Patent Publication Number: US-8989482-B2

Title: Image processing apparatus, image processing method, and program

Description:
BACKGROUND 
     The present disclosure relates to an image processing apparatus, an image processing method, and a program, and more particularly to an image processing apparatus, an image processing method, and a program, which can perform a more appropriate parallax control. 
     In the related art, a technique has been developed in which a stereoscopic image is displayed using a display device. A sense of depth of a subject reproduced by the stereoscopic image is varied depending on a viewing condition when a user views the stereoscopic image, or a capturing condition of the stereoscopic image. For this reason, depending situations, a subject too protrudes or is too depressed, and thus a sense of depth which is reproduced is unnatural, which causes a user to feel exhausted. 
     Therefore, there have been many techniques for appropriately controlling a parallax of a displayed stereoscopic image. For example, as such techniques, there has been a technique in which a stress value indicating a stress extent on a user when viewing a stereoscopic image is obtained based on a parallax of each pixel of the stereoscopic image, and the parallax of the stereoscopic image is controlled so as to minimize the stress value (for example, refer to Japanese Unexamined Patent Application Publication No. 2011-055022). 
     SUMMARY 
     However, there are cases where a completely appropriate parallax control may not be performed in the above-described technique. 
     For example, in the technique for performing a parallax control for minimizing a stress value, there are cases where a comfortable sense of depth can be reproduced in the entire screen of the stereoscopic image; however, a depth position of a subject to which a user pays attention on the stereoscopic image may not be appropriate. As such, if the depth position of the main subject to which the user pays attention is inappropriate, the user experiences eye fatigue when viewing the stereoscopic image. 
     It is desirable to more appropriately perform a parallax control of a stereoscopic image. 
     According to an embodiment of the present disclosure, there is provided an image processing apparatus including an attention region estimation unit that estimates an attention region which is estimated as a user paying attention thereto on a stereoscopic image; a parallax detection unit that detects a parallax of the stereoscopic image and generates a parallax map indicating a parallax of each region of the stereoscopic image; a setting unit that sets conversion characteristics for correcting a parallax of the stereoscopic image based on the attention region and the parallax map; and a parallax conversion unit that corrects the parallax map based on the conversion characteristics. 
     The image processing apparatus may further include an image synthesis unit that corrects a parallax of the stereoscopic image based on the corrected parallax map. 
     The image processing apparatus may further include a maximum and minimum parallax detection unit that detects a maximum value and a minimum value of parallaxes indicated by the parallax map and detects a parallax of the attention region based on the parallax map and the attention region. In this case, the setting unit may set the conversion characteristics based on the maximum value, the minimum value, and the parallax of the attention region. 
     The setting unit may set the conversion characteristics such that the parallax of the attention region is converted into a parallax with a predetermined size set in advance. 
     The setting unit may set different conversion characteristics for the attention region on the stereoscopic image and regions other than the attention region on the stereoscopic image. 
     The setting unit may set the conversion characteristics of the attention region on the stereoscopic image such that a parallax is linearly converted in a predetermined parallax section including the parallax of the attention region. 
     The image processing apparatus may further include a smoothening unit that smoothens the attention region or the conversion characteristics. 
     According to another embodiment of the present disclosure, there is provided an image processing method or a program including estimating an attention region which is estimated as a user paying attention thereto on a stereoscopic image; detecting a parallax of the stereoscopic image and generating a parallax map indicating a parallax of each region of the stereoscopic image; setting conversion characteristics for correcting a parallax of the stereoscopic image based on the attention region and the parallax map; and correcting the parallax map based on the conversion characteristics. 
     In the embodiments of the present disclosure, an attention region which is estimated as a user paying attention thereto on a stereoscopic image is estimated; a parallax of the stereoscopic image is detected and a parallax map indicating a parallax of each region of the stereoscopic image is generated; conversion characteristics for correcting a parallax of the stereoscopic image are set based on the attention region and the parallax map; and the parallax map is corrected based on the conversion characteristics. 
     According to still another embodiment of the present disclosure, there is provided an image processing apparatus including an attention region estimation unit that estimates an attention region which is estimated as a user paying attention thereto on a stereoscopic image; a parallax detection unit that detects a parallax of the stereoscopic image and generates a parallax map indicating a parallax of each region of the stereoscopic image; a histogram generation unit that generates a histogram of a parallax of the stereoscopic image indicated by the parallax map by weighting a parallax of the attention region; a shift amount calculation unit that calculates a shift amount indicating a correction amount of the parallax of the stereoscopic image based on the histogram; and an image processing unit that corrects the parallax of the stereoscopic image by shifting at least one of a left eye image or a right eye image forming the stereoscopic image based on the shift amount. 
     According to still another embodiment of the present disclosure, there is provided an image processing method or a program including estimating an attention region which is estimated as a user paying attention thereto on a stereoscopic image; detecting a parallax of the stereoscopic image and generating a parallax map indicating a parallax of each region of the stereoscopic image; generating a histogram of a parallax of the stereoscopic image indicated by the parallax map by weighting a parallax of the attention region; calculating a shift amount indicating a correction amount of the parallax of the stereoscopic image based on the histogram; and correcting the parallax of the stereoscopic image by shifting at least one of a left eye image or a right eye image forming the stereoscopic image based on the shift amount. 
     In the embodiments of the present disclosure, an attention region which is estimated as a user paying attention thereto on a stereoscopic image is estimated; a parallax of the stereoscopic image is detected and a parallax map indicating a parallax of each region of the stereoscopic image is generated; a histogram of a parallax of the stereoscopic image indicated by the parallax map is generated by weighting a parallax of the attention region; a shift amount indicating a correction amount of the parallax of the stereoscopic image is calculated based on the histogram; and the parallax of the stereoscopic image is corrected by shifting at least one of a left eye image or a right eye image forming the stereoscopic image based on the shift amount. 
     According to still another embodiment of the present disclosure, there is provided an image processing apparatus including a scene recognition unit that performs scene recognition for a stereoscopic image; a parallax detection unit that detects a parallax of the stereoscopic image and generates a parallax map indicating a parallax of each region of the stereoscopic image; a setting unit that sets conversion characteristics for correcting a parallax of the stereoscopic image based on the parallax map; and a parallax conversion unit that corrects the parallax map based on the conversion characteristics and a result of the scene recognition. 
     According to still another embodiment of the present disclosure, there is provided an image processing method or a program including performing scene recognition for a stereoscopic image; detecting a parallax of the stereoscopic image and generates a parallax map indicating a parallax of each region of the stereoscopic image; setting conversion characteristics for correcting a parallax of the stereoscopic image based on the parallax map; and correcting the parallax map based on the conversion characteristics and a result of the scene recognition. 
     In the embodiments of the present disclosure, scene recognition for a stereoscopic image is performed; a parallax of the stereoscopic image is detected and a parallax map indicating a parallax of each region of the stereoscopic image is generated; conversion characteristics for correcting a parallax of the stereoscopic image is set based on the parallax map; and the parallax map is corrected based on the conversion characteristics and a result of the scene recognition. 
     According to the embodiments of the present disclosure, it is possible to more appropriately perform a parallax control of a stereoscopic image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating an outline of the present disclosure. 
         FIG. 2  is a diagram illustrating an appropriate parallax range. 
         FIG. 3  is a diagram illustrating a configuration example of the image processing apparatus according to an embodiment. 
         FIG. 4  is a flowchart illustrating an image conversion process. 
         FIG. 5  is a diagram illustrating an example of the conversion function. 
         FIG. 6  is a diagram illustrating an example of the conversion function. 
         FIG. 7  is a diagram illustrating an image conversion. 
         FIG. 8  is a diagram illustrating a conversion function for each region. 
         FIG. 9  is a diagram illustrating another configuration example of the image processing apparatus. 
         FIG. 10  is a flowchart illustrating an image conversion process. 
         FIG. 11  is a diagram illustrating still another configuration example of the image processing apparatus. 
         FIG. 12  is a flowchart illustrating an image conversion process. 
         FIG. 13  is a diagram illustrating each region of the parallax map. 
         FIG. 14  is a diagram illustrating generation of the histogram. 
         FIG. 15  is a diagram illustrating an example of the stress function. 
         FIG. 16  is a diagram illustrating an example of the stress function. 
         FIG. 17  is a diagram illustrating an effect of the parallax control considering an attention region. 
         FIG. 18  is a diagram illustrating still another configuration example of the image processing apparatus. 
         FIG. 19  is a flowchart illustrating an image conversion process. 
         FIG. 20  is a diagram illustrating an example of the conversion function. 
         FIG. 21  is a diagram illustrating still another configuration example of the image processing apparatus. 
         FIG. 22  is a flowchart illustrating an image conversion process. 
         FIG. 23  is a diagram illustrating smoothening of the attention region. 
         FIG. 24  is a diagram illustrating smoothening of the conversion function. 
         FIG. 25  is a diagram illustrating still another configuration example of the image processing apparatus. 
         FIG. 26  is a diagram illustrating a configuration example of a parallax conversion unit. 
         FIG. 27  is a flowchart illustrating an image conversion process. 
         FIG. 28  is a diagram illustrating generation of the synthesis parallax map. 
         FIG. 29  is a diagram illustrating a configuration example of the computer. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. 
     First Embodiment 
     Outline of Present Disclosure 
     First, an outline of the present disclosure will be described with reference to  FIG. 1 . 
     In the present disclosure, for example, a parallax control of a stereoscopic image including a left eye image L and a right eye image R is performed as shown in the upper part of  FIG. 1 . When the left eye image L and the right eye image R are input, a parallax map DM 11  indicating parallaxes of the left eye image L and the right eye image R is first generated. In addition, for example, if a histogram HT 11  of a parallax of each pixel on the stereoscopic image is generated from the parallax map DM 11 , a parallax distribution of each subject in the stereoscopic image can be found. 
     In addition, the transverse axis of the histogram HT 11  indicates parallax, that is, a depth of the subject, and the longitudinal axis indicates a frequency of each parallax, that is, the number of pixels of the parallax map, having the parallax. Particularly, the right direction of the transverse axis of the histogram HT 11  indicates a direction of the parallax where a subject is positioned on the front side when viewed from a viewing user, and the left direction of the transverse axis indicates a direction of the parallax where a subject is positioned on the depth side when viewed from the viewer. In addition, a subject having the parallax of “0” is positioned on a display surface which displays a stereoscopic image, and a subject having the parallax of a positive value is positioned further on the front side than the display surface when viewed from the user. 
     Here, for example, a region which is estimated as the user paying attention thereto on a stereoscopic image is referred to as an attention region, and a region on the parallax map DM 11 , placed at the same position as the attention region is assumed as a region AR 11 . In addition, it is assumed that distributions of the respective pixels in the region AR 11  correspond to a region AR′ 11  part in the histogram HT 11 . 
     In this case, the parallax of the attention region on the stereoscopic image has a positive number, and thus the subject in the attention region is viewed on the front side of the display surface when viewed from the user. That is to say, the subject seems protruding. Generally, if a parallax of a subject to which the user pays attention is small, and the subject is positioned around the display surface, inconsistency between convergence and focus adjustment does not occur, and thus the user hardly feels exhausted. 
     Therefore, in the present disclosure, the parallax map is corrected, which is a corrected parallax map DM 12 , such that a subject of the attention region is positioned around the display surface, and a parallax of each subject on the stereoscopic image has a value in an appropriate parallax range giving a lesser burden to a user. In a parallax histogram HT 12  obtained from the corrected parallax map DM 12 , parallax distributions of the respective pixels in the region AR 12  placed at the same position as the attention region correspond to the AR′  12  part of the histogram HT 12 , and this it can be seen that the subject of the attention region is positioned around the display surface. 
     If the corrected parallax map DM 12  is obtained in this way, an image conversion is performed for the left eye image L and the right eye image R such that parallaxes of the left eye image L and the right eye image R become parallaxes shown in the corrected parallax map DM 12 , and thereby final left eye image L′ and right eye image R′ are generated. 
     In the image conversion, one image of the left eye image L and the right eye image R may be not converted and only the other image thereof may be converted, or both the left eye image L and the right eye image R may be converted and then new left eye image L′ and the right eye image R′ are generated. 
     In addition, the appropriate parallax range giving a lesser burden to a user when viewing a stereoscopic image is determined depending on a viewing distance of the stereoscopic image or a display device size. 
     For example, as shown in  FIG. 2 , it is assumed that a user views a stereoscopic image at a position of a viewing distance L s  from a display surface DS 11  which displays the stereoscopic image, and a binocular gap of the user is d e . In addition, a distance between the user and a location of the subject on the stereoscopic image, that is, a position where a stereoscopic image of the subject is generated, is L d , and a convergence angle in a case where the distance L d  to the position where a stereoscopic image is generated is the same as the viewing distance L s  is β. 
     In addition, it is assumed that a convergence angle relative to a location of a subject having a minimum value of a parallax in an appropriate parallax range, that is, a location of the subject on the deepest side, is α min , and an convergence angle relative to a location of a subject having a maximum value of a parallax in an appropriate parallax range is α max . 
     In a case where a user views a stereoscopic image on the display surface DS 11  in the viewing condition shown in  FIG. 2 , it is generally said that the user can view the stereoscopic image comfortably if the following Expression (1) is satisfied. In addition, in Expression (1), α denotes a convergence angle relative to a location of the substrate on the stereoscopic image. 
     
       
         
           
             
               
                 
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     In addition, a relationship between the viewing distance L s  and the convergence angle β is expressed by the following Expression (2) 
     
       
         
           
             
               
                 
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     Therefore, if the above Expression (2) is modified, the following Expression (3) can be obtained. 
     
       
         
           
             
               
                 
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     In addition, in a manner similar to Expression (2), a relationship between the distance L d  where a stereoscopic image is generated and the convergence angle α is expressed by the following Expression (4). 
     
       
         
           
             
               
                 
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     Here, since a range taken by the convergence angle α is α min ≦α≦α max , a range of a value taken by the distance L d  to the position where a stereoscopic image is generated can be expressed by the following Expression (5). 
     
       
         
           
             
               
                 
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     In addition, from the above Expression (1), the convergence angle α min =β−(Π/180), and α max =β+(Π/180). Therefore, a subject parallax range where the viewer can view the stereoscopic image comfortably can be obtained from the binocular gap d e  and the convergence angle β. 
     For example, in a case where a display device displaying a stereoscopic image is a 46V type display device, and the viewing distance L s  is 1.7 m, if the distance L d  between the user and locations of the respective substrates is in a range of 0.5 m to 1.5 m, the user can view the stereoscopic image comfortably. If the range of the distance L d  is replaced with a parallax, the parallax is in a range of −56 pixels to 55 pixels. 
     Configuration Example of Image Processing Apparatus 
     Next, an image processing apparatus according to an embodiment of the present disclosure will be described.  FIG. 3  is a diagram illustrating a configuration example of the image processing apparatus according to an embodiment of the present disclosure. 
     An image processing apparatus  11  includes an attention region estimation unit  21 , a parallax detection unit  22 , a parallax analysis unit  23 , a parallax conversion unit  24 , and an image synthesis unit  25 . In the image processing apparatus  11 , a left eye image L and a right eye image R forming a stereoscopic image are supplied to the attention region estimation unit  21 , the parallax detection unit  22 , and the image synthesis unit  25 . 
     The attention region estimation unit  21  estimates an attention region on the stereoscopic image based on the supplied left eye image L and right eye image R, and supplies the estimation result to the parallax analysis unit  23 . Here, the attention region is a region which is estimated as the user who views the stereoscopic image paying attention thereto. 
     The parallax detection unit  22  generates a parallax map indicating a parallax of each pixel on the stereoscopic image based on the supplied left eye image L and right eye image R, and supplies the generated parallax map to the parallax analysis unit  23  and the parallax conversion unit  24 . 
     The parallax analysis unit  23  defines parallax conversion characteristics used for parallax correction of the stereoscopic image, more specifically, correction of the parallax map, based on the estimation result from the attention region estimation unit  21  and the parallax map from the parallax detection unit  22 , and supplies the defined parallax conversion characteristics to the parallax conversion unit  24 . The parallax analysis unit  23  includes a maximum and minimum parallax detection portion  31  and a setting portion  32 . 
     The maximum and minimum parallax detection portion  31  detects a maximum value and a minimum value (hereinafter, referred to as a maximum parallax and a minimum parallax) of a pixel value (parallax) of each pixel of the parallax map from the parallax detection unit  22 . In addition, the maximum and minimum parallax detection portion  31  detects a parallax of the attention region (hereinafter, referred to as an attention parallax) based on the parallax map from the parallax detection unit  22  and the estimation result from the attention region estimation unit  21 , and supplies the attention parallax, the maximum parallax, and the minimum parallax to the setting portion  32 . 
     The setting portion  32  defines conversion characteristics based on the attention parallax, the maximum parallax, and the minimum parallax from the maximum and minimum parallax detection portion  31 , so as to be supplied to the parallax conversion unit  24 . 
     The parallax conversion unit  24  converts a parallax of each pixel of the parallax map from the parallax detection unit  22  based on the conversion characteristics from the setting portion  32 , so as to correct the parallax map, and supplies a corrected parallax map obtained as a result thereof and the parallax map to the image synthesis unit  25 . The image synthesis unit  25  adjusts a parallax of the stereoscopic image by performing an image conversion for the supplied left eye image L and right eye image R based on the corrected parallax map and the parallax map from the parallax conversion unit  24 , and outputs left eye image L′ and right eye image R′ obtained as a result thereof. 
     Description of Image Conversion Process 
     When the image processing apparatus  11  shown in  FIG. 3  is supplied with a stereoscopic image formed by the left eye image L and the right eye image R and is instructed to adjust a parallax of the stereoscopic image, it adjust a parallax of the stereoscopic image through an image conversion process, and outputs the stereoscopic image where the parallax is adjusted. Hereinafter, the image conversion process performed by the image processing apparatus  11  will be described with reference to the flowchart in  FIG. 4 . 
     In step S 11 , the attention region estimation unit  21  estimates an attention region on a stereoscopic image based on at least one of a left eye image L and a right eye image R which have been supplied, and supplies information indicating the attention region to the maximum and minimum parallax detection portion  31 . For example, the attention region estimation unit  21  estimates the attention region by performing face recognition, visual attention, scene recognition, and the like. 
     Specifically, for example, if a face region is detected from the left eye image L and the right eye image R through the face recognition, the face region is used as the attention region. In addition, for example, in a case where the visual attention is performed, a predetermined feature amount such as luminance information, color information, or edge information is extracted from the left eye image L or the right eye image R, and a region which has a large luminance difference on an image or a region which has colors different from the periphery is detected and is used as the attention region. 
     In addition, the attention region estimation unit  21  may detect an object region which has a large area on the left eye image L or the right eye image R, or a moving subject region, and use such a region as the attention region. 
     In step S 12 , the parallax detection unit  22  detects a parallax of the stereoscopic image based on the supplied left eye image L and right eye image R, and generates a parallax map. 
     For example, the parallax detection unit  22  sequentially uses each pixel of the right eye image R as an attention pixel and detects a pixel (hereinafter, referred to as a corresponding pixel) of the left eye image L corresponding to the attention pixel, thereby obtaining a parallax of each pixel of the right eye image R relative to the left eye image L. 
     At this time, a difference between a region around the attention pixel on the right eye image R and a region around each pixel on the left eye image L, more specifically, for example, a sum of absolute difference values of pixels in the region is calculated, and a pixel of the left eye image L where the difference becomes the minimum is a corresponding pixel relative to the attention pixel. In addition, a distance between the attention pixel of the right eye image R and the corresponding pixel of the left eye image L is a parallax between the pixels, and the parallax is a pixel value of a pixel of the parallax map, which is located at the same position as the attention pixel. 
     In addition, although an example where the parallax map using the right eye image R as a reference is generated using each pixel of the right eye image R as an attention pixel has been described here, a parallax map using the left eye image L as a reference may be generated, or a parallax map using each of the left eye image L and the right eye image R as a reference may be generated. 
     If the parallax map is generated in this way, the parallax detection unit  22  supplies the generated parallax map to the maximum and minimum parallax detection portion  31  and the parallax conversion unit  24 , and the flow proceeds to step S 13 . 
     In step S 13 , the maximum and minimum parallax detection portion  31  detects a maximum parallax and a minimum parallax based on the parallax map supplied from the parallax detection unit  22 . That is to say, the maximum value and the minimum value of the parallaxes of the respective pixels on the parallax map are detected, and are respectively used as the maximum parallax and the minimum parallax. 
     In step S 14 , the maximum and minimum parallax detection portion  31  detects a parallax of the attention region based on the parallax map supplied from the parallax detection unit  22  and the estimation result of the attention region supplied from the attention region estimation unit  21 . 
     For example, the maximum and minimum parallax detection portion  31  designates an average value of pixel values (parallaxes) of pixels in the same region as the attention region on the parallax map as an attention parallax which is a parallax of the attention region. In addition, a centroid value of pixel values of pixels in the same region as the attention region on the parallax map may be used as the attention parallax, or the most common pixel value of pixel values of pixels in the same region as the attention region may be used as the attention parallax. 
     The maximum and minimum parallax detection portion  31  obtains the attention parallax of the attention region, and supplies the maximum parallax, the minimum parallax, and the attention parallax to the setting portion  32 , and the flow proceeds to step S 15 . 
     In step S 15 , the setting portion  32  sets conversion characteristics based on the maximum parallax, the minimum parallax, and the attention parallax supplied from the maximum and minimum parallax detection portion  31 . 
     For example, it is assumed that an appropriate parallax range described with reference to  FIG. 2  is obtained in advance, and a maximum value and a minimum value of the parallax range are respectively an allowable maximum parallax d max ′ and an allowable minimum parallax d min ′. In addition, the maximum parallax, the minimum parallax, and the attention parallax are respectively denoted by d max , d min , and d at . 
     In this case, as shown in  FIG. 5 , the setting portion  32  defines the conversion characteristics such that each parallax on the parallax map is converted into a parallax in a range from the allowable minimum parallax d min ′ to the allowable maximum parallax d max ′ (hereinafter, referred to as an allowable parallax range), and the attention parallax d at  is converted into a parallax “0”. That is to say, a conversion function for converting each parallax d i  on the parallax map into a corrected parallax d o  is defined so as to satisfy such conditions. 
     Further, in  FIG. 5 , the transverse axis indicates a parallax d i  of each pixel on the parallax map, and the longitudinal axis indicates a corrected parallax d o . In addition, in the figure, the bent line F 11  indicates a graph of the conversion function. 
     In the conversion function indicated by the bent line F 11 , the maximum parallax d max  is converted into the allowable maximum parallax d max ′ and the minimum parallax d min  is converted into the allowable minimum parallax d min ′. In addition, the attention parallax d at  is converted into 0. In addition, the section of the conversion function from the attention parallax d at  to the maximum parallax d max  is a first-order function with a predetermined slope, and, in a similar manner, the section from the minimum parallax d min  to the attention parallax d at  is also a first-order function, but the entire conversion function is a nonlinear function. 
     As such, if the parallax of the attention region is converted into 0, a depth position of the attention region becomes a display surface of the stereoscopic image, and thus it is possible to reduce eye fatigue of a user. In addition, parallaxes of the entire stereoscopic image are in the allowable parallax range, and thus a user can view the stereoscopic image comfortably. 
     The setting portion  32  defines the conversion function in this way, and supplies the defined conversion function to the parallax conversion unit  24  as conversion characteristics. 
     In addition, the conversion characteristics are not limited to the example shown in  FIG. 5 , and may be a function indicated by a bent line which monotonously increases for a parallax or may be any function. 
     Therefore, for example, a linear function shown in  FIG. 6  may be a conversion function. In  FIG. 6 , the transverse axis indicates a parallax d i  of each pixel on the parallax map, and the longitudinal axis indicates a corrected parallax d o . In addition, in the figure, the straight line F 12  indicates a graph of the conversion function. In the example shown in  FIG. 6 , the conversion function indicated by the straight line F 12  is a first-order function. 
     Referring to the flowchart of  FIG. 4  again, in step S 16 , the parallax conversion unit  24  generates a corrected parallax map based on the conversion characteristics from the setting portion  32  and the parallax map from the parallax detection unit  22 , and supplies the corrected parallax map and the parallax map to the image synthesis unit  25 . 
     For example, the parallax conversion unit  24  obtains a corrected parallax d o  by assigning the parallax d i  of the pixel of the parallax map to the conversion function as the conversion characteristics, and designates the corrected parallax obtained as a pixel value of a pixel of the corrected parallax map, located at the same position as the pixel. 
     In addition, the calculation of the corrected parallax using the conversion function may be realized using a conversion table where each parallax d i  is correlated with a corrected parallax d o  which is obtained through conversion of the parallax d i . In this case, when conversion characteristics are supplied from the setting portion  32 , the parallax conversion unit  24  generates a conversion table having the conversion characteristics, and converts the parallax map into a corrected parallax map using the conversion table. 
     In step S 17 , the image synthesis unit  25  converts the supplied left eye image L and right eye image R into a left eye image L′ and a right eye image R′ having appropriate parallaxes using the corrected parallax map and the parallax map from the parallax conversion unit  24 , and outputs the converted images. 
     For example, it is assumed that the parallax detection unit  22  generates a parallax map using the right eye image R as a reference, the attention region estimation unit  21  estimates an attention region on the right eye image R, and the parallax conversion unit  24  generates a corrected parallax map using the right eye image R as a reference. 
     In this case, the image synthesis unit  25  uses the right eye image R as a right eye image R′ for output without conversion, and, as shown in  FIG. 7 , generates an image of a phase of a parallax specified by the pixel of the corrected parallax map from the right eye image R and the left eye image L, and uses the generated image as a left eye image L′. 
     In addition, in  FIG. 7 , the transverse direction of the figure indicates a direction of parallaxes (horizontal direction) of the left eye image L and the right eye image R. For example, it is assumed that a corresponding pixel of the pixel Gr on the right eye image R is a pixel Gl on the left eye image L, and a pixel value (parallax) of the pixel on the parallax map located at the same position as the pixel Gr is d. 
     In this case, if a position of the pixel Gr in the transverse direction (hereinafter, referred to as an x coordinate) of the figure is x R , an x coordinate x L  of the pixel Gl is x R +d. In addition, if a pixel value (parallax) of the pixel on the corrected parallax map, located at the same position as the pixel Gr is d′ (here, d&gt;d′), a pixel Gl′ of an image of a phase p of a parallax between a phase 1 of the parallax of the right eye image R and a phase 0 of the parallax of the left eye image L is generated as a pixel of the left eye image L′ after the image conversion. 
     That is to say, a calculation of the following Expression (6) is performed, and a pixel value I M (x′) of the pixel Gl′ is calculated. 
     
       
         
           
             
               
                 
                   [ 
                   
                     Expression 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     6 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     
                       I 
                       M 
                     
                     ⁡ 
                     
                       ( 
                       
                         x 
                         ′ 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       
                         I 
                         M 
                       
                       ⁡ 
                       
                         ( 
                         
                           
                             x 
                             R 
                           
                           + 
                           
                             d 
                             ′ 
                           
                         
                         ) 
                       
                     
                     = 
                     
                       
                         
                           
                             d 
                             ′ 
                           
                           ⁢ 
                           
                             
                               I 
                               L 
                             
                             ⁡ 
                             
                               ( 
                               
                                 x 
                                 L 
                               
                               ) 
                             
                           
                         
                         + 
                         
                           
                              
                             
                               d 
                               - 
                               
                                 d 
                                 ′ 
                               
                             
                              
                           
                           ⁢ 
                           
                             
                               I 
                               R 
                             
                             ⁡ 
                             
                               ( 
                               
                                 x 
                                 R 
                               
                               ) 
                             
                           
                         
                       
                       
                         
                           d 
                           ′ 
                         
                         + 
                         
                            
                           
                             d 
                             - 
                             
                               d 
                               ′ 
                             
                           
                            
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     In addition, in Expression (6), I L (x L ) and I R (x R ) respectively indicate pixel values of the pixel Gl and the pixel Gr. In addition, the phase p of the generated parallax is p=d′/(d′+|d−d′|). 
     In this way, the image synthesis unit  25  performs the calculation shown in Expression (6) for each pixel on the right eye image R, generates the new left eye image L′ having the parallax d′ with the right eye image R, and outputs a stereoscopic image formed by the right eye image R′ and the left eye image L′ as a stereoscopic image after the parallax is adjusted. 
     In addition, although an example where the right eye image R is used as the right eye image R′ without conversion and the left eye image L is converted into the left eye image L′ has been described, the left eye image L may be used as the left eye image L′ without conversion, and the right eye image R may be converted into the right eye image R′. 
     Further, the right eye image R and the left eye image L may be respectively converted into the right eye image R′ and the left eye image L′. In this case, the attention region estimation unit  21  detects an attention region from each of the right eye image R and the left eye image L, and the parallax detection unit  22  generates a parallax map having the right eye image R as a reference and a parallax map having the left eye image L as a reference. 
     In addition, conversion characteristics are defined from an attention region on the right eye image R and a parallax map having the right eye image R as a reference, and the parallax map is converted into a corrected parallax map. In the same manner, conversion characteristics are defined from an attention region on the left eye image L and a parallax map having the left eye image L as a reference, and the parallax map is converted into a corrected parallax map. 
     In addition, each pixel on the right eye image R and a corresponding pixel on the left eye image L corresponding to the pixel are used, a pixel value of a pixel of an image which is different from a parallax and a phase of the right eye image R by half of the parallax d′ of the pixel of the corrected parallax map having the right eye image R as a reference is obtained, and thereby the left eye image L′ is generated. In the same manner, each pixel on the left eye image L and a corresponding pixel on the right eye image R corresponding to the pixel are used, a pixel value of a pixel of an image which is different from a parallax and a phase of the left eye image L by half of the parallax d′ of the pixel of the corrected parallax map having the left eye image L as a reference is obtained, and thereby right eye image R′ is generated. 
     In this way, in the image processing apparatus  11 , an attention region is positioned around the display surface displaying a stereoscopic image based on the parallax map and the attention region, and conversion characteristics are defined such that a parallax of each pixel of the stereoscopic image becomes a parallax in the allowable parallax range, thereby performing parallax adjustment. Thereby, a parallax of the stereoscopic image can be more appropriately controlled, and thus a user can view the stereoscopic image more comfortably. As a result, it is possible to reduce eye fatigue of a user. 
     Second Embodiment 
     Configuration Example of Image Processing Apparatus 
     Although a case where all the pixels of the parallax map are converted by the same conversion function (conversion characteristics) has been described, a different conversion function may be used in a region at the same position as an attention region on the parallax map and in a region a position different from the attention region. 
     In this case, for example, as shown in  FIG. 8 , a region AR 41  located at the same position as an attention region on a parallax map DM 21  is specified. In addition, as shown in the lower part of the figure, a conversion function for a parallax of each pixel in the region AR 41  and a conversion function of a parallax of each pixel outside the region AR 41  are defined. 
     In the example shown in  FIG. 8 , a bent line F 31  indicates a conversion function of a parallax of each pixel in the region AR 41 , and a bent line F 32  indicates a conversion function of a parallax of each pixel outside the region AR 41 . In addition, in the graph of the conversion function, the transverse axis indicates a parallax d i  of each pixel on the parallax map, and the longitudinal axis indicates a corrected parallax d o . 
     In the conversion function indicated by the bent line F 31 , the attention parallax d at  is converted into 0, and in the section from the parallax d ats  including the attention parallax d at  to the parallax d ate , the conversion function is a first-order function with a predetermined slope. 
     In addition, the parallax d ats , the parallax d ate , or a slope of the conversion function in the section between the parallax d ats  and the parallax d ate  may be predefined, or may be set by a user. In addition, the parallax d ats  and the parallax d ate  may be defined based on a parallax of each pixel in the region AR 41 . In this case, for example, a minimum value and a maximum value of parallaxes of pixels in the region AR 41  respectively become the parallax d ats  and the parallax d ate . 
     In addition, in the conversion function indicated by the bent line F 31 , the maximum parallax d max  and the minimum parallax d min  of the pixels on the parallax map are respectively converted into an allowable maximum parallax d max ′ and an allowable minimum parallax d min ′, and thus the conversion function is continuous in the overall sections. In this example as well, the section between the minimum parallax d min  and the parallax d ats , and the section between the parallax d ate  and the maximum parallax d max  show a first-order function. 
     In contrast, in the conversion function indicated by the bent line F 32 , the maximum parallax d max  and the minimum parallax d min  are respectively converted into an allowable maximum parallax d max ′ and an allowable minimum parallax d min ′. In addition, the attention parallax d at  is converted into 0. That is to say, the conversion function indicated by the bent line F 32  is the same as the conversion function indicated by the bent line F 11  in  FIG. 5 . 
     As such, if the parallax map is converted using the conversion functions having different conversion characteristics inside and outside the region AR 41  of the parallax map DM 21  located at the same position as the attention region, it is possible to more appropriately control a parallax of the attention region of the stereoscopic image. Particularly, characteristics of sections around the attention region in the conversion function inside the region AR 41  are linear functions (first-order functions), and thereby it is possible to suppress distortion of depth around the attention region. 
     As such, in a case where a parallax map is converted into a corrected parallax map with different conversion characteristics according to regions of the parallax map, an image processing apparatus is configured, for example, as shown in  FIG. 9 . In addition, in  FIG. 9 , parts corresponding to the case in  FIG. 3  are given the same reference numerals, and description thereof will be appropriately omitted. 
     An image processing apparatus  61  in  FIG. 9  is the same as the image processing apparatus  11  in  FIG. 3  in that the image processing apparatus  61  includes the attention region estimation unit  21  to the image synthesis unit  25 . However, in the image processing apparatus  61 , an estimation result of the attention region obtained by the attention region estimation unit  21  is supplied to the maximum and minimum parallax detection portion  31  of the parallax analysis unit  23  and the parallax conversion unit  24 . In addition, the parallax conversion unit  24  performs conversion of a parallax map for each region based on the estimation result of the attention region and the conversion characteristics. 
     Description of Image Conversion Process 
     Next, an image conversion process performed by the image processing apparatus  61  will be described with reference to the flowchart of  FIG. 10 . In addition, the processes in steps S 41  to S 44  are the same as those in the steps S 11  to S 14  of  FIG. 4 , and thus description thereof will be omitted. 
     However, in step S 41 , an estimation result of the attention region is supplied to the maximum and minimum parallax detection portion  31  and the parallax conversion unit  24  from the attention region estimation unit  21 . 
     In step S 45 , the setting portion  32  sets conversion characteristics based on the maximum parallax, the minimum parallax, and the attention parallax supplied from the maximum and minimum parallax detection portion  31 . Specifically, for example, as described with reference to  FIG. 8 , a conversion function for a region located at the same position as the attention region on the parallax map and a conversion function for a region located at a position different from the attention region on the parallax map, are set. The setting portion  32  supplies the conversion function set for each region to the parallax conversion unit  24 . 
     In step S 46 , the parallax conversion unit  24  converts the parallax map from the parallax detection unit  22  into a corrected parallax map based on the conversion functions (conversion characteristics) from the setting portion  32  and the estimation result from the attention region estimation unit  21 . 
     That is to say, the parallax conversion unit  24  converts a parallax of each pixel of the parallax map into a corrected parallax using the conversion function for a region which is located at the same position as the attention region on the parallax map. In addition, the parallax conversion unit  24  converts a parallax of each pixel of the parallax map into a corrected parallax using the conversion function for a region which is located at a position different from the attention region on the parallax map. In this way, the corrected parallax is obtained using the conversion function set for each region of the parallax map, and thereby a corrected parallax map is generated. 
     The corrected parallax map is generated, and, thereafter, the image conversion process finishes through a process in step S 47 . This process is the same as the process in step S 17  of  FIG. 4 , and thus description thereof will be omitted. 
     The image processing apparatus  61  sets a conversion function for each region of the parallax map, particularly, conversion functions for a region which is the same as the attention region and the other region, and coverts the parallax map into a corrected parallax map. Thereby, it is possible to more appropriately control a parallax of the attention region. 
     Third Embodiment 
     Configuration Example of Image Processing Apparatus 
     Although a case where a parallax of the stereoscopic image is adjusted based on the corrected parallax map has been described in the above description, a parallax may be adjusted by calculating a shift amount using a parallax of each pixel of a stereoscopic image, particularly, a parallax of an attention region, and shifting the left eye image L and the right eye image R. 
     In this case, an image processing apparatus is configured, for example, as shown in  FIG. 11 . In addition, in  FIG. 11 , parts corresponding to the case in  FIG. 3  are given the same reference numerals, and description thereof will be appropriately omitted. 
     An image processing apparatus  91  in  FIG. 11  includes an attention region estimation unit  21 , a parallax detection unit  22 , a histogram calculation unit  101 , a stress value calculation unit  102 , a shift amount calculation unit  103 , and an image processing unit  104 . 
     The histogram calculation unit  101  generates a parallax histogram for each region on a stereoscopic image based on a parallax map from the parallax detection unit  22  and an attention region from the attention region estimation unit  21 , and supplies the parallax histogram to the stress value calculation unit  102 . When the histogram is generated, a parallax of each pixel of the attention region is weighted. 
     In a case of adjusting a parallax by shifting the left eye image L and the right eye image R by a predetermined amount based on the histogram supplied from the histogram calculation unit  101 , the stress value calculation unit  102  calculates a stress value indicating stress which a user feels when viewing the stereoscopic image where the parallax is adjusted, and supplies the stress value to the shift amount calculation unit  103 . The shift amount calculation unit  103  calculates a shift amount which minimizes the stress value supplied from the stress value calculation unit  102 , and supplies the shift amount to the image processing unit  104 . 
     The image processing unit  104  shifts the supplied left eye image L and right eye image R based on the shift amount from the shift amount calculation unit  103 , and generates and outputs a left eye image L′ and a right eye image R′. 
     Description of Image Conversion Process 
     Next, an image conversion process performed by the image processing apparatus  91  will be described with reference to the flowchart of  FIG. 12 . 
     In step S 71 , the attention region estimation unit  21  estimates an attention region based on the left eye image L and the right eye image R which has been supplied, and supplies the estimation result to the histogram calculation unit  101 . For example, the attention region is estimated through a process such as face recognition or visual attention. 
     Specifically, for example, in the face detection, coordinates of the vertices of a rectangular region including a person&#39;s face on the left eye image L or the right eye image R, or a size of the rectangular region is detected. 
     In addition, for example, in a case where the image processing apparatus  91  is an imaging apparatus capturing a stereoscopic image, information or the like obtained through an automatic focus process when capturing a stereoscopic image may be used. In other words, since a region of a subject where focus is adjusted on the stereoscopic image is specified in contrast type automatic focusing, the region of the subject where focus is adjusted is an attention region to which a user pays attention. 
     As such, in a case where an attention region is estimated using information regarding the automatic focusing, it is possible to adjust a parallax of the stereoscopic image with respect to a subject to which a user capturing the stereoscopic image pays attention. In addition, in step S 71 , the same process as the process in step S 11  of  FIG. 4  is performed. 
     In step S 72 , the parallax detection unit  22  detects a parallax based on the supplied left eye image L and right eye image R, and supplies a parallax map obtained as a result thereof to the histogram calculation unit  101 . In addition, in step S 72 , the same process as the process in step S 12  of  FIG. 4  is performed. 
     In step S 73 , the histogram calculation unit  101  generates a histogram indicating a parallax distribution for each region on the stereoscopic image based on the parallax map from the parallax detection unit  22  and the attention region from the attention region estimation unit  21 , and supplies the histogram to the stress value calculation unit  102 . 
     For example, if a parallax map DM 41  shown in  FIG. 13  is supplied, the histogram calculation unit  101 , the histogram calculation unit  101  generates a parallax histogram of pixels for each region of the central region DC of the parallax map DM 41 , the region DL around the left end, and the region DR around the right end. 
     Here, the region DC is the same region as a central region on the stereoscopic image, and the region DL and the region DR are the same regions as regions of left and right image frame parts (left and right ends) of the stereoscopic image. In addition, hereinafter, the region DC, the region DL, and the region DR are respectively referred to as a central region DC, a left image frame region DL, and a right image frame region DR. 
     For example, if a histogram of the central region DC is generated, the histogram calculation unit  101  sequentially selects pixels in the central region DC on the parallax map DM 41  as an attention pixel. If the attention pixel is a pixel outside the attention region, the histogram calculation unit  101  adds 1 to a frequency value of the histogram bin of to which a parallax of the attention pixel belongs, and, if the attention pixel is a pixel in the attention region, adds a predetermined weight value W (here, 1&lt;W) to a frequency value of the histogram bin to which a parallax of the attention pixel belongs, thereby generating a histogram of the central region DC. 
     In addition, when a histogram is generated, for example, as shown in  FIG. 14 , in a case where a plurality of attention regions AR 51  to AR 53  are detected, and several parts of the attention regions overlap each other, the greatest weight of the weights of the attention regions is used. 
     For example, it is assumed that weights W 1  to W 3  are respectively predefined for the attention regions AR 51  to AR 53 . In this case, for example, if an attention pixel is located in a region Q 11  where the attention regions AR 51  to AR 53  overlap each other, the greatest value of the weights W 1  to W 3  is added to a frequency value of the bin to which a parallax of the attention pixel belongs. Specifically, in a case where the weight W 1  is the greatest of the weights W 1  to W 3 , the weight W 1  is added to a frequency value. 
     In addition, in a case where the attention pixel is located in a region Q 12  where the attention region AR 51  and the attention region AR 53  overlap each other, a greater value of the weight W 1  and the weight W 3  is added to a frequency value of the bin to which a parallax of the attention pixel belongs. Further, in a case where the attention pixel is located in a region Q 13  which does not overlap the other attention regions on the attention region AR 53 , the weight W 3  is added to a frequency value of the bin to which a parallax of the attention pixel belongs. 
     Referring to the flowchart of  FIG. 12  again, the histogram calculation unit  101  generates the histogram for each of the central region DC, the left image frame region DL, and the right image frame region DR, and supplies the histogram to the stress value calculation unit  102 , and the flow proceeds to step S 74 . 
     In step S 74 , the stress value calculation unit  102  calculates a stress value based on the histogram supplied from the histogram calculation unit  101 , and supplies the stress value to the shift amount calculation unit  103 . 
     For example, the stress value calculation unit  102  records a stress function in advance for each region of the central region DC, the left image frame region DL, and the right image frame region DR. Here, the stress function is a function indicating a relationship between a parallax of the stereoscopic image and stress of a user when viewing the stereoscopic image. 
     Specifically, a stress function stress_func(d) of the central region DC is a function indicated by a curve F 51  in  FIG. 15 . In addition, in  FIG. 15 , the transverse axis indicates a parallax, and the longitudinal axis indicates a stress value. In addition, parallax values −th far  and th near  are values calculated in advance in the allowable parallax range. 
     The stress function stress_func(d) indicated by the curve F 51  is a fourth-order function in the section where the parallax d is smaller than −th far , is a second-order function in the section where the parallax d is equal to or more than −th far  and equal to or less than th near , and is a first-order function in the section where the parallax d is greater than th near . 
     The stress function stress_func(d) is expressed by the following Expression (7). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Expression 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     7 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     
                       
                         
                           stress_func 
                           ⁢ 
                           
                             ( 
                             d 
                             ) 
                           
                         
                         = 
                         
                           
                             
                               d 
                               4 
                             
                             
                               2 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               
                                 th 
                                 far 
                                 2 
                               
                             
                           
                           + 
                           
                             
                               th 
                               far 
                               2 
                             
                             2 
                           
                         
                       
                     
                     
                       
                         
                           when 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           d 
                         
                         &lt; 
                         
                           - 
                           
                             th 
                             far 
                           
                         
                       
                     
                   
                   
                     
                       
                         
                           stress_func 
                           ⁢ 
                           
                             ( 
                             d 
                             ) 
                           
                         
                         = 
                         
                           d 
                           2 
                         
                       
                     
                     
                       
                         
                           when 
                           ⁢ 
                           
                               
                           
                           - 
                           
                             th 
                             far 
                           
                         
                         ≤ 
                         d 
                         ≤ 
                         
                           th 
                           near 
                         
                       
                     
                   
                   
                     
                       
                         
                           stress_func 
                           ⁢ 
                           
                             ( 
                             d 
                             ) 
                           
                         
                         = 
                         
                           
                             2 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             
                               
                                 th 
                                 near 
                               
                               · 
                               d 
                             
                           
                           - 
                           
                             th 
                             near 
                             2 
                           
                         
                       
                     
                     
                       
                         
                           when 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           
                             th 
                             near 
                           
                         
                         &lt; 
                         d 
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     In addition, a stress function stress_func_border(d) of the left image frame region DL and the right image frame region DR is a function indicated by a bent line F 61  in  FIG. 16 . In addition, in  FIG. 16 , the transverse axis indicates a parallax, and the longitudinal axis indicates a stress value. 
     The stress function stress_func_border(d) indicated by the bent line F 61  is 0 in the section where the parallax d is less than 0, and is a first-order function in the section where the parallax d is equal to or more than 0. 
     The stress function stress_func_border(d) is expressed by the following Expression (8). 
     
       
         
           
             
               
                 
                   [ 
                   
                     Expression 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     8 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     
                       
                         
                           stress_func 
                           ⁢ 
                           _border 
                           ⁢ 
                           
                             ( 
                             d 
                             ) 
                           
                         
                         = 
                         0 
                       
                     
                     
                       
                         
                           when 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           d 
                         
                         &lt; 
                         0 
                       
                     
                   
                   
                     
                       
                         
                           stress_func 
                           ⁢ 
                           _border 
                           ⁢ 
                           
                             ( 
                             d 
                             ) 
                           
                         
                         = 
                         
                           
                             d 
                             2 
                           
                           · 
                           BORDER_RATIO 
                         
                       
                     
                     
                       
                         
                           when 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           d 
                         
                         ≥ 
                         0 
                       
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     In addition, in Expression (8), BORDER_RATIO denotes a weight for the left image frame region DL and the right image frame region DR, and the larger the weight BORDER_RATIO is, the higher a contribution ratio of the left image frame region DL and the right image frame region DR to the calculation of a stress value is. In addition, in the stress function stress_func_border(d) indicated by the bent line F 61  of  FIG. 16 , the weight BORDER_RATIO is 1. 
     The stress value calculation unit  102  computes the following Expression (9) based on the stress function stress_func(d) of the central region DC, the stress function stress_func_border(d) of the left image frame region DL and the right image frame region DR, and the histogram for each region, and calculates a stress value stress(s) for a shift amount s. 
     
       
         
           
             
               
                 
                   [ 
                   
                     Expression 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     9 
                   
                   ] 
                 
               
               
                 
                     
                 
               
             
             
               
                 
                   
                     stress 
                     ⁡ 
                     
                       ( 
                       s 
                       ) 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           d 
                           = 
                           
                             VX 
                             ⁢ 
                             _ 
                             ⁢ 
                             MIN 
                           
                         
                         
                           VX 
                           ⁢ 
                           _ 
                           ⁢ 
                           MAX 
                         
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         stress_func 
                         ⁢ 
                         
                           
                             ( 
                             
                               d 
                               - 
                               s 
                             
                             ) 
                           
                           · 
                           
                             
                               Hist 
                               C 
                             
                             ⁡ 
                             
                               ( 
                               d 
                               ) 
                             
                           
                         
                       
                     
                     + 
                     
                       
                         ∑ 
                         
                           d 
                           = 
                           
                             VX 
                             ⁢ 
                             _ 
                             ⁢ 
                             MIM 
                           
                         
                         
                           VX 
                           ⁢ 
                           _ 
                           ⁢ 
                           MAX 
                         
                       
                       ⁢ 
                       
                         stress_func 
                         ⁢ 
                         _border 
                         ⁢ 
                         
                           
                             ( 
                             
                               d 
                               - 
                               s 
                             
                             ) 
                           
                           · 
                           
                             { 
                             
                               
                                 
                                   Hist 
                                   L 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   d 
                                   ) 
                                 
                               
                               + 
                               
                                 
                                   Hist 
                                   R 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   d 
                                   ) 
                                 
                               
                             
                             } 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     In addition, in Expression (9), Hist C (d) denotes a frequency value of the bin to which the parallax d belongs in the histogram of the central region DC. Further, Hist L (d) and Hist R (d) respectively denote a frequency value of the bin to which the parallax d belongs in the histograms of the left image frame region DL and the right image frame region DR. In Expression (9), a detection range of the parallax d is VX_MIN≦d≦VX_MAX. 
     Here, the first term of the right side of the stress value stress(s) in Expression (9) indicates a stress value of the central region DC. That is to say, for each parallax in a detection range in a case where parallax adjustment is performed by shifting a stereoscopic image by the shift amount s, a product between a stress value of the parallax and a frequency value of the parallax is obtained, a sum total of products obtained for the respective parallaxes is a final stress value of the central region DC. 
     In addition, the second term of the right side of the stress value stress(s) indicates a stress value of the left image frame region DL and the right image frame region DR. That is to say, for each parallax in a detection range in a case where parallax adjustment is performed by shifting a stereoscopic image by the shift amount s, a product between a stress value of the parallax and a frequency value of the histogram of the left image frame region DL and the right image frame region DR of the parallax is obtained. In addition, a sum total of products obtained for the respective parallaxes is a final stress value of the left and right image frame parts (the left image frame region DL and the right image frame region DR). 
     A sum of the stress value of the central region DC and the stress value of the left and right image frame parts obtained in this way is a stress value stress(s). 
     As such, when the stress value stress(s) is calculated, a stress value stress(s) of the entire stereoscopic image is calculated by adding a stress value of each pixel with the unit of each pixel of the stereoscopic image. Therefore, it is possible to perform a parallax control by putting emphasis on a dominant subject with a large area, that is, a main substrate. 
     In addition, even if some errors occur in the parallax distribution detection, it is possible to reduce the influence on the parallax control of a stereoscopic image. In addition, if the parallax control of a stereoscopic image is performed using the stress value stress(s), it is possible to perform a parallax control in consideration of the left and right image frame parts of a stereoscopic image. 
     In step S 75 , the shift amount calculation unit  103  calculates a shift amount s which minimizes the stress value stress(s) supplied from the stress value calculation unit  102  and supplies the shift amount to the image processing unit  104 . In addition, the shift amount s is detected in the same range as the detection range of the parallax d, that is, in a range of VX_MIN≦s≦VX_MAX. 
     Specifically, the shift amount calculation unit  103  computes the following Expression (10) and obtains a shift amount s minimizing the stress value stress(s). In addition, when Expression (10) is computed, the shift amount calculation unit  103  initially obtains a stress value stress(0) in a case of s=0 assuming a case where a parallax distribution of the stereoscopic image is flat, and temporarily uses the obtained value as a shift amount s minimizing the stress value stress(s). 
     
       
         
           
             
               
                 
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     In step S 76 , the image processing unit  104  generates a left eye image L′ and a right eye image R′ by shifting the supplied left eye image L and right eye image R based on the shift amount s from the shift amount calculation unit  103 . 
     For example, the image processing unit  104  shifts the left eye image L in the parallax direction by −s/2 for generation of the left eye image L′, and shifts the right eye image R in the parallax direction by s/2 for generation of the right eye image R′, thereby performing parallax adjustment of the stereoscopic image. The image processing unit  104  outputs the stereoscopic image formed by the left eye image L′ and the right eye image R′ obtained in this way as a stereoscopic image where a parallax is adjusted, and the image conversion process finishes. 
     In this way, the image processing apparatus  91  generates a histogram by weighting an attention region for each region of the stereoscopic image, and obtains a stress value from the generated histogram and a stress function for each region. In addition, the image processing apparatus  91  shifts the stereoscopic image based on an appropriate shift amount obtained from the stress value, thereby performing parallax adjustment. 
     If a shift amount is calculated in consideration of an attention region as such, a more appropriate parallax control can be realized, and thus it is possible to make a subject of the attention region more easily viewed and to suppress fatigue of a user viewing the stereoscopic image to the minimum. 
     Specifically, for example, as shown in  FIG. 17 , upon comparison between a parallax distribution of a stereoscopic image where parallax adjustment is performed taking no account of the attention region and a parallax distribution of a stereoscopic image where parallax adjustment is performed taking account of the attention region, it can be seen that a more appropriate parallax control is performed for one taking account of the attention region. 
     In addition, in  FIG. 17 , the left part of the figure shows a histogram indicating a parallax distribution of the stereoscopic image where the parallax adjustment is performed taking no account of the attention region, and the right part of the figure shows a histogram indicating a parallax distribution of the stereoscopic image where the parallax adjustment is performed taking account of the attention region. In addition, in each histogram, the transverse axis indicates a parallax, and the longitudinal axis indicates a frequency value for each parallax. Here, the parallax adjustment performed taking no account of the attention region refers to parallax adjustment of performing the image conversion process in  FIG. 12  without weighting parallaxes of pixels of the attention region. 
     In the histogram shown in the left part of the figure, the curves C 11  to C 13  respectively indicate parallax distributions in the regions of the central region DC, the left image frame region DL, and the right image frame region DR of the stereoscopic image after the parallax adjustment. 
     In addition, X 11  denotes a position of the display surface displaying the stereoscopic image, and X 12  denotes a peak position of the curve C 11 , that is, a location of a foreground (main subject) on the stereoscopic image. Further, X 13  denotes a peak position of a frequency value of a parallax of the image frame part defined from the curves C 12  and C 13 , that is, a location of a background on the stereoscopic image. 
     As can be seen from the histogram of the left part of the figure, if a shift amount is obtained taking no account of the attention region, the main subject which is a foreground excessively protrudes toward a user, and the background is located around the display surface which thus leads to an insufficient sense of depth. 
     In contrast, the histogram shown in the right part of the figure is a histogram indicating parallax distributions of the stereoscopic image obtained through the parallax adjustment by the image processing apparatus  91 . In the histogram, the curves C 21  to C 23  respectively indicate parallax distributions in the regions of the central region DC, the left image frame region DL, and the right image frame region DR of the stereoscopic image after the parallax adjustment. 
     In addition, X 21  denotes a position of the display surface displaying the stereoscopic image, and X 22  denotes a peak position of the curve C 21 , that is, a location of the foreground (main subject) on the stereoscopic image. Further, X 23  denotes a location of the background on the stereoscopic image defined from the curves C 22  and C 23 . 
     As can be seen from the histogram of the right part of the figure, if a shift amount is obtained taking account of the attention region, the main subject which is a foreground is suppressed from protruding toward a user, and the foreground and the background are positioned with balance. 
     In addition, although, in the above description, a case where the left eye image L and the right eye image R forming the stereoscopic image are processed using their original sizes and a shift amount s is calculated has be described, the left eye image L and the right eye image R may be reduced at a predetermined reduction ratio and then the shift amount s may be calculated. In this case, detection of an attention region or generation of a histogram is performed using the reduced left eye image L and right eye image R, and then a shift amount is calculated. 
     In addition, in this case, when parallax adjustment of the stereoscopic image is performed, the calculated shift amount is enlarged at a reciprocal of the reduction ratio of the left eye image L or the right eye image R and then is used for the parallax adjustment. That is to say, the left eye image L or the right eye image R is shifted based on the enlarged shift amount. If the stereoscopic image is reduced and then the shift amount is calculated in this way, it is possible to perform parallax adjustment more rapidly with a less process amount. 
     In addition, in the image processing apparatus  91 , since a stress function for each region is changed, and thereby methods of controlling a parallax of the stereoscopic image can be freely changed, it is possible to realize a more appropriate parallax control depending on stereoscopic images or viewing conditions. 
     Fourth Embodiment 
     Configuration Example of Image Processing Apparatus 
     After the parallax adjustment is performed based on the above-described shift amount s, a left eye image L′ and a right eye image R′ may be generated by further performing the parallax adjustment using a conversion function. 
     In this case, an image processing apparatus is configured as shown in  FIG. 18 . In addition, in  FIG. 18 , parts corresponding to the case in  FIG. 3  or  FIG. 11  are given the same reference numerals, and description thereof will be appropriately omitted. 
     An image processing apparatus  131  in  FIG. 18  includes an attention region estimation unit  21 , a parallax detection unit  22 , a histogram calculation unit  101 , a stress value calculation unit  102 , a shift amount calculation unit  103 , a parallax analysis unit  141 , a parallax conversion unit  24 , and an image synthesis unit  25 . 
     In the image processing apparatus  131 , an estimation result of the attention region obtained by the attention region estimation unit  21  is supplied to the parallax analysis unit  141  and the histogram calculation unit  101 . In addition, a parallax map obtained by the parallax detection unit  22  is also supplied to the parallax analysis unit  141  and the histogram calculation unit  101 . Further, a shift amount obtained by the shift amount calculation unit  103  is supplied to the parallax analysis unit  141 . 
     The parallax analysis unit  141  sets conversion characteristics based on the attention region from the attention region estimation unit  21 , the parallax map from the parallax detection unit  22 , and the shift amount from the shift amount calculation unit  103 , and supplies the conversion characteristics to the parallax conversion unit  24 . The parallax analysis unit  141  includes a maximum and minimum parallax detection portion  151  and a setting portion  152 . 
     The maximum and minimum parallax detection portion  151  detects an attention parallax, a maximum parallax, and a minimum parallax by shifting a parallax of each pixel of the attention region from the attention region estimation unit  21  and the parallax map from the parallax detection unit  22  based on the shift amount from the shift amount calculation unit  103 , for supply to the setting portion  152 . In addition, the maximum and minimum parallax detection portion  151  supplies a parallax map corrected based on the shift amount to the parallax conversion unit  24 . 
     The setting portion  152  sets conversion characteristics based on the attention parallax, the maximum parallax, and the minimum parallax supplied from the maximum and minimum parallax detection portion  151 , and supplies the conversion characteristics to the parallax conversion unit  24 . 
     Description of Image Conversion Process 
     Next, an image conversion process performed by the image processing apparatus  131  will be described with reference to the flowchart of  FIG. 19 . In addition, the processes in steps S 101  and S 102  are the same as the processes in steps S 11  and S 12  of  FIG. 4 , and thus description thereof will be omitted. Here, the attention region obtained in step D 101  and the parallax map obtained in step S 102  are supplied to the histogram calculation unit  101  and the maximum and minimum parallax detection portion  151 . 
     In step S 103 , the histogram calculation unit  101  generates histograms of the central region DC, the left image frame region DL, and the right image frame region DR based on the attention region from the attention region estimation unit  21  and the parallax map from the parallax detection unit  22 , and supplies the histograms to the stress value calculation unit  102 . 
     The histogram for each region is generated, and, thereafter, a shift amount is calculated through processes in steps S 104  and S 105 . These processes are the same as the processes in steps S 74  and S 75  of  FIG. 12 , and thus description thereof will be omitted. If a shift amount is calculated in step S 105 , the shift amount calculation unit  103  supplies the calculated shift amount to the maximum and minimum parallax detection portion  151 . 
     In step S 106 , the maximum and minimum parallax detection portion  151  corrects a position of the attention region from the attention region estimation unit  21  and the parallax map from the parallax detection unit  22  based on the shift amount s supplied from the shift amount calculation unit  103 . For example, a position of the attention region is shifted (moved in parallel) by the shift amount s, and the shift amount s is added to a pixel value (parallax) of each pixel of the parallax map, for generation of a corrected parallax map. In addition, the maximum and minimum parallax detection portion  151  supplies the parallax map corrected based on the shift amount s to the parallax conversion unit  24 . 
     The attention region and the parallax map are corrected, and, thereafter, the image conversion process finishes through processes in steps S 107  to S 111 . The processes are the same as the processes in steps S 13  to S 17  of  FIG. 4 , and thus description thereof will be omitted. 
     However, in steps S 107  and S 108 , a parallax of the attention region, a maximum parallax, and a minimum parallax are obtained based on the corrected attention region and parallax map. In addition, in step S 110 , the parallax conversion unit  24  converts the parallax map after being corrected which is supplied from the maximum and minimum parallax detection portion  151  into a corrected parallax map using the conversion function from the setting portion  152 , and supplies the corrected parallax map to the image synthesis unit  25 . In addition, the parallax conversion unit  24  obtains a parallax map before being corrected from the maximum and minimum parallax detection portion  151  as necessary, for supply to the image synthesis unit  25 . 
     In addition, since the parallax map or the attention region is corrected based on the shift amount s in the image processing apparatus  131 , a conversion function is shifted through the correction, for example, as shown in  FIG. 20 . In addition, in  FIG. 20 , the transverse axis indicates a parallax d i  of each pixel on the parallax map, and the longitudinal axis indicates a corrected parallax d o . A bent line F 71  in the figure indicates a graph of the conversion function. 
     For example, if a maximum parallax, an attention parallax, and a minimum parallax in the parallax map after being corrected is db max , db at  and db min , the parallaxes are shifted to d max , d at , and d min  through correction of the attention region and the parallax map based on the shift amount s. In addition, after the shift is performed, a conversion function is set in which a parallax of each pixel on the parallax map is a parallax in the allowable parallax range, and the attention parallax d at  is converted into 0. In the example shown in  FIG. 20 , the attention parallax d at  is 0, and, thus, the attention parallax d at  is converted into the corrected parallax d o =0 in the nonlinear conversion function indicated by the bent line F 71 . 
     As described above, the image processing apparatus  131  calculates a shift amount s by generating the histogram for each region, corrects the attention region and the parallax map based on the shift amount s, then sets the conversion function, and generates the corrected parallax map. 
     As such, if the parallax map and the attention region are corrected based on the shift amount s, it is possible to make a parallax of the corrected attention region almost 0. In addition, if a conversion function is set from the parallax map and the attention region corrected in this way, it is possible to convert a parallax of each pixel of the stereoscopic image into a parallax in an appropriate parallax range and to thereby perform a more appropriate parallax control. 
     Fifth Embodiment 
     Configuration Example of Image Processing Apparatus 
     In a case where the parallax map is converted into a corrected parallax map using a conversion function, and parallax adjustment of the stereoscopic image is performed using the corrected parallax map, the attention region or the conversion function may be smoothened taking account of time variations in the attention region or the conversion function. 
     In this case, an image processing apparatus is configured, for example, as shown in  FIG. 21 . In addition, in  FIG. 21 , parts corresponding to the case in  FIG. 3  are given the same reference numerals, and description thereof will be appropriately omitted. 
     The image processing apparatus  181  in  FIG. 21  includes an attention region estimation unit  21 , a smoothening unit  191 , a parallax detection unit  22 , a parallax analysis unit  23 , a parallax conversion unit  24 , and an image synthesis unit  25 . The image processing apparatus  181  is different from the image processing apparatus  11  in  FIG. 3  in that the smoothening unit  191  is newly provided, and a smoothening portion  201  is provided in the parallax conversion unit  24 , and the remaining configuration is the same as the configuration of the image processing apparatus  11 . 
     The smoothening unit  191  holds the attention region supplied from the attention region estimation unit  21  for a specific period, smoothens the held attention region, and supplies the smoothened attention region to the maximum and minimum parallax detection portion  31 . 
     In addition, the smoothening portion  201  provided in the parallax conversion unit  24  holds a conversion table generated based on the conversion function supplied from the parallax analysis unit  23 , and smoothens the held conversion table. The parallax conversion unit  24  converts the parallax map into a corrected parallax map using the conversion table smoothened by the smoothening portion  201 . 
     Description of Image Conversion Process 
     Next, an image conversion process performed by the image processing apparatus  181  will be described with reference to the flowchart of  FIG. 22 . 
     In step S 141 , the attention region estimation unit  21  estimates an attention region based on the supplied left eye image L and right eye image R, and supplies the estimation result to the smoothening unit  191 . In addition, in step S 141 , the same process as in step S 11  of  FIG. 4  is performed. 
     In step S 142 , the smoothening unit  191  smoothens the attention region using several attention regions which were supplied from the attention region estimation unit  21  in the past and the attention region supplied from the attention region estimation unit  21  this time, and supplies the smoothened attention region to the maximum and minimum parallax detection portion  31 . 
     For example, as shown in  FIG. 23 , it is assumed that attention regions TR- 1  to TR-N on left eye images L- 1  to L-N of continuous N frames including a current frame are smoothened, and an attention region TR-N′ obtained as a result thereof is an attention region of the left eye image L-N of the current frame. 
     Here, it is assumed that the left eye images L- 1  to L-(N−1) (here, the left eye images L- 3  to L-(N−1) are not shown) are left eye images of the past frames. In this case, the smoothening unit  191  applies a filter process for coordinates of the respective vertices of the rectangular attention regions TR- 1  to TR-N, and uses coordinates obtained as a result thereof as coordinates of the respective vertices of the attention region TR-N′ after being smoothened. 
     Therefore, for example, the filter process is performed using vertices U- 1  to U-N which are top left vertices of the attention regions TR- 1  to TR-N, and a coordinate obtained as a result thereof is a coordinate of the top left vertex U-N′ of the attention region TR-N′ of the current frame. 
     Here, the smoothening is performed using an average value, a rule of majority, a filter process using an IIR (Infinite Impulse Response) filter, or the like. Therefore, for example, in a case where the smoothening is performed using an average value, an average value of coordinates of the vertices U- 1  to U-N is used as a coordinate of the vertex U-N′. 
     As such, if the attention region is smoothened, time variations in the attention region are suppressed, and thus it is possible to perform a stable parallax control. In other words, it is possible to suppress a parallax on the stereoscopic image from being steeply varied. 
     Referring to the flowchart of  FIG. 22  again, the attention region is smoothened, and, then, processes in step S 143  to S 146  are performed. The processes are the same as the processes in steps S 12  to S 15  of  FIG. 4 , and thus description thereof will be appropriately omitted. However, in step S 145 , a parallax of the attention region is detected based on the smoothened attention region. 
     If the conversion function (conversion characteristics) of the stereoscopic image of the current frame is set by the setting portion  32 , the parallax conversion unit  24  generates a conversion table based on the conversion function supplied from the setting portion  32 . The conversion table is a table where the parallax d i  and the parallax d o  which are input and output of the conversion function are recorded so as to be correlated with each other. 
     In step S 147 , the smoothening portion  201  smoothens the conversion table using the conversion table of the current frame generated by the parallax conversion unit  24  and recorded conversion tables of several frames in the past. 
     For example, a filter process is performed using a corrected parallax d o  correlated with the same parallax d i  in such a conversion table, and a corrected parallax obtained as a result thereof is a corrected parallax corresponding to the parallax d i  in a conversion table of the current frame after being smoothened. The smoothening of the conversion table is also performed using, for example, an average value, a rule of majority, a filter process using an IIR filter, or the like. 
     The smoothening of the conversion table is equivalent to smoothening of a conversion function of a plurality of continuous frames, for example, as shown in  FIG. 24 . In addition, in  FIG. 24 , each of bent lines FU- 1  to FU-N (here, the bent lines FU- 2  to FU-(N−1) are not shown) indicates a conversion function of each frame. In addition, in the graph of the conversion function, the transverse axis indicates a parallax d i , and the longitudinal axis indicates a corrected parallax d o . 
     As such, if the conversion table is smoothened, time variations in the parallax conversion characteristics are suppressed, and thus it is possible to perform a stable parallax control. In other words, it is possible to suppress a parallax on the stereoscopic image from being steeply varied. 
     Referring to the flowchart of  FIG. 22  again, the conversion table is smoothened, and, then, the image conversion process finishes through processes in steps S 148  and S 149 . In addition, the processes are the same as the processes in steps S 16  and S 17  of  FIG. 4 , and thus description thereof will be omitted. However, in step S 148 , the parallax map is converted into a corrected parallax map using the conversion table obtained through the smoothening. 
     As described above, the image processing apparatus  181  generates the corrected parallax map and adjusts a parallax of the stereoscopic image by smoothening the attention region and the conversion table. Thereby, it is possible to perform a more appropriate and stable parallax control. 
     In addition, although a case where both the attention region and the conversion table are smoothened has been described as an example in the embodiment, only one of the attention region and the conversion table may be smoothened. Further, the attention region or the conversion table may be also smoothened in the above-described second to fourth embodiments. 
     Sixth Embodiment 
     Configuration Example of Image Processing Apparatus 
     Although, in the above description, a case where an attention region is estimated and a parallax control is performed taking account of the attention region has been described as an example, scene recognition of a stereoscopic image may be performed, and the recognition result may be used for the parallax control. 
     In this case, an image processing apparatus is configured as shown in  FIG. 25 . In addition, in  FIG. 25 , parts corresponding to the case in  FIG. 3  are given the same reference numerals, and description thereof will be appropriately omitted. 
     An image processing apparatus  231  in  FIG. 25  includes a scene recognition unit  241 , a parallax detection unit  22 , a parallax analysis unit  242 , parallax conversion unit  243 , and an image synthesis unit  25 . 
     The scene recognition unit  241  performs scene recognition for at least one of the supplied left eye image L and right eye image R, and supplies the recognition result to the parallax conversion unit  243 . In addition, the parallax detection unit  22  generates a parallax map from the supplied left eye image L and right eye image R, and supplies the parallax map to the parallax analysis unit  242  and the parallax conversion unit  243 . 
     The parallax analysis unit  242  sets a conversion function (conversion characteristics) based on the parallax map from the parallax detection unit  22 , and supplies the conversion function to the parallax conversion unit  243 . The parallax conversion unit  243  generates a corrected parallax map based on the scene recognition result from the scene recognition unit  241 , the parallax map from the parallax detection unit  22 , and the conversion function from the parallax analysis unit  242 , for supply to the image synthesis unit  25 . 
     Configuration Example of Parallax Conversion Unit 
     In addition, the parallax conversion unit  243  in  FIG. 25  is configured as shown in  FIG. 26 . In other words, the parallax conversion unit  243  includes a conversion table generation portion  271 , a parallax conversion map generation portion  272 , a parallax map synthesis portion  273 , and a parallax map conversion portion  274 . 
     The conversion table generation portion  271  generates a conversion table based on the conversion function supplied from the parallax analysis unit  242 , and supplies the conversion table to the parallax map conversion portion  274 . The parallax conversion map generation portion  272  generates a parallax conversion map based on the scene recognition result from the scene recognition unit  241 , and supplies the parallax conversion map to the parallax map synthesis portion  273 . Here, the parallax conversion map is a map indicating corrected parallaxes for correcting the parallax map according to scenes of the stereoscopic image. 
     The parallax map synthesis portion  273  synthesizes the parallax map from the parallax detection unit  22  with the parallax conversion map from the parallax conversion map generation portion  272 , and supplies the synthesized parallax map obtained as a result thereof to the parallax map conversion portion  274 . The parallax map conversion portion  274  converts a parallax of each pixel of the synthesized parallax map from the parallax map synthesis portion  273  using the conversion table from the conversion table generation portion  271 , and supplies the corrected parallax map obtained as a result thereof to the image synthesis unit  25 . 
     Description of Image Conversion Process 
     Next, an image conversion process performed by the image processing apparatus  231  will be described with reference to the flowchart of  FIG. 27 . 
     In step S 171 , the scene recognition unit  241  performs scene recognition for at least one of the supplied left eye image L and right eye image R, and supplies the recognition result to the parallax conversion unit  243 . 
     For example, the scene recognition unit  241  specifies an image of which scene of several scenes such as predefined “street”, “landscape”, “indoor”, and “portrait” is the stereoscopic image, and uses information indicating the specified scene as a result of the scene recognition. Thereby, a scene in which the stereoscopic image is captured is specified. 
     In step S 172 , the parallax detection unit  22  detects a parallax of each pixel of the stereoscopic image based on the supplied left eye image L and right eye image R, and supplies the parallax map obtained as a result thereof to the parallax analysis unit  242  and the parallax conversion unit  243 . 
     In step S 173 , the parallax analysis unit  242  detects a maximum parallax and a minimum parallax of the parallaxes of the pixels on the parallax map supplied from the parallax detection unit  22 . In addition, in step S 174 , the parallax analysis unit  242  sets conversion characteristics (conversion function) such that a parallax of each pixel on the parallax map becomes a parallax in the allowable parallax range, based on the detected maximum parallax and minimum parallax, and the predefined allowable parallax range. Further, the parallax analysis unit  242  supplies the set conversion function to the conversion table generation portion  271 . 
     In step S 175 , the conversion table generation portion  271  generates a conversion table based on the conversion function supplied from the parallax analysis unit  242 , and supplies the conversion table to the parallax map conversion portion  274 . 
     In step S 176 , the parallax conversion map generation portion  272  generates a parallax conversion map based on the scene recognition result supplied from the scene recognition unit  241 , and supplies the parallax conversion map to the parallax map synthesis portion  273 . More specifically, the parallax conversion map generation portion  272  records a parallax conversion map defined for each scene in advance, and selects a parallax conversion map for a scene indicated by the scene recognition result for supply to the parallax map synthesis portion  273 . 
     In step S 177 , the parallax map synthesis portion  273  synthesizes the parallax map from the parallax detection unit  22  with the parallax conversion map from the parallax conversion map generation portion  272 , and supplies the synthesized parallax map obtained as a result thereof to the parallax map conversion portion  274 . 
     For example, as shown in  FIG. 28 , the parallax conversion map generation portion  272  records parallax conversion maps CM 11  to CM 13  for the respective scenes. 
     In the example shown in  FIG. 28 , the parallax conversion maps CM 11  to CM 13  respectively correspond to parallax conversion maps for the scenes of “street”, “landscape” and “indoor”. 
     For example, the parallax conversion map CM 11  is a parallax conversion map of the “street” scene. Therefore, according to the parallax conversion map CM 11 , the parallax map is corrected such that a subject in the central part region of the stereoscopic image is positioned further on the depth side when viewed from a user, and subjects in regions around the ends of the stereoscopic image are positioned further on the front side when viewed from the user. For example, a pixel value (parallax) of each pixel of the central region of the parallax conversion map CM 11  has a negative value, and a pixel value of each pixel of the regions around the ends thereof has a positive value. 
     Typically, in the street image, there are many cases where a road is located at the center of the image, and buildings at both sides of the roads are located in the regions around the ends of the images. Therefore, if a parallax of each pixel on the parallax map is corrected such that the road part is depressed inward, and the building part protrudes forward, it is possible to further emphasize a stereoscopic effect of the stereoscopic image. 
     In addition, the parallax conversion map CM 12  is a parallax conversion map for the “landscape” scene. Therefore, according to the parallax conversion map CM 12 , the parallax map is corrected such that a subject in the upper region of the stereoscopic image in the figure is positioned further on the inner side, and a subject in the lower region of the stereoscopic image is positioned further on the front side. 
     Typically, in the landscape image, there are many cases where the sky is located on the upper side of the image, and other subjects are located on the lower part of the image. Therefore, if a parallax of each pixel on the parallax map is corrected such that the sky part located on the upper part of the image is depressed inward, and the other main subject part protrudes forward, it is possible to further emphasize a stereoscopic effect of the stereoscopic image. 
     In addition, the parallax conversion map CM 13  is a parallax conversion map for the “indoor” scene. Therefore, in the parallax conversion map CM 13 , the entire stereoscopic image, that is, the entire parallax map is uniformly corrected. This is because, in the image captured indoors, a wall or the like occupies the background, and thus it is not necessary to particularly emphasize perspective in many cases. 
     The parallax conversion map generation portion  272  selects any one of the parallax conversion maps prepared in advance in this way. For example, if the “street” scene is recognized and the parallax conversion map CM 11  is selected, the parallax map synthesis portion  273  synthesizes the parallax map DM 61  with the parallax conversion map CM 11 , and uses the synthesized result as a synthesized parallax map BM 11 . 
     In other words, if a pixel to which attention is paid on the synthesized parallax map BM 11  is an attention pixel, a sum of pixel values of pixels of the parallax map DM 61  and the parallax conversion map CM 11  located at the same position as the attention pixel is obtained, and a value of the obtained sum is used as a pixel value of the attention pixel. 
     Referring to the flowchart of  FIG. 27  again, if the synthesized parallax map is generated, the flow proceeds to step S 178  from step S 177 . 
     In step S 178 , the parallax map conversion portion  274  converts a parallax of each pixel of the synthesized parallax map from the parallax map synthesis portion  273  using the conversion table from the conversion table generation portion  271 , and supplies a corrected parallax map obtained as a result thereof to the image synthesis unit  25 . In addition, the parallax map conversion portion  274  obtains a parallax map from the parallax map synthesis portion  273  as necessary, and supplied the obtained parallax map to the image synthesis unit  25 . 
     The corrected parallax map is generated, and, then, the image conversion process finishes through a process in step S 179 . The process is the same as the process in step S 17  of  FIG. 4 , and thus description thereof will be omitted. 
     In this way, the image processing apparatus  231  performs scene recognition, corrects the parallax map according to the recognition result so as to generate a synthesized parallax map, and converts the synthesized parallax map into a corrected parallax map based on a conversion table, thereby performing image conversion of the stereoscopic image. 
     As such, the parallax map is corrected according to a result of the scene recognition, and thereby it is possible to appropriately emphasize of a parallax of the stereoscopic image according to a scene of the stereoscopic image. In other words, it is possible to perform a more appropriate parallax control. 
     Further, in the image processing apparatus  231  as well, an attention region of the stereoscopic image may be estimated and a conversion function may be set based on the estimation result, or a conversion function may be set using a scene recognition result. 
     For example, in a case where a conversion function is set using a scene recognition result, a parallax conversion map can be specified from the scene recognition result, and thus it is possible to specify a maximum parallax and a minimum parallax of the synthesized parallax map. Therefore, if a conversion function is set based on the maximum parallax and the minimum parallax of the synthesized parallax map such that a parallax of each pixel of the synthesized parallax map becomes a parallax in the allowable parallax range, it is possible to perform a more appropriate parallax control. 
     The above-described series of processes may be performed by hardware or software. When a series of processes is performed by the software, programs constituting the software are installed from a program recording medium to a computer which is built in dedicated hardware, or, for example, a general personal computer which can execute various kinds of functions by installing various kinds of programs. 
       FIG. 29  is a block diagram illustrating a hardware configuration example of a computer which executes the series of processes using a program. 
     In the computer, a CPU (Central Processing Unit)  501 , a ROM (Read Only Memory)  502 , and a RAM (Random Access Memory)  503  are connected to each other via a bus  504 . 
     Also, the bus  504  is connected to an input and output interface  505 . The input and output interface  505  is connected to an input unit  506  including a keyboard, a mouse, a microphone, and the like, an output unit  507  including a display, a speaker and the like, a storage unit  508  including a hard disc, a nonvolatile memory and the like, a communication unit  509  including a network interface and the like, and a drive  510  which drives a removal medium  511  such as a magnetic disc, an optical disc, a magnetic optical disc, or a semiconductor memory. 
     In the computer configured in this way, the CPU  501  loads, for example, a program stored in the storage unit  508  to the RAM  503  via the input and output interface  505  and the bus  504  for execution, thereby performing the series of processes. 
     The program executed by the computer (the CPU  501 ) is recorded in the removable medium  511  which is a package medium including a magnetic disc (including a flexible disc), an optical disc (including CD-ROM (Compact Disc-Read Only Memory) and DVD (Digital Versatile Disc)), a magnetic optical disc, a semiconductor memory, or the like, or is provided using a wired or wireless communication medium such as a LAN, the Internet, or digital satellite broadcasting. 
     The program can be installed in the storage unit  508  via the input and output interface  505  by mounting the removable medium  511  in the drive  510 . In addition, the program may be received using the communication unit  509  via a wired or wireless transmission medium and be installed in the storage unit  508 . Further, the program may be installed in the ROM  502  or the storage unit  508  in advance. 
     The program executed by the computer may be a program where processes are performed in a time series according to the order described in this specification, or may be a program executed in parallel therewith or a program where processes are performed at a necessary timing such as when accessed. 
     The present disclosure may employ the following configurations. 
     [1] An image processing apparatus including an attention region estimation unit that estimates an attention region which is estimated as a user paying attention thereto on a stereoscopic image; a parallax detection unit that detects a parallax of the stereoscopic image and generates a parallax map indicating a parallax of each region of the stereoscopic image; a setting unit that sets conversion characteristics for correcting a parallax of the stereoscopic image based on the attention region and the parallax map; and a parallax conversion unit that corrects the parallax map based on the conversion characteristics. 
     [2] The image processing apparatus set forth in [1], further including an image synthesis unit that corrects a parallax of the stereoscopic image based on the corrected parallax map. 
     [3] The image processing apparatus set forth in [1] or [2], further including a maximum and minimum parallax detection unit that detects a maximum value and a minimum value of parallaxes indicated by the parallax map and detects a parallax of the attention region based on the parallax map and the attention region, wherein the setting unit sets the conversion characteristics based on the maximum value, the minimum value, and the parallax of the attention region. 
     [4] The image processing apparatus set forth in [3], wherein the setting unit sets the conversion characteristics such that the parallax of the attention region is converted into a parallax with a predetermined size set in advance. 
     [5] The image processing apparatus set forth in any one of [1] to [4], wherein the setting unit sets different conversion characteristics for the attention region on the stereoscopic image and regions other than the attention region on the stereoscopic image. 
     [6] The image processing apparatus set forth in [5], wherein the setting unit sets the conversion characteristics of the attention region on the stereoscopic image such that a parallax is linearly converted in a predetermined parallax section including the parallax of the attention region. 
     [7] The image processing apparatus set forth in any one of [1] to [6], further including a smoothening unit that smoothens the attention region or the conversion characteristics. 
     The present disclosure contains subject matter related to that disclosed in Japanese Priority Patent Application JP 2011-128101 filed in the Japan Patent Office on Jun. 8, 2011, the entire contents of which are hereby incorporated by reference. 
     It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.