506  determines whether the target gradation range is a bright portion or a dark portion. For example, gradation as a target of determination on whether the target gradation range is a bright portion or a dark portion is expressed as a gradation value “o”, and a minimum gradation value and a maximum gradation value of the gradation range acquired from the target range acquisition unit  504  are expressed as “p” and “q”, respectively. If the following formula 6 is satisfied, the gradation value “o” is determined to be a bright portion. If the following formula 7 is satisfied, the gradation value “o” is determined to be a dark portion. 
     
       
         
           
             
               
                 
                   o 
                   ≥ 
                   
                     
                       p 
                       + 
                       q 
                     
                     2 
                   
                 
               
               
                 
                   Formula 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   6 
                 
               
             
             
               
                 
                   o 
                   &lt; 
                   
                     
                       p 
                       + 
                       q 
                     
                     2 
                   
                 
               
               
                 
                   Formula 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   7 
                 
               
             
           
         
       
     
     In step S 1103 , the range-by-range gamma curve changing unit  902  changes a gamma curve to suppress overexposure if the target gradation range is determined to be a bright portion (i.e., if the gradation value “o” satisfies the formula 6). Specifically, as illustrated in  FIG. 13 , a gamma curve is pulled down from a predetermined gamma curve indicated by a dashed line.  FIG. 13  illustrates an example where the gamma curve is changed in a case where there is no overlap according to the first exemplary embodiment of the disclosure. 
     On the other hand, in step S 1104 , the range-by-range gamma curve changing unit  902  pulls up a gamma curve to suppress underexposure if the target gradation range is determined to be a dark portion (i.e., if the gradation value “o” satisfies the formula 7). Specifically, as illustrated in  FIG. 13 , a gamma curve is pulled up from the predetermined gamma curve indicated by the dashed line. At this time, the predetermined gamma curve illustrated in  FIG. 13  is applied with respect to the gradation range other than the target gradation range. By changing the gamma curve as described above, the change in the entire image can minimized, and an appropriate gradation conversion can be executed with respect to the crop image selected by the user. The gamma curve changed by the range-by-range gamma curve changing unit  902  is input to the gamma processing application unit  507 , and the processing executed by the gamma curve changing unit  506  is ended. 
     If the overlapping ratio is determined to be a value greater than 0 in step S 1102 , in step S 1105 , the gamma curve changing unit  506  determines whether the overlapping ratio is a predetermined threshold value or more. Herein, a threshold value calculated by the threshold value calculation unit  1002  is calculated according to a noise level acquired from the noise level calculation unit  1001 . Specifically, the noise level calculated by the noise level calculation unit  1001  refers to dispersion of noise superimposed on the entire image. The noise dispersion is acquired by the following formulas 8 and 9. 
     
       
         
           
             
                 
             
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     Herein, Y(v, h) represents a luminance value of the entire image in the current frame, and “v” and “h” respectively represent coordinate positions in the horizontal direction and the vertical direction in the frame. Generally, the dispersion of noise included in the signal can be calculated precisely by setting large values to “s 1 ” and “s 2 ”. In addition, a calculation method of the noise level is not limited to the formulas 8 and 9, and various calculation methods such as a calculation method using a standard deviation of noise or a noise characteristic of a sensor can be also employed. 
       FIG. 14  is a line graph illustrating a relationship between a noise level and a threshold value according to the exemplary embodiment of the disclosure. An example of a relationship between a noise level σ2Y(v, h) calculated by the noise level calculation unit  1001  based on the formula 10 and the threshold value th 3  calculated by the threshold value calculation unit  1002  is illustrated in  FIG. 14 . 
     
       
         
           
             
               
                 
                   
                     th 
                     ⁢ 
                     
                         
                     
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                     3 
                   
                   = 
                   
                     { 
                     
                       
                         
                           
                             
                               
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                                 ⁢ 
                                 
                                     
                                 
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                           1.0 
                         
                         
                           
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                   Formula 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   10 
                 
               
             
           
         
       
     
     In the formula  10 , “σ2max” represents a constant number which determines an upper limit of the standard deviation of noise, “k 1 ” represents an inclination for correlating the standard deviation of noise and the threshold value th 3  in a proportional relationship in that the threshold value th 3  becomes greater as the noise level is higher. In addition, the relationship between the noise level and the threshold value th 3  is not limited to such a proportional relationship as illustrated in  FIG. 14  and the formula 10, and can be expressed by a non-linear function such as a quadratic function or a cubic function. 
     The gamma curve changing unit  506  uses the acquired threshold value th 3  to compare the overlapping ratio with the threshold value th 3 . If the overlapping ratio is less than the threshold value th 3  (NO in step S 1105 ), the processing proceeds to step S 1106 . If the overlapping ratio is the threshold value th 3  or more (YES in step S 1105 ), the processing proceeds to step S 1107 . 
     If the gamma curve changing unit  506  determines that the overlapping ratio is less than the predetermined threshold value th 3  in step S 1105 , in step S 1106 , similar to the processing in step S 1102 , the gamma curve changing unit  506  determines whether the target gradation range is a bright portion or a dark portion. Thereafter, as illustrated in  FIG. 15 , the gamma curve is changed with respect to the gradation range where there is no overlap between any target gradation ranges.  FIG. 15  is a diagram illustrating an example where the gamma curve is changed in a case where there is no overlap according to the exemplary embodiment of the disclosure. In order to suppress the overexposure, the gamma curve of an area determined to be a bright portion is changed and pulled down from the predetermined gamma curve. On the other hand, if the target gradation range is determined to be a dark portion, the gamma curve is pulled up from the predetermined gamma curve in order to suppress the underexposure. At this time, the predetermined gamma curve is applied with respect to a gradation range where there is an overlap between the target ranges and a gradation range other than the target gradation range. As described above, by changing the gamma curve, a change in the entire image and an interference between crop images of users can be suppressed to minimum, and an appropriate gradation conversion can be executed with respect to the crop images selected by the users. The changed gamma curve is input to the gamma processing application unit  507 , and the processing executed by the gamma curve changing unit  506  is ended. 
     If the gamma curve changing unit  506  determines that the overlapping ratio is the predetermined threshold value th 3  or more in step S 1105 , in step S 1107 , similar to the processing in step S 1102 , the gamma curve changing unit  506  determines whether the target gradation range is a bright portion or a dark portion. Thereafter, as illustrated in  FIG. 16 , the gamma curve is changed with respect to all of the target gradation ranges regardless of presence or absence of an overlap.  FIG. 16  is a graph illustrating an example where the gamma curve is changed when there is an overlap over a wide range according to the exemplary embodiment of the disclosure. As described above, the change of the gamma curve is executed by the range-by-range gamma curve changing unit  902 . At this time, a predetermined gamma curve is applied with respect to the gradation range other than the target gradation range. As described above, although changing the gamma curve with respect to a wide gradation range causes a substantial change in the entire image, it is possible to improve a gradation characteristic in a target gradation range that is focused on by a plurality of users in common. The changed gamma curve is input to the gamma processing application unit  507 , and the processing executed by the gamma curve changing unit  506  is ended. 
     When the gamma curve changing processing in step S 606  described with reference to  FIG. 11  is completed, in step S 607 , the gamma processing application unit  507  applies the gamma curve received from the gamma curve changing unit  506  to the input signal. 
     By executing the above-describe processing steps to the moving image signal, the gamma processing according to the aspect of the embodiments can be reflected on the entire image as well as the crop image. According to the present exemplary embodiment, an appropriate gamma curve can be determined according to the crop image selected by the user. For example, as illustrated in  FIG. 11 , in a case there is no overlap between the target gradation ranges of the crop images monitored by the users, the gamma curve is finely changed for the respective users, so that images processed by the appropriate graduation processing are distributed. On the other hand, as illustrated in  FIG. 15 , in a case where there is a certain degree of overlap between the target gradation ranges of the crop images of the users, images processed by the appropriate graduation processing are distributed while a change of the image that affects the entire image is minimized. Further, as illustrated in  FIG. 16 , if the target gradation ranges of the users overlap with each other considerably, the gamma curve is changed with respect to the wide gradation range. Therefore, although the impression of the entire image will be substantially different from that of the image where the predetermined gamma processing is executed thereon, a gradation characteristic can be improved with respect to the target gradation range that is focused on by the plurality of users in common. 
     In the present exemplary embodiment of the disclosure, although an example in which gamma processing is executed with respect to the luminance component is described above, a component on which the gamma processing is executed is not limited in particular. For example, similar processing can be executed with respect to a color component or a near-infrared or a far-infrared component. 
     Hereinafter, a second exemplary embodiment will be described. In the first exemplary embodiment, a method of calculating a threshold value according to a noise level calculated by the noise level calculation  1001  illustrated in  FIG. 10  has been described. Hereinafter, processing of the changing range calculation unit  901  according to the second exemplary embodiment will be described in detail with reference to  FIG. 18 .  FIG. 18  is a block diagram illustrating a configuration example of the changing rage calculation unit according to the second exemplary embodiment of the disclosure. The changing range calculation unit  901  of the second exemplary embodiment includes an imaging condition acquisition unit  1801 , an imaging mode acquisition unit  1802 , and a threshold value calculation unit  1803 . The imaging condition acquisition unit  1801  acquires various imaging conditions including an aperture and a shutter for capturing an input image to be acquired by an input image acquisition unit. The imaging mode acquisition unit  1802  acquires an imaging mode. The threshold value calculation unit  1803  calculates a threshold value from the imaging condition acquired by the imaging condition acquisition unit  1801 . In addition, the same reference numeral is applied to a configuration similar to the configuration in the first exemplary embodiment, and description thereof will be omitted. 
     The noise level acquired in the first exemplary embodiment is calculated from an input image through the formulas 8 and 9. In the second exemplary embodiment, the noise level is calculated based on the imaging condition and the imaging mode.  FIG. 17  is a diagram illustrating a relationship between a light amount, an aperture, and a shutter speed according to the second exemplary embodiment of the disclosure. As illustrated in  FIG. 17 , an amount of light received by the sensor is smaller as an aperture of the lens is reduced, and an image with more noise is acquired. On the other hand, an amount of light received by the sensor is greater if an aperture is released closer to the open end, and an image with less noise is acquired. Similarly, an image with more noise is acquired if a shutter speed is higher, and an image with less noise is acquired if a shutter speed is lower. 
     As described above, in the present exemplary embodiment, a noise level σ2Y(v, h) is calculated by using a relationship between the aperture and the shutter speed. In addition, the imaging condition to be used is not limited to the aperture and the shutter, and a condition such as a gain or a sensor temperature may be also used. 
     Further, in the monitoring camera, various modes can be selected according to scenes. For example, a motion priority mode is intended to be used for a scene including a large number of moving objects. Therefore, when the motion priority mode is being executed, the target gradation range detected from the histogram may significantly change from time to time because various objects are assumed to move in and out of the screen. Therefore, when the motion priority mode is selected, the inclination “k 1 ” in the formula 10 is set to be smaller. Through the above setting, an amount of change of the threshold value th 3  caused by fluctuation of the noise level is reduced, so that the gamma curve can be changed appropriately without making an unnecessary change. In addition, the imaging mode to be used is not limited to the motion priority mode, but an imaging mode other than the motion priority mode may be also used. The processing to be executed after acquiring the threshold value th 3  is similar to that of the first exemplary embodiment, and thus description thereof will be omitted. 
     As described above, according to the second exemplary embodiment, it is possible to execute gamma processing in which the crop image, the imaging condition, and the imaging mode selected by the user are taken into consideration. 
     Hereinafter, a third exemplary embodiment will be described. In the first exemplary embodiment, a method of changing a gamma curve based on the changing range calculated by the changing range calculation unit  901  illustrated in  FIG. 9  has been described. Hereinafter, the range-by-range gamma curve changing unit  902  according to the third exemplary embodiment will be described with reference to FIG.  19 .  FIG. 19  is a block diagram illustrating a configuration example of the range-by-range gamma curve changing unit  902  according to the third exemplary embodiment of the disclosure. As illustrated in  FIG. 19 , the range-by-range gamma curve changing unit  902  includes a user authority acquisition unit  1901  for acquiring user authorities granted to a plurality of users and a number of increased/decreased users acquisition unit  1902  for acquiring the number of increased/decreased users which is changed at a certain time period. In addition, the same reference numeral is applied to a configuration similar to the configuration in the first exemplary embodiment, and description thereof will be omitted. 
     The range-by-range gamma curve changing unit  902  of the first exemplary embodiment changes the gamma curve with respect to the gradation range acquired from the changing range calculation unit  901 . In the third exemplary embodiment, a gamma curve is changed while information about a user authority and the number of increased/decreased users is taken into consideration in addition to information about a gradation range described in the first exemplary embodiment. Hereinafter, a specific flow of processing will be described. 
     In a case where the number of users who crop out regions that do not overlap with other regions is increased, the gamma curve is appropriately with respect to the target range of each user like the a first exemplary embodiment without other particular processing (Cf.  FIG. 13 ). 
     In a case where the number of users who crop out regions that overlap with other regions is increased, whether to change the gamma curve is determined by an authority of a corresponding user. If the additional user is given an administrator authority, a change of the gamma curve is immediately executed. If the additional user is given only a general authority, the gamma curve is changed at a timing at which substantial change has occurred in the luminance of the entire image or the object, i.e., a timing at which the luminance of the imaging environment is changed or a pan/tilt operation is executed. 
     As described above, by changing the gamma curve while considering increase or decrease in the number of users and user authorities, unnecessary change of the gamma curve caused by increase or decrease in the number of users can be suppressed, and appropriate gamma processing is executed with respect to a user who holds an administrator authority. 
     As described above, according to the third exemplary embodiment, it is possible to execute gamma processing in which increase or decrease in the number of users and user authorities are taken into consideration in addition to the crop images selected by the users. 
     Although exemplary embodiments of the disclosure have been described above, the disclosure is not limited to the above-described exemplary embodiments, and may be changed and applied as appropriate according to a target circuit configuration within a technical spirit of the aspect of the embodiments. For example, an imaging apparatus described as a camera in the above-described exemplary embodiment may be applied to a digital still camera or a digital video camera. 
     Further, the aspect of the embodiments can be embodied as, for example, a system, an apparatus, a method, a computer program, or a storage medium. Specifically, the aspect of the embodiments may be applied to a single apparatus or a system that includes a plurality of apparatuses. The respective units constituting the imaging apparatus and the steps of the control method of the imaging apparatus according to the present exemplary embodiment can be realized through an operation of a program stored in a memory of a computer. The computer program and a computer readable storage medium storing that program are also included within the scope of the aspect of the embodiments. 
     The disclosure can be realized in such a manner that a program for realizing one or more functions according to the above-described exemplary embodiments is supplied to a system or an apparatus via a network or a storage medium, so that the one or more processors in the system or the apparatus read and execute the program. Further, the aspect of the embodiments can be also realized with a circuit (e.g., application specific integrated circuit (ASIC)) that realizes one or more functions. 
     According to an aspect of the embodiments, even in a case where a plurality of users performs cropping on a distributed image, a gradation characteristic can be improved with respect to each of the plurality of images (crop images). 
     Other Embodiments 
     Embodiment(s) of the disclosure can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)m), a flash memory device, a memory card, and the like. 
     While the disclosure has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2017-148613, filed Jul. 31, 2017, which is hereby incorporated by reference herein in its entirety.