Patent Publication Number: US-8970672-B2

Title: Three-dimensional image processing

Description:
I. CLAIM OF PRIORITY 
     The present application claims priority to U.S. Provisional Patent Application No. 61/349,738, entitled “THREE-DIMENSIONAL IMAGE PROCESSING,” filed May 28, 2010, incorporated herein by reference in its entirety. 
    
    
     II. FIELD 
     The present disclosure is generally related to three dimensional (3D) image processing. 
     III. DESCRIPTION OF RELATED ART 
     Advances in technology have resulted in smaller and more powerful computing devices. For example, there currently exist a variety of portable personal computing devices, including wireless computing devices, such as portable wireless telephones, personal digital assistants (PDAs), and paging devices that are small, lightweight, and easily carried by users. Many such wireless telephones include other types of devices that are incorporated therein. For example, a wireless telephone can also include a digital still camera, a digital video camera, a digital recorder, and an audio file player. Also, such wireless telephones can process executable instructions, including software applications, such as a video file player. 
    
    
     
       IV. BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a particular embodiment of a system that includes a 3D playback device configured to perform adaptive convergence in response to a zoom or pan instruction during playback of a media file; 
         FIG. 2  is a block diagram of a particular embodiment of a system that includes a 3D capture device configured to perform adaptive convergence in response to a zoom instruction during image or video capture; 
         FIG. 3  is a diagram of a particular embodiment of a 3D capture device configured to detect disparity of objects in a scene during image or video capture; 
         FIG. 4  is a block diagram of a particular embodiment of a 3D image processing system that can be included in any of the systems of  FIGS. 1-3 ; 
         FIGS. 5A and 5B  are diagrams of illustrative embodiments of object disparities correlated with perceived object depths; 
         FIG. 6  is a block diagram of a particular embodiment of a 3D image processing system that can be included in any of the systems of  FIGS. 1-3 ; 
         FIG. 7  is a diagram illustrating a particular embodiment of a subsampled luma component of a scene and a result of a horizontal variation detection filter applied to the subsampled luma component; 
         FIGS. 8A and 8B  are diagrams of illustrative embodiments of local horizontal spatial maxima in the filtered result of  FIG. 7  and neighborhoods of each local horizontal spatial maximum in the subsampled luma component of the scene of  FIG. 7 ; 
         FIG. 9  is a histogram of a particular illustrative embodiment illustrating disparity of key points in a scene; 
         FIG. 10  is a diagram of a particular embodiment depicting effects of viewing distance on 3D perception of a scene; 
         FIG. 11  is a diagram of illustrative embodiments depicting disparity dependency on display viewing distance; 
         FIG. 12  is a diagram of a particular embodiment depicting effects of a zoom operation on 3D perception of a scene; 
         FIG. 13  is a histogram of a particular illustrative embodiment depicting scene disparity and convergence point selection; 
         FIG. 14  is a flow diagram of a first embodiment of a method of 3D image processing; 
         FIG. 15  is a flow diagram of a second embodiment of a method of 3D image processing; and 
         FIG. 16  is a flow diagram of a third embodiment of a method of 3D image processing. 
     
    
    
     V. DETAILED DESCRIPTION 
     Systems and methods of three dimensional (3D) image processing are disclosed. A three-dimensional (3D) media player can receive input data corresponding to a scene and provide output data to a 3D display device. The 3D media player is responsive to user input including a zoom command and a pan command. The 3D media player includes a convergence control module configured to determine a convergence point of a 3D rendering of the scene responsive to the user input. The convergence point can be adaptively determined on a scene-by-scene basis and can be set according to a display scene geometry, viewing distance, and zoom/pan user input. 
     3D perception can occur in a viewer&#39;s mind as a result of fusing an object that appears in a left image of a scene and with a corresponding object that appears in a right image of the same scene. A perceived depth of the object is based on the object&#39;s disparity, i.e. a location shift of the object between the left and right images. Disparity (left and right) image shift plays a role in 3D effects and can produce discomfort/headache if not handled correctly. Display dimension and viewing distance affect disparity and can produce undesired effects if not compensated for. Zoom functionality can be an attribute to a 3D suite and also affects the disparity (in real time). 
     Disparity constraints may be configured based on user preferences. Scene-dependent disparity constraints can be derived in real time based on display size and distance and zoom factor. Scene disparity can be measured and if the scene disparity is within the constraints, a convergence point of the scene may be adjusted. If the scene disparity is not within constrains, processing may switch to a special mode to bring disparity within the constraints. 
     Special processing (3D effect control) may be used depending on one or more of: display viewing angle, display size, distance between viewer and display, and zoom factor. Consideration may be made to fully use an available range and to produce a 3D effect that is comfortable to the user. 
     Humans can only interpret (fuse) up to a certain angular disparity, after that ghosting appears (double images). An object&#39;s perceived depth may be solely dependent on the angular disparity. Angular disparity depends on a combination of screen size, screen resolution, and viewing distance. 
     Referring to  FIG. 1 , a particular illustrative embodiment of a system includes a portable electronic device such as a camera phone  102  coupled to a computing device  120 . In a particular embodiment, the computing device  120  is a personal computer that is coupled to a display device  130 . The camera phone  102  is coupled to provide 3D image data  158  to the computing device  120  via an interface. The camera phone  102  includes an image sensor pair  104 , a synchronization application-specific integrated circuit (ASIC), a video front end component  108 , and external memory  114 . The synchronization ASIC includes synchronization and interface functionality  106  and is responsive to the image sensor pair  104 . The synchronization and interface functionality  106  receives image data  150  which includes first and second image data from the image sensor pair  104 . The video front end component  108  is coupled to the synchronization ASIC and includes one or more image processing functions  110  and 3D video encoding functionality  112 . The video front end component  108  provides video encoded data  156  to the external memory  114 . The video front end component  108  may be included in a mobile device chipset, such as a Mobile Station Modem™ (MSM™)-type chipset that may also enable wireless connectivity and application processing capability at the camera phone  102 . (Mobile Station Modem and MSM are trademarks of Qualcomm Incorporated.) 
     The computing device  120  includes an in-house player with a user interface  126 , a realtime playback component  124 , and a storage device, such as a personal computer (PC) hard drive  122 . The realtime playback component  124  includes fine image alignment and adaptive convergence functionality. The computing device  120  is configured to process the 3D image data  158  and to provide an output signal  166  to the 3D display device  130 . 
     During operation, an image is captured by a camera device within the camera phone  102 . The captured image is detected by the image sensor pair  104  and first and second image data  150  corresponding to first and second images related to a scene is provided to the synchronization ASIC. The synchronization ASIC performs synchronization of operation of the sensor pair  104  and AE functionality, such as auto-exposure and white balancing type processing. Synchronized and processed data  152  from the synchronization ASIC is provided to the video front end component  108 . The video front end component  108  performs the one or more image processing functions  110  and provides the processed data  154  to the 3D video encoding module  112 . The 3D video encoding module  112  provides 3D video encoding and provides a video encoded output stream  156  including video encoded data which can be stored in the external memory  114 . Data from the external memory  114  can be provided via an output connection and the 3D image data  158  can be output for delivery to the computing device  120 . 
     The computing device  120 , responsive to the 3D image data  158 , stores the 3D image data at the PC hard drive  122 . The 3D image data may be retrieved from the PC hard drive  122  and provided as input data  160  to the realtime playback component  124 . The realtime playback component  124  performs fine image alignment and adaptive convergence processing to the input data  160 . Adaptive convergence and image alignment processing produces further processed image data  162  which is provided to the in-house player  126 . The in-house player  126  is responsive to user input to perform certain user requested commands. For example, the in-house player  126  is responsive to a zoom or pan command from a user. Responsive to the user command, the in-house player  126  provides a zoom/pan command  164  indicating zoom/pan control via a feedback path to the realtime playback component  124 . The image alignment and adaptive convergence module  124 , responsive to the zoom/pan command  164 , performs adaptive convergence in response to a change in perceived scene depth resulting from the zoom or pan operation. 
     Thus, the computing device  120  includes a 3D media player configured to receive input data that includes at least a first image corresponding to a scene and second image corresponding to the scene. For example, the first image and the second image may be captured by the image sensor pair  104  and processed to generate 3D image data. The 3D media player is configured to provide output data to a 3D display device. For example, the realtime playback component  124  combined with the in-house player  126  may provide the output data  166  to the 3D display device  130 . The 3D media player is responsive to user input including at least one of a zoom command and a pan command. For example, the realtime playback component  124  may perform adaptive convergence responsive to either a zoom command or a pan command  164  received via the zoom/pan control path. The 3D media player is configured to determine a convergence point of a 3D rendering of the scene responsive to the user input. 
       FIG. 2  is a diagram of a particular embodiment of a system that includes a 3D capture device  202  configured to perform adaptive convergence in response to a zoom instruction during image or video capture. The 3D capture device  202  includes an image sensor pair  204  coupled to an image processing module  208 . A user interface device  280 , an external memory  214 , and a 3D preview display device  284  are coupled to the image processing module  208 . A 3D video player  220  is illustrated as coupled to a 3D display device  230  to display 3D image data  258  provided by the image capture device  202 . 
     The image sensor pair  204  is configured to provide image data  250  to the image processing module  208 . For example, the image sensor pair  204  may include complementary metal-oxide-silicon (CMOS)-type image sensors or charge coupled device (CCD)-type image sensors. Each image sensor of the image sensor pair  204  may concurrently capture image data corresponding to a scene being captured by the 3D capture device  202 . Image data  250  may be read out of the image sensor pair  204  and may be provided to the image processing module  208 . In a particular embodiment, the image sensor pair  204  is set in a substantially parallel alignment and is not controllable to rotate or “toe in” during zoom operations. 
     The image processing module  208  may correspond to the synchronization ASIC and the video front end  108  of  FIG. 1 . The image processing module  208  may include a synchronization and interfacing module  206 , an image processing functions module  210 , a 3D processing with convergence control module  240 , and a 3D video encoding module  212 . The synchronization and interfacing module  206  may include circuitry to control synchronization of operation of the image sensor pair  204  and to control one or more other operating parameters of the image sensor pair  204 . 
     The image processing functions module  210  may be configured to receive input data  252  and perform one or more image processing functions, such as color correction, color conversion, and noise reduction. For example, the image processing functions module  210  may be configured to perform the image processing functions  110  of  FIG. 1  and to generate processed image data  270 . 
     The 3D processing with convergence control module  240  may be configured to perform 3D-specific processing of a scene represented by the processed image data  270 . The 3D processing with convergence control module  240  may be configured to perform convergence-point processing on a scene-by-scene basis. 
     For example, the 3D processing with convergence control module  240  may be configured to determine a convergence point of a 3D rendering of the scene. The 3D processing with convergence control module  240  may be configured to determine disparity values corresponding to objects within the scene and to determine the convergence point based on the disparity values of objects in the scene. The 3D processing with convergence control module  240  may be configured to determine whether the disparity values result in at least one of the objects not being fusable in the 3D rendering, and an alert may be generated at the user interface device  280  to inform a user of the device  202  that the scene is not fusable during 3D video capture of the scene. 
     The 3D processing with convergence control module  240  may be responsive to a predetermined display geometry. For example, the convergence-point processing may include adjusting a scene-based convergence point based on disparity values corresponding to objects in the scene and further based on the predetermined display geometry. The convergence-point processing on the scene-by-scene basis enables dynamic adjustment of the convergence point during a zoom operation performed by the first image sensor and the second image sensor of the image sensor pair  204  during 3D video capture. 
     For example, the predetermined display device geometry may correspond to the 3D video preview display device  284 . Alternatively, or in addition, the predetermined device geometry may correspond to a “generic” or default device geometry that may be generally applicable to 3D video display devices. In a particular embodiment, multiple predetermined device geometries may be stored at the image capture device  202 , and the predetermined display device geometry corresponds to a display geometry that is selected according to a user input. For example, the user interface device  280  may present a menu of selectable options that enable a user to select a first display geometry corresponding to a first display device (e.g. the 3D display device  230 ) when recording a first 3D movie and to select a different display geometry corresponding to another display device (not shown) when recording another 3D movie. 
     The 3D video encoding module  212  may be a 3D encoding module configured to generate 3D output data  256  based on an output  272  of the 3D processing with convergence control module  240 . For example, when the output  272  corresponds to a still image, the 3D video encoding module  212  may be configured to encode the output  272  based on a Joint Photographic Experts Group (JPEG) encoding standard for 3D images. As another example, when the output  272  corresponds to video data, the 3D video encoding module may be configured to encode the output  272  based on a Moving Pictures Experts Group (MPEG) encoding standard for 3D images. The 3D video encoding module  212  may output 3D data  256  that may be provided to the 3D preview display device  284 , the external memory  214 , or to both. 
     The 3D preview display device  284  may display the 3D data  256  from the image processing module  208  to enable a user to view 3D scenes during image or video capture. For example, the image processing module  208  may generate reduced resolution video data for viewing by a user during video capture via the 3D preview display device  284 . 
     By performing adaptive convergence control on a scene-by-scene basis, the image capture device  202  may enable zoom functionality during 3D video capture. For example, a user input  282  may be received via the user interface device  280  indicating a zoom command. As described in further detail with respect to  FIGS. 10-14 , a scene that may otherwise become unfusable in response to a zoom operation can be accommodated using adaptive convergence control. As a result, if the 3D video player  220  is not capable of processing 3D video to adjust a convergence point during playback, portions of 3D image data  258  that would otherwise be unfusable at the 3D display device  230  (e.g. due to zooming during image capture) may instead be fusable due to the processing performed at the image capture device  202 . 
       FIG. 3  is a diagram of a particular embodiment of a 3D capture device  302  configured to detect disparity of objects in a scene during image or video capture. The 3D capture device  302  includes an image sensor pair  304  coupled to an image processing module  308 . The image processing module  308  is coupled to a user interface device  380  and to an external memory  314 . The image processing module  308  includes a synchronization and interfacing module  306 , an image processing functions module  310 , a disparity detection module  342 , and a 3D video encoding module  312 . 
     The image sensor pair  304  is configured to provide image data  350  to the image processing module  308 . The synchronization and interfacing module  306  is configured to provide data  352  to the image processing functions module  310 . The image processing functions module  310  is configured to provide processed image data  370  to the disparity detection module  342 . The 3D video encoding module  312  is configured to receive 3D video data  372  and to generate encoded 3D video data  356 . In a particular embodiment, the image sensor pair  304 , the user interface device  380 , the external memory  314 , the synchronization and interfacing module  306 , the image processing functions module  310 , and the 3D video encoding module  312  correspond to the image sensor pair  204 , the user interface device  280 , the external memory  214 , the synchronization and interfacing module  206 , the image processing functions module  210 , and the 3D video encoding module  212  of  FIG. 2 , respectively. 
     The disparity detection module  342  may be configured to determine disparity values corresponding to objects within a scene captured by the image sensor pair  304 , such as described in further detail with respect to  FIGS. 7-9 . The disparity detection module  342  may be configured to determine whether the disparity values result in at least one of the objects not being fusable in a 3D rendering of the scene, and in this case, an alert  382  may be generated at the user interface device  380  to inform a user of the device  302  that the scene is not fusable during 3D video capture of the scene. In a particular embodiment, the disparity detection module  342  incorporates scene-specific object detection or key point detection and disparity determination functionality of the 3D processing with convergence control module  240  of  FIG. 2 , but is not configured to adjust a convergence point of the scene during video capture. 
     The image sensor pair  304  is illustrated in a representative view as a pair of sensors including a right sensor (i.e. a first sensor that captures the image that is associated with the scene perceived by a viewer&#39;s right eye) and a left sensor (i.e. a second sensor that captures the image that is associated with the scene perceived by a viewer&#39;s left eye). The image data  350  includes left image data generated by the left sensor and right image data generated by the right sensor. Each sensor is illustrated as having rows of photo-sensitive components extending in a horizontal direction and columns of photo-sensitive components extending in a vertical direction. The left sensor and the right sensor are substantially aligned at a distance d from each other along a horizontal direction. As used herein, a “horizontal” direction within 3D image data is a direction of displacement between a location of an object in the right image data and a location of the same object in the left image data. 
       FIG. 4  is a diagram of a particular embodiment of a 3D image processing system  440  that can be included in any of the systems of  FIGS. 1-3 . The 3D processing system  440  is configured to receive input image data  404  and to generate output image data  428 . The 3D processing system  440  may be responsive to camera calibration parameters  406  received via a calibration input  450  and usage mode parameters  408  received via a usage mode input  452 . 
     The 3D processing system  440  includes a fine geometry compensation module  410 , a key point detection module  412 , a key point matching module  414 , a 3D convergence control module  416 , a 3D image adjustment module  418 , one or more of a smart 3D convergence module  420 , a smart two-dimensional (2D) module  422 , and another convergence control module  424 . The 3D processing system  440  may also includes an image crop and border correction module  426 . In a particular embodiment, the 3D processing system  440  may be implemented by a graphics processing unit (GPU) executing program instructions configured to cause the GPU to process the input image data  404  in a manner as described for one or more of the modules  410 - 426 . 
     The geometry compensation module  410  is configured to receive the input image data  404  via a data path  470  and to generate compensated image data  454 . The geometry compensation module  410  may use data from the camera calibration parameters  406  and may adjust the input image data  404  to correct for misalignment, aberration, or other calibration conditions that may adversely impact rendering of the 3D image data  404 . To illustrate, the geometry compensation module  410  may effectively perform a resampling of the image data  404  on an arbitrary grid to adjust for the calibration parameters  406 . 
     In an embodiment where the 3D processing system  440  is implemented in a 3D video playback device, such as the computing device  120  of  FIG. 1 , the camera calibration parameters  406  may be received with the input image data  404 , such as in a header of a 3D video data file. In an embodiment where the 3D processing system  440  is implemented in a 3D image capture device, such as the image capture devices  102  of  FIG. 1 ,  202  of  FIG. 2 , or  302  of  FIG. 3 , the camera calibration parameters  406  may correspond to an image sensor pair of the image capture device and may be stored in a memory accessible to the fine geometry compensation module  410 . 
     The key point detection module  412  is configured to receive the compensated image data  454  and to generate key point location data  456 . The key point detection module  412  is configured to identify distinctive points in the compensated image data  454 . For example, the distinctive points may correspond to vertical edges of objects in a scene or other points of the scene having a high-frequency component in the horizontal direction. Although such distinctive elements in the image data are referred to herein as “key points” or “objects” it should be understood that such identified elements may correspond to individual pixels, groups of pixels, fractional pixel portions, other image components, or any combination thereof. For example, as described further with respect to  FIGS. 7A-7B , the key points may correspond to pixels with a subsampled luma component of received image data and may be detected using a vertical edge detection filter. 
     The key point matching module  414  is configured to receive the key point location data  454  and to generate disparity data  458  corresponding to the identified key points. The key point matching module  414  may be configured to search around the key points within a search range and produce reliability measures of disparity vectors. 
     The 3D convergence control module  416  is configured to receive the disparity data  458  and to generate data  460  indicating a convergence point. For example, the 3D convergence control module  416  may extract the convergence point based on display geometry, zoom operations, pan operations, other display factors, or any combination thereof. The 3D convergence control module  416  may also control convergence loops. For example, the 3D convergence control module  416  may implement a filter, such as an infinite impulse response (IIR) filter, to slowly vary a convergence point based on a history of scenes to smooth scene changes and prevent large jumps in disparity from scene to scene. However, when an indication of a zoom or pan operation is received via the usage mode input  452 , the 3D convergence control module  416  may initiate a state reset of the filter to enable large variations of disparity to accommodate the zoom or pan operation. 
     The 3D image adjustment module  418  is configured to receive the data  460  indicating the convergence point and to selectively direct image processing to the 3D convergence adjustment module  420 , to the smart 3D module  422 , or to the other convergence control module  424 . For example, when a range of disparity of key points in the scene is within a predetermined range, the key points of the scene are fusable and the convergence point may be adjusted at the convergence adjustment module  420  by shifting at least one of the first image and the second image relative to the other of the first image and the second image. 
     As another example, when the range of disparity of the key points exceeds the predetermined range, processing may be performed at the smart 3D module  422 . The smart 3D module  422  may be configured to replace one of the first image and the second image with a shifted version of the other of the first image and the second image. A shift amount may be determined based on an average disparity or median disparity of the identified key points. A resulting scene may appear as a flat scene (i.e. with no perceived depth between objects in the scene) with all objects perceived at a depth appropriate for at least some objects in the scene. 
     Alternatively, when the range of disparity of the key points exceeds the predetermined range, processing may be performed at the other convergence module  424 . For example, the other convergence module  424  may be configured to adjust the image data by using one or more invasive techniques, such as by identifying an object in the scene that is not fusable and altering at least one of the first image and the second image to reposition the object to be fusable. 
     The image crop and border correction module  426  may be configured to receive fusable 3D image data  464  and to generate the output 3D data  428 . The image crop and border correction module  426  may be configured to perform one or more image crop operations of the fusable 3D image data  464 . The image crop and border correction module  426  may further be configured to perform border correction to the fusable 3D image data  464 . 
     During operation, a scene-dependent adaptive convergence process is performed that includes fine geometry compensation of the 3D image data  404  (e.g. by the fine geometry compensation module  410 ). A calibration procedure designed to estimate and compensate the relative position between the two sensors that captured the 3D image data  404  may be performed off-line (e.g. prior to delivery to an end-user of the device) but the geometry compensation may be performed for every frame of the 3D image data  404 . 
     Processing continues with key point detection (e.g. at the key point detection module  412 ). A set of objects or pixels (key points) of the image are selected that can be used to reliably estimate disparities. A high confidence in the estimated disparity may be achieved, and not all regions or objects in the scene may be used. Selection of the set of key points may include image sub-sampling to produce appropriate resolution(s). An image high pass filter may be applied (e.g. only looking for horizontal frequencies, corresponding to vertical features), followed by taking a square or absolute value of a result generated by applying the filter. Results exceeding a predetermined threshold may be identified as potential key points. A key points pruning process may be performed to the potential key points to select the best key point within some local neighborhood (e.g. the key point corresponding to a largest filter result of all key points within a predetermined region). 
     Key point matching may be performed using the detected key points (e.g. at the key point matching module  414 ). Correspondence between a key point in a first image (e.g. the left image or the right image) and the corresponding area in a second image (e.g. the other of the left image and the right image) may be determined. A reliability estimator may be produced, which together with key point selection may improve significantly the disparity estimation accuracy. Matching may be performed using a normalized cross-covariance to enable determination of how close the match is between the key points in the left image and the right image. A reliability measure may be based on the normalized cross-covariance. In a particular embodiment, a search range for locating a key point in a second image that corresponds to a key point in the first image is only horizontal because image compensation for sensor calibration has already been performed, and the search range is adjusted to only cover a certain range around the convergence point, which may be determined at the fine geometry compensation module  410 . 
     3D convergence control (e.g. at the 3D convergence control module  416 ) may be performed after the set of key points in the first image are matched to a set of corresponding key points in the second image. Based on sufficient statistics, such as disparity histogram screen geometry and crop window size, a decision can be made whether the scene can be rendered in 3D. If the scene can be rendered in 3D, then a horizontal shift is applied (e.g. via the 3D convergence adjustment  420 ). If the scene cannot be rendered in 3D, one or more of several possible fallbacks may be used, such as smart 2D processing, 2D to 3D processing, or invasive convergence control processing, as particular examples. 
     To determine whether the scene can be rendered in 3D, a histogram may be generated of disparity values for each key point of the selected set of key points. One or both tails of the histogram may be cropped. For example, when some of the disparity values are expected to be erroneous, reliability of determining whether the scene is fusable (i.e. all key point disparity values that are examined fall within a predetermined range of disparity values) may be improved by removing one or more of the largest disparity values or the smallest disparity values from the set of key points disparity values that are considered. The disparity range of the scene is then specified by the min/max of the histogram. 
     In a particular embodiment, at least a portion of the 3D image processing system  440  may be implemented in one or more of the systems of  FIGS. 1-3 . For example, the 3D image processing system  440  may be implemented in the fine image alignment and adaptive convergence module  124  of  FIG. 1 . As another example, the 3D image processing system  440  may be implemented within the 3D processing with convergence control module  240  of  FIG. 2 . As another example, the modules  410 - 416  of the 3D image processing system  440  may be implemented in the disparity detection module  342  of  FIG. 3 . 
     Although  FIGS. 1-4  describe pairs of image sensors used for 3D capture, in other embodiments more than two image sensors may be used. In some embodiments one or more of the various modules and components described in  FIGS. 1-4  may be implemented as dedicated hardware (i.e. circuitry), as a processor that executes processor executable instructions that are stored at a non-transient storage medium, such as an erasable programmable read-only memory (PROM)-type storage, a flash memory, a read-only memory (ROM) memory, or as a combination of hardware and instructions executing at a processing device (e.g. at a controller, a general-purpose processor, a digital signal processor (DSP), a graphics processing unit (GPU), or any combination thereof). 
       FIGS. 5A and 5B  are diagrams of illustrative embodiments of object disparities correlated with perceived object depths. Stereo 3D display relies on directing different images to each eye  504 ,  506 . The purpose is to recreate depth illusion from left and right (L/R) images, as object disparities (horizontal shift) are correlated with depths.  FIG. 5A  shows a positive disparity  550  corresponding to an object  530  perceived past a display surface  524 . The disparity  550  indicates a distance between a location  520  of the object in a left image and a location  522  of the object in a right image. An observer will fuse the image of the object  530  in the left image and the image of the object  530  in the right image to perceive the object  530  at an intersection of a line of sight  560  of the left eye  504  and a line of sight  562  of the right eye  506 . 
       FIG. 5B  shows a negative disparity  550  corresponding to the object  530  perceived in front of the display surface  524 . The disparity  550  indicates a distance between a location  520  of the object in a left image and a location  522  of the object in a right image. An observer will fuse the image of the object  530  in the left image and the image of the object  530  in the right image to perceive the object  530  in front of the display surface  534  at an intersection of the line of sight  560  of the left eye  504  and the line of sight  562  of the right eye  506 . 
     Object displacement as seen from the two eyes is interpreted by the visual cortex as depth. The visual cortex can interpret as depth up to a certain displacement. There is a range, [−d1 . . . d2], in which image fusion can happen without an eye strain. Disparity between two captured images will depend on the scene. Sensing the scene depth (or disparity range) allows for adjusting the disparity to fit in the fusable range [−d1 . . . d2]. Disparity can be adjusted by changing the convergence point, such as by shifting horizontally one of the images relative to the other. 
     The convergence point can be controlled by shifting horizontally the crop windows in the left and right images. The convergence point may be scene dependent in order to fully utilize the available z-range on the screen (i.e. depth range available at the display surface of the 3D display device  130  of  FIG. 1 ). 
     A design for adaptive convergence control can include scene range estimation and convergence control. Scene range estimation can be used for the convergence point control and possibly invasive disparity control. Convergence control may contain the logic that controls all the disparity in the image. 
       FIG. 6  is a diagram of a particular embodiment of a 3D image processing system that can be included in any of the systems of  FIGS. 1-3 . A scene range estimation module  602  provides scene range data  620  to an adaptive convergence point control module  604 . After adaptive convergence point control processing, image data may be processed by a horizontal shift crop module  606 , a switch to 2D module  608 , a 2D to 3D module  610 , or an invasive disparity control module  612 . The processed image data may be included into a 3D fusable stream of image data  614 . 
     Scene range estimation performed by the scene range estimation module  602  may be generalized as sparse motion vectors estimation between the left and right images. The scene range estimation process can include key (distinctive) point identification. Vertical variations are not needed since only horizontal shift is present (and will be measured). Horizontal variations (edges with some vertical component) are used. In some embodiments key points may be detected at different resolutions. The scene range estimation process can also include key point matching. Key point matching may be performed using normalized cross-covariance in order to be light-level independent and to produce a robust disparity reliability metric. As a result, matching key points with different resolutions may be unnecessary. 
       FIG. 7  is a diagram illustrating a particular embodiment of a subsampled luma component of a scene  702  and a result  706  of a horizontal variation detection filtering  704  applied to the subsampled luma component. The luma component of the scene may be subsampled by four, as an illustrative example. The horizontal edge detection filtering  706  may pass through a filter with a response h given by: 
     
       
         
           
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     An absolute value of a result may be taken to generate the result  706 . 
       FIGS. 8A and 8B  are diagrams of illustrative embodiments of local horizontal spatial maxima in the filtered result  706  of  FIG. 7  and neighborhoods of each local horizontal spatial maximum in the subsampled luma component of the scene of  FIG. 7 . Using the filtered result  706  of  FIG. 7 , a highest value in each horizontal neighborhood of potential key points in the filtered result  706  (local horizontal spatial maximum) may be selected to generate selected key points illustrated in  FIG. 8A . 
     For every selected key point, a neighborhood may be selected around the key point from the left luma image (shown in  FIG. 8B ), a correlation coefficient with the right image neighborhood may be computed (with a search range), and if correlation confident above a certain threshold, keep the motion vector (i.e. treat the key point as having a reliable disparity). 
     Convergence control (e.g. by the convergence control point module  604  of  FIG. 6 ) may be performed by building a histogram of motion vectors.  FIG. 9  depicts a histogram of a particular illustrative embodiment of disparity of key points in a scene. Disparity values (in bins of one or more disparity values) are depicted along a horizontal axis and a number of the selected key points having a disparity value corresponding to each bin is illustrated along the vertical axis. A maximum disparity range and desired convergence point are also illustrated in the histogram. Although  FIG. 9  depicts a histogram in a graphical form for clarity of explanation, implementations of the process may not include generating a graphical form of a disparity histogram and instead a data structure such as a table, list, array or other structure may be used to relate disparity values with a count of key points corresponding to the disparity values. 
     After building the histogram, the tails of the histogram may be trimmed. Scene range may be estimated as a difference between minimum and maximum values in the histogram (e.g. a difference between a largest disparity bin in the histogram that has a non-zero size and a smallest disparity bin in the histogram that has a non-zero size). An average between the minimum value and the maximum value, or some other linear combination, will be the desired zero disparity point (convergence point). The maximum remaining disparity determines if the scene is fusable or not. If the scene is not fusable, options include switching to a 2D stream (make left and right frames the same), performing a 2D to 3D conversion, or invasive disparity control processing, as illustrative examples. 
     Disparity (left and right) image shift plays a role in 3D effects and can produce discomfort/headache if not handled correctly. Display dimension and viewing distance affect disparity and can produce undesired effects if not compensated for. Zoom functionality can be an attribute to a 3D suite and also affects the disparity (in real time). 
     Disparity constraints may be configured based on user preferences. Scene-dependent disparity constraints can be derived in real time based on display size and distance and zoom factor. Scene disparity can be measured and if the scene disparity is within the constraints, a convergence point of the scene may be adjusted. If the scene disparity is not within constrains, processing may switch to a special mode to bring disparity within the constraints. 
     Special processing (3D effect control) may be used depending on one or more of: display viewing angle, display size, distance between viewer and display, and zoom factor. Consideration may be made to fully use an available range and to produce a 3D effect that is comfortable to the user. 
     Humans can only interpret (fuse) up to a certain angular disparity, after that ghosting appears (double images). An object&#39;s perceived depth may be solely dependent on the angular disparity. Angular disparity depends on a combination of screen size, screen resolution, and viewing distance. 
       FIG. 10  is a diagram of a particular embodiment of effects of viewing distance on 3D perception of a scene. A first configuration  1000  has a viewer  1002  a first distance  1018  from a screen  1008 . A first object  1004  is perceived in front of the scene (i.e. has negative disparity), a second object  1006  is perceived at the screen  1008  (i.e. has zero disparity), a third object  1010  is perceived beyond the screen  1008  (i.e. has positive disparity) and a fourth object  1012  is not fusable due to too large of a disparity. 
     In a second configuration  1020  a viewing distance  1038  is increased and all objects  1004 ,  1006 ,  1010 , and  1012  are fused. However, a depth contrast is reduced. In a third configuration  1040  the viewing distance  1018  matches the first configuration  1000  and a size of the viewing screen  1008  has decreased. All objects  1004 ,  1006 ,  1010 , and  1012  are fused but a depth contrast is reduced. 
       FIG. 11  is a diagram of illustrative embodiments of disparity dependency on display viewing distance. A first configuration  1102  illustrates an allowed scene depth range  1110  extending from in front of a screen  1108  to behind the screen  1108 . A first position  1116  indicates a largest allowed cross-before-screen disparity and a second position  1114  indicates a largest cross-after-screen disparity. A second configuration  1104  illustrates a smaller viewing distance between the screen  1108  and an observer than the first configuration  1102 . The range  1110  of allowed scene depth does not extend as far in front of the screen  1108  as in the first configuration  1102 . 
     Cross-after-screen disparity on the screen  1108  may not be greater than the eye distance (i.e. the distance between the observer&#39;s eyes). This consideration leaves less angular shift in that direction for screens on greater distance. As illustrated in the second configuration  1104 , closer screens do not allow large cross-before-screen disparity due to eye strain. A particular solution based on such considerations includes allowing more 3D depth effect for closer screens and more 3D pop out effect for farther screens. 
       FIG. 12  is a diagram of a particular embodiment of effects of a zoom operation on a 3D perception of a scene. A first configuration  1200  includes a viewer  1202  perceiving a scene that includes a set of objects, including a first object  1214  and a second object  1204  in front of a screen  1208 , a third object  1206  at the screen  1208 , and a fourth object  1210  and a fifth object  1212  past the screen  1208 . 
     A second configuration  1220  includes a zoomed image of the same scene as the first configuration  1200 . Only the third object  1206  and the fourth object  1210  appear in the scene due to the zoom. The fourth object  1210  is not fusable. 
     A third configuration  1240  includes a zoomed image of the same scene as the first configuration  1200  with appropriate zoom control. In contrast to the second configuration  1220 , both the third object  1206  and the fourth object  1210  are fusable. The disparity of the fourth object  1210  is reduced to within a fusable range by shifting the convergence point of the scene between the objects  1206  and  1210  so that the third object  1206  appears before the screen  1208  and the fourth object  1210  appears past the screen  1208 . 
     For display and zoom dependent 3D effect control, an allowable disparity may be set at a value α=0.5°−2° (e.g. specified during configuration time). θ display  is a viewing angle, D is a scene depth, i.e., the difference between maximum (D2) and minimum (D1) disparity vectors, expressed in numbers of pixels. W is an image width in pixels, x is a distance from the screen. Two questions that may be addressed are whether the scene depth range is fusable and where the convergence point should be placed. 
     Angular disparity (°) that will be seen on screen is given by (D/W)*θ display . Disparity may be tested and determined to be fusable when α&gt;(D/W)*θ display . If the disparity is fusable, the convergence point may be adjusted. Using the notation D1 for cross-before-screen disparity and D2 for cross-after-screen disparity, the convergence point may be selected as C=D1+D*M(X), where X is distance from screen and M( ) is a monotonically increasing function between 0 and 1. 
       FIG. 13  is a histogram of a particular illustrative embodiment of scene disparity and convergence point selection. When a distance (X) between a viewer and the display is small, the convergence point is set so that few objects appear in front of the screen. In contrast, when the distance (X) is larger, the convergence point is set so that more of the objects appear in front of the screen. 
     In conjunction with the systems and processes illustrated in  FIGS. 1-13 , adaptive 3D convergence is enabled during video capture, during playback, or both. For example, in a particular embodiment, a method  1400  illustrated in  FIG. 14  may be performed at a 3D image capture device such as the devices  102 ,  202 , and  302  of  FIGS. 1-3 , respectively. Alternatively or in addition, the method may be performed at a 3D playback device such as the device  120  of  FIG. 1  or at another device that includes one or more of the components  410 - 426  of  FIG. 4  or one or more of the components  602 - 614  of  FIG. 6 . 
     The method may be performed using a first image and a second image that correspond to a single scene. The first image may correspond to a first image capture of the scene by a first sensor and the second image may correspond to a second image capture of the scene by a second sensor, where the second image capture is substantially concurrent with the first image capture, such as by one of the sensor pairs depicted in  FIGS. 1-3 , at  1402 . 
     A first point may be selected within a first image based on a variation within image data corresponding to the first image, at  1404 . To illustrate, the first point may be a key point or object that is selected as described with respect to the key point detection module  412  of  FIG. 4 . A second point may be located within a second image, at  1406 . The second point may correspond to the first point. To illustrate, the second point may be located as described with respect to the key point matching module  414  of  FIG. 4 . 
     A determination may be made whether a distance between the first point and the second point exceeds a threshold distance to be perceptible as a single point of a three-dimensional (3D) rendering of the scene, at  1408 . For example, the distance between the first point and the second point can correspond to a key point or object disparity and may be unfusable when the disparity exceeds a threshold (e.g. is less than D1 or is greater than D2 of  FIG. 13 ) or is outside an allowable range (e.g. a range according to the range  1110  of  FIG. 11 ). To illustrate, the determination may be performed by the 3D convergence control module  416  of  FIG. 4 . In response to the distance between the first point and the second point exceeding the threshold distance, the second image may be replaced with a shifted version of the first image, at  1410 . Otherwise, when the distance between the first point and the second point is not greater than the threshold distance, the first point and the second point are fusable. 
     For example, the variation may be identified by applying an edge detection filter to image data corresponding to the first image and locating a largest absolute value that results from applying the edge detection filter within a horizontal region of the first image, where the first image corresponds to a left image and the second image corresponds to a right image of the 3D rendering. As an example, the filter can be the filter used for horizontal variation detection filtering  704  of  FIG. 7  to locate edges having at least some vertical component. The image data may include subsampled luma data corresponding to the first image, as described with respect to  FIG. 7 . A horizontal direction may correspond to a direction between a first image capture device corresponding to the first image and a second image capture device corresponding to the second image, as described with respect to  FIG. 3 . 
     One of skill in the art will understand that the method could be implemented by one or more field programmable gate array (FPGA) devices, one or more application specific integrated circuits (ASICs), one or more central processing units (CPUs), one or more digital signal processors (DSP), one or more graphics processing units (GPUs), one or more controllers, one or more other hardware devices, one or more firmware devices, or any combination thereof. In addition, a computer readable medium may stare program instructions that are readable by a computer or processing unit and executable to cause the computer or processing unit to perform at least a portion of the method. For example, a computer readable medium may include a flash memory, EEPROM, ROM, or other non-transient storage that includes code for selecting the first point within a first image based on a variation within image data corresponding to the first image, code for locating the second point within a second image, and code for determining whether a distance between the first point and the second point exceeds the threshold distance. 
     In another embodiment, a method  1500  depicted in  FIG. 15  may be performed at a 3D image capture device such as the devices  102 ,  202 , and  302  of  FIGS. 1-3 , respectively. Alternatively or in addition, the method may be performed at a 3D playback device such as the device  120  of  FIG. 1  or at another device that includes one or more of the components  410 - 426  of  FIG. 4  or one or more of the components  602 - 614  of  FIG. 6 . 
     The method may include selecting a first point within a first image, at  1502 . The method may also include locating a second point within a second image using a reliability measure, at  1504 . The second point may correspond to the first point and the first image and the second image may correspond to a single scene. For example, selecting the first point may be performed at the key point detection module  412  and locating the second point may be performed at the key point matching module  414 . The reliability measure may be determined based on a normalized cross-covariance between a first region of the first image that includes the first point and a second region of the second image that includes the second point. 
     For example, locating the second point may be performed by selecting a first region that includes the first point in the first image and selecting a second region of the second image having a highest determined correlation to the first region. The correlation determination may be performed using luma components of the first image and the second image. The correlation determination may be performed for multiple regions of the second image within a search range of the first point. The reliability measure may correspond to the second region and the second region may be identified as corresponding to the first region based on whether the reliability measure exceeds a reliability threshold. 
     The method may also include determining whether a distance between the first point and the second point exceeds a threshold distance to be perceptible as a single point of a three-dimensional (3D) rendering of the scene, at  1506 . For example, the determination may be performed by the 3D convergence control module  416  of  FIG. 4  and may correspond to whether any key point disparity is unfusable during 3D rendering. 
     One of skill in the art will understand that the method could be implemented by one or more field programmable gate array (FPGA) devices, one or more application specific integrated circuits (ASICs), one or more central processing units (CPUs), one or more digital signal processors (DSP), one or more graphics processing units (GPUs), one or more controllers, one or more other hardware devices, one or more firmware devices, or any combination thereof. In addition, a computer readable medium may stare program instructions that are readable by a computer or processing unit and executable to cause the computer or processing unit to perform at least a portion of the method. For example, a computer readable medium may include a flash memory, EEPROM, ROM, or other non-transient storage that includes code for selecting a first point within a first image, code for locating a second point within a second image using a reliability measure, and code for determining whether a distance between the first point and the second point exceeds a threshold distance to be perceptible as a single point of a three-dimensional (3D) rendering of the scene. 
     In another embodiment, a method  1600  depicted in  FIG. 16  includes determining whether a three-dimensional (3D) scene including a set of objects (e.g. key points located within an image, such as by the key point detection module  412  of  FIG. 4 ) can be rendered using a first image of the scene and a second image of the scene with each object of the set of objects being perceptible as a single object in the 3D scene (e.g. determined to be fusable), at  1602 . The method may also include, in response to determining that the 3D scene can be rendered with each object of the set of objects being perceptible as the single object, at  1604 , adjusting a convergence point of the scene to cause a disparity of each object of the set of objects to be within a predetermined range of disparity values, at  1606 . Adjusting the convergence point of the scene may include shifting at least one of the first image and the second image relative to the other of the first image and the second image to change the disparity of each object of the set of objects, such as by the 3D convergence adjustment module  420  of  FIG. 4 . 
     The method may also include, in response to determining that the 3D scene can not be rendered with each object of the set of objects being perceptible as the single object, replacing the second image with a shifted version of the first image, at  1608 , such as described with respect to the smart 2D module  420  of  FIG. 4 . 
     Determining whether the 3D scene can be rendered with each object of the set of objects being perceptible as the single object may include generating, for each object of the set of objects, a corresponding disparity value indicating a distance between a first location of the object in the first image and a second location of the object in the second image. A range of the disparity values may be compared to a predetermined range of disparity values, such as the maximum disparity range illustrated in  FIG. 9 . The range of the disparity values excludes at least a largest of the disparity values or a smallest of the disparity values corresponding to the set of objects. For example, a histogram of disparity values may be generated and one or more largest and/or smallest values may be discarded. 
     One of skill in the art will understand that the method could be implemented by one or more field programmable gate array (FPGA) devices, one or more application specific integrated circuits (ASICs), one or more central processing units (CPUs), one or more digital signal processors (DSP), one or more graphics processing units (GPUs), one or more controllers, one or more other hardware devices, one or more firmware devices, or any combination thereof. In addition, a computer readable medium may stare program instructions that are readable by a computer or processing unit and executable to cause the computer or processing unit to perform at least a portion of the method. For example, a computer readable medium may include a flash memory, EEPROM, ROM, or other non-transient storage that includes code for determining whether a three-dimensional (3D) scene including a set of objects can be rendered using a first image of the scene and a second image of the scene with each object of the set of objects being perceptible as a single object in the 3D scene, and code for, in response to determining that the 3D scene can be rendered with each object of the set of objects being perceptible as the single object, adjusting a convergence point of the scene to cause a disparity of each object of the set of objects to be within a predetermined range of disparity values. 
     In another embodiment, a device includes a three-dimensional (3D) media player configured to receive input data including at least a first image corresponding to a scene and a second image corresponding to the scene and configured to provide output data to a 3D display device. The 3D media player may be responsive to user input including at least one of a zoom command and a pan command. The 3D media player includes a convergence control module configured to determine a convergence point of a 3D rendering of the scene responsive to the user input. For example, the device may include the 3D processing system  440  of  FIG. 4 . The device may be implemented as a single chip, as a chipset such as a chipset for a mobile device, or as a mobile device such as a smart phone, photography device, or other type of device. 
     The convergence control module may be configured to adjust the convergence point on a scene-by-scene basis during playback. The convergence control module may be configured to determine the convergence point based on disparity values of objects in the scene. The convergence point may be determined further based on at least one of a size and a resolution of the 3D display device, a viewing distance from the 3D display device, one or more other factors, or any combination thereof. The disparity values may be determined in pixel units of the 3D display device. 
     In another embodiment, a device includes an image processing module that is configured to receive image data including first image data corresponding to a first image sensor and second image data corresponding to a second image sensor. For example, the device may be the device  202  of  FIG. 2 , the device  302  of  FIG. 3 , or another device that may include components of the 3D processing system  404  of  FIG. 4 . The image processing module may include a convergence control module configured to determine a convergence point of a 3D rendering of the scene. The image processing module may also include a 3D encoding module configured to generate 3D output data based on an output of the convergence control module. 
     The convergence control module may be further configured to determine is configured to determine the convergence point based on disparity values of objects in the scene. The convergence control module may also be configured to determine whether the disparity values result in at least one of the objects not being fusable in the 3D rendering. For example, an alert can be generated to inform a user of the device that the scene is not fusable during 3D video capture of the scene, such as the alert  382  of  FIG. 3 . 
     The convergence control module can be configured to perform convergence-point processing on a scene-by-scene basis. The convergence-point processing may include adjusting a scene-based convergence point based on disparity values corresponding to objects in the scene and further based on a predetermined display geometry. Convergence-point processing on the scene-by-scene basis can enable dynamic adjustment of the convergence point during a zoom operation performed by the first image sensor and the second image sensors during 3D video capture. 
     The device may be implemented as a single chip, as a chipset such as a chipset for a mobile device, or as a mobile device such as a smart phone, photography device, or other type of device. For example, the device may also include a video preview display, such as the 3D preview display device  284  of  FIG. 2 . The predetermined display device geometry may correspond to the video preview display. Alternatively, or in addition, the device geometry may correspond to a display geometry that is selected according to a user input. 
     The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disk, a removable disk, a compact disc read-only memory (CD-ROM), or any other form of non-transient storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). The ASIC may reside in a computing device or a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a computing device or user terminal. 
     The previous description of the disclosed embodiments is provided to enable a person skilled in the art to make or use the disclosed embodiments. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope possible consistent with the principles and novel features as defined by the following claims.