Patent Publication Number: US-7911513-B2

Title: Simulating short depth of field to maximize privacy in videotelephony

Description:
BACKGROUND 
     Current videophones use cameras having a long depth of field which results in the subject matter in a scene captured by the camera from foreground to background being in focus. This compares to video images captured by cameras having a shorter depth of field where subject matter in the foreground appears in focus while subject matter in the background of the scene appears out of focus. 
     Long depth of field in videophones generally results from a small digital imaging sensor size relative to the lens aperture in combination with a fixed focal length and shutter speed. These particular design parameters are selected in order to provide good videophone image quality while maintaining low component costs which is important for videophones sold into the highly competitive consumer electronics market. 
     Consumer-market videophones provide excellent performance overall, and the long depth of field provided is normally acceptable in many settings. Not only does it provide a perception that the videophone image is sharp and clear overall, but a videophone can be used in a variety of settings without the user worrying that some portions of a captured scene be out of focus. For example, a group of people on one end of a videophone call can have some participants positioned close to the camera while others are farther away. Another user may wish to use the videophone to show something that needs to be kept at some distance from the camera. 
     However, the videophone&#39;s long depth of field can present issues in some situations. Some users may find the details in the background of the received video image to be distracting. Others might be uncomfortable that their videophone captures too a clear view of themselves, their home, or surroundings and represents some degree of intrusion on their privacy. And even for those users who fully embrace the videophone&#39;s capabilities, it is possible that details of a user&#39;s life may be unintendedly revealed during a videophone call. For example, a person might not realize that a videophone call is taking place and walk through the background in a state of attire that is inappropriate for viewing by people outside the home. 
     One current solution to address privacy concerns includes placing controls on the videophone that let a user turn the videophone camera off while keeping the audio portion of the call intact. While effective in many situations, it represents an all or none solution that not all users accept since the loss of the video function removes a primary feature provided by the videophone. In addition, such user controls do not prevent the accidental capture of undesirable or inappropriate content by the videophone. 
     SUMMARY 
     An arrangement for simulating a short depth of field in a captured videophone image is provided in which the background portion of the image is digitally segregated and blurred to render it indistinct. As a result, the displayed video image of a videophone user in the foreground is kept in focus while the background appears to be out of focus. 
     In various illustrative examples, image detection and tracking techniques are used to dynamically segregate a portion of interest—such as a person&#39;s face, or face and shoulder area that is kept in focus—from the remaining video image. Image processing techniques are applied to groups of pixels in the remaining portion to blur that portion and render it indistinct. Such techniques include the application of one or more filters selected from convolution filters in the spatial domain (e.g., mean, median, or Gaussian filters), or frequency filters in the frequency domain (e.g., low-pass or Gaussian filters). Fixed templates are also alternatively utilizable to segregate the portions of the captured video which are respectively focused and blurred. The templates have various shapes including those that are substantially rectangular, oval, or arch-shaped. For example, application of the oval-shaped template keeps the portion of the captured video image falling inside a fixed oval in focus and the remaining portion of the image falling outside the oval is then digitally blurred. 
     User-selectable control is optionally provided to enable control of the type of foreground/background segregation technique utilized (i.e., dynamic object detection/tracking or fixed template shape), degree of blurring applied to the background, and on/off control of the background blurring. 
     The simulated short depth of field provided by present arrangement advantageously enables a high degree of privacy to be implemented while preserving the intrinsic value of videophone telephony by keeping the video component of the videophone call intact. The privacy feature is provided using economically-implemented digital image processing techniques that do not require modifications or additions to the camera hardware which would add undesirable costs. In addition, the blurred background portion of the video image appears natural to the viewer because short depth of field images are in common use in television, movies, and other media presentations. Thus, privacy is enabled in a non-intrusive manner that does not interfere with the videophone call or bring attention to the fact that privacy is being utilized. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a camera and two black and white patterned targets located in the camera&#39;s field of view; 
         FIGS. 2 and 3  show images captured by the camera to illustrative depth of view; 
         FIG. 4  is a pictorial view of an illustrative arrangement showing two videophone users; 
         FIG. 5  is a pictorial view of one of the videophones shown in  FIG. 4 ; 
         FIG. 6  shows an illustrative screen shot of a video image having a long depth of field that is rendered by a videophone; 
         FIG. 7  shows an illustrative screen shot of a video image with a simulated short depth of field that is rendered by a videophone in accordance with the principles of the present arrangement; 
         FIG. 8  is an illustration showing an illustrative segregation of a captured video image into a portion of interest that is kept in focus and a remaining portion that is blurred using a variety of alternative image processing techniques; 
         FIGS. 9-11  show various illustrative fixed templates, each of which segregate a portion of interest in a video image that is kept in focus while the remaining portions are blurred; 
         FIG. 12  is a diagram of an illustrative template having a transition area between the portion of interest that is kept in focus and the blurred portion; 
         FIG. 13  shows an illustrative image and kernel arrays used to perform convolution attendant to application of digital filtering; 
         FIG. 14  is an illustrative kernel used with a mean (i.e., averaging) digital filter; 
         FIG. 15  is simplified diagram of an illustrative videophone architecture; 
         FIG. 16  is a flowchart of an illustrative method simulating depth of field effects in a video image; and 
         FIG. 17  shows an illustrative screen shot of a video image with a simulated short depth of field that is rendered by a videophone to provide positive feedback to a user that privacy is enabled in accordance with the principles of the present arrangement. 
     
    
    
     Like reference numerals indicate like elements throughout the drawings. 
     DETAILED DESCRIPTION 
     Various compositional techniques are employed in traditional photography to emphasize the primary subject matter in a scene. One such technique is known as “Bokeh” which is Japanese term that translates into “fuzzy” or “dizziness.” Bokeh refers to the use of out-of-focus highlights or areas in a rendered image. Bokeh techniques may be used for a variety of functional, artistic, or aesthetic reasons in which an attribute known as “depth of field” is manipulated to provide the desire effect where the primary subject is kept in focus while the remaining portion of the rendered image is out of focus. 
     Depth of field in both still and video photography is determined by lens aperture, film negative/image sensor size (in traditional/digital imaging, respectively), and focal length. Traditional 35 mm film has a short depth of field because the negative size is large compared with the lens aperture. By comparison, to minimize costs, most videophones targeted at the consumer market use a very small digital image sensor along with an optics package that includes a fixed focal length and shutter speed. Thus, traditional techniques used to shorten depth of field by adjusting the aperture number (i.e., f/stop) down below the lens&#39;s maximum aperture and reducing shutter speed to compensate for exposure are not generally applicable to videophone cameras. 
     Depth of field is the range of distance around the focal plane which is acceptably sharp. The depth of field varies depending on camera type, aperture and focusing distance, although the rendered image size and viewing distance can influence the perception of it. The depth of field does not abruptly change from sharp to unsharp, but instead occurs as a gradual transition. In fact, everything immediately in front of or in back of the focusing distance begins to lose sharpness even if this is not perceived by the viewer or by the resolution of the camera. 
     Because there is no critical point of transition, a term called the “circle of confusion” is used to define how much a particular point needs to be blurred in order to be perceived as being unsharp. The circle of confusion is an optical spot caused by a cone of light from a lens not coming to a perfect focus when imaging a point source. Objects with a small “circle of confusion” show a clear and clean dot and are in focus. Objects with a large “circle of confusion” show a dot with blurry edges and are out of focus. 
     Accordingly, the present arrangement provides a person&#39;s face or other area of interest in the foreground of the rendered videophone image with a small circle of confusion. The remaining portion of the image is rendered with a large circle of confusion. Further discussion of Bokeh techniques, circle of confusion and sample images are available in H. Merklinger, A Technical View of Bokeh,  Photo Techniques , May/June (1997). 
       FIGS. 1-3  are provided to illustrate the application of the principles of depth of field to the present arrangement.  FIG. 1  is a pictorial illustration showing a camera  105  having two black and white patterned targets  112  and  115  within its field of view. As shown, target  112  is in the foreground of the camera&#39;s field of view and target  115  is in the background.  FIG. 2  shows an example of the appearance of an image with a long depth of focus taken by camera  105 . As shown, targets  112  and  115  are both in focus. By comparison,  FIG. 3  shows an example of an image having a shorter depth of focus. Here, the target  112  in the foreground is in focus, but target  115  in the background is no longer in focus and appears blurry. 
     Turning to  FIG. 4 , there is shown an illustrative arrangement  400  in which two videophone users are engaged in a video telephony session. User  405  is using videophone  408  in home  413 . Videophone  408  is coupled over a network  418  to videophone  426  used by user  430  in home  435 . Videophones generally provide better image quality with both higher frame rates and resolution when calls are carried over broadband networks, although some videophones are configured to work over regular public switched telephone networks (“PSTNs”). Broadband networks services are commonly provided from cable, DSL (Digital Subscriber Line) and satellite service providers. Videophones are normally used in pairs where each party on the call uses a videophone. 
       FIG. 5  is a pictorial view of the videophone  408  shown in  FIG. 4 . Videophone  408  is representative of videophones that are available to the consumer market. Videophone  408  includes a display component  502  that is attached to a base  505  with a mounting arm  512 . Base  505  is configured to allow videophone  408  to be positioned on desk or table, for example. A camera  514  is disposed in the display component having a lens that is oriented towards the videophone user, as shown. A microphone (not shown) is also positioned near camera  514  to capture voices and other sounds associated with a videophone call. 
     Camera  514  is commonly implemented using a CCD (charge coupled device) image sensor that captures images formed, from a multiplicity of pixels (i.e., discrete picture elements), of the videophone user and surrounding area. The images from camera  514  are subjected to digital signal processing in videophone  408  to generate a digital video image output stream that is transmitted to the videophone  426  on the other end of the videophone call. In this illustrative example, the digital video image output stream is a compressed video stream compliant with MPEG-4 video standard defined by the Moving Picture Experts Group with the International Organization for Standardization (“ISO”). In alternative embodiments, other formats and/or video compression schemes are usable including one selected from MPEG-1, MPEG-2, MPEG-7, MPEG-21, VC-1 (also known as Society of Motion Picture and Television Engineers SMPTE 421M), DV (Digital Video), DivX created by DivX, Inc. (formerly known as DivXNetworks Inc.), International Telecommunications Union ITU H.261, ITU H.263, ITU H.264, WMV (Windows Media Video), RealMedia, RealVideo, Apple QuickTime, ASF (Advanced Streaming Format, also known as Advanced System Format), AVI (Audio Video Interface), 3GPP (3 rd  Generation Partnership Project), 3GPP2 (3 rd  Generation Partnership Project 2), JPEG (Joint Photographic Experts Group), or Motion-JPEG. 
     Display component  502  includes a screen  516  that comprises a receiving picture area  520  and a sending picture area  525 . The receiving picture area  520  of screen  516  is arranged to display the video image of the user  430  captured by a camera in videophone  426  shown in  FIG. 4 . The sending picture area  525  displays a relatively smaller image of the user  405  captured by the camera  514 . Sending picture area  525  thus enables user  405  to see the picture of himself that is being sent and seen by the other user  430 . Such feedback is important to enable user  405  to place himself in field of view of camera  514  with the desired positioning and framing within the captured video image. 
     Mounting arm  512  is arranged to position the display component  502  and camera  514  at a distance above the base  505  to provide comfortable viewing of the displayed video image and position the camera  514  with a good field of view of the videophone user. Disposed in mounting arm  512  are videophone operating controls  532  which are provided for the user to place videophone calls, set user-preferences, adjust videophone settings, and the like. 
     Referring again to  FIG. 4 , videophone user  430  is positioned in the foreground of a scene  440  captured by the camera disposed in videophone  426 . The foreground is indicated by reference numeral  442 . Similarly, as shown, a houseplant  450  is in the middle ground  452  of the scene, and a family member  460  is in the background  462 . 
       FIG. 6  shows an illustrative screen shot  600  of a video image of the captured scene  440  in  FIG. 4  as rendered onto screen  516  by the videophone  408 . As shown, the rendered image appears with a long depth of field as user  430 , houseplant  450 , and family member  460  are all in focus. As noted above, such long depth of field is normally provided for video images rendered by conventional videophones. And, such clear imaging of all the subject matter in the capture scene may present privacy concerns. 
     In comparison to the conventional long depth of field video image shown in  FIG. 6 ,  FIG. 7  shows an illustrative screen shot  700  of a video image of having a simulated short depth of field as provided by the present arrangement. The video image shown in screen shot  700  is of the same captured scene  440  as rendered onto screen  516  by the videophone  408 . Here, only the image of the user  430  in the foreground  442  is kept in focus while the houseplant  450  and family member  460  are blurred and rendered indistinct as indicated by the dot patterns in  FIG. 7 . 
       FIG. 8  is an illustration showing an illustrative segregation of a captured video image into a region of interest  805  that is kept in focus and a remaining portion  810  that is blurred using a one of several alternative image processing techniques (as described below in the text accompanying  FIGS. 13 and 14 ). In this illustrative example, object detection techniques are utilized in which a specific feature, in this case the user&#39;s face, head, and shoulders are dynamically detected in the captured video image and tracked as the user moves and/or changes position during the course of the videophone call. While  FIG. 8  shows the area of interest comprises the user&#39;s face, head, and shoulder region, other areas of interest may also be defined for detection and tracking. For example, the area of the image kept in focus using a dynamic detection and tracking technique may be limited to just the user&#39;s face area. 
     Object detection, and in particular, face detection is an important element of various computer vision areas, such as image retrieval, shot detection, video surveillance, etc. The goal is to find an object of a pre-defined class in a video image. A variety of conventional object detection in video images techniques are usable depending on the requirements of a specific application. Such techniques include feature-based approaches which locate face geometry features by extracting, for example certain image features, such as edges, color regions, textures, contours, video motion cues etc., and then using some heuristics to find configurations and/or combinations of those features specific to the object of interest. 
     Other object detection techniques use image-based approaches in which the location of objects such as faces is essentially treated as a pattern recognition problem. The basic approach in recognizing face patterns is via a training procedure which classifies examples into face and non-face prototype classes. Comparison between these classes and a 2D intensity array (hence the name image-based) extracted from an input image allows the decision of face existence to be made. Image-based approaches include linear subspace methods, neural networks, and statistical approaches. 
     An overview of these techniques and a discussion of others may be found in E. Hjelmas and B. K. Low, Face Detection: A Survey,  Computer Vision and Image Understanding  83, 236-274 (2001). In addition, a variety of open source code sources are available to implement appropriate face-detection algorithms including the OpenCV computer vision facility from Intel Corporation provides both low-level and high-level APIs (application programming interfaces) for face detection using a statistical model. This statistical model, or classifier, takes multiple instances of the object class of interest, or “positive” samples, and multiple “negative” samples, i.e., images that do not contain objects of interest. Positive and negative samples together make a training set. During training, different features are extracted from the training samples and distinctive features that can be used to classify the object are selected. This information is “compressed” into the statistical model parameters. If the trained classifier does not detect an object (misses the object) or mistakenly detects the absent object (i.e., gives a false alarm), it is easy to make an adjustment by adding the corresponding positive or negative samples to the training set. More information on Intel OpenCV face detection may be found in G. Bradski, A. Kaehler, and V. Pisarevsky, Learning-Based Computer Vision with Intel&#39;s Open Source Computer Vision Library,  Intel Technical Journal , Vol. 9, Issue 2, (2005). 
       FIGS. 9-11  show illustrative examples of fixed templates that are applied to a captured video image to segregate the portion of interest from the remaining portion. By comparison to the object detection technique where the shape of the target portion dynamically varies as the subject moves, the templates in  FIGS. 9-11  use a fixed border between the target and remaining portions. Use of fixed templates may provide a less complex implementation of the segregation aspect of the present arrangement for implementing privacy while maintaining the majority of its functionality which may be beneficial in some scenarios. In an optional arrangement, control is provided to the videophone user to select from various templates to find a template that best matches the particular use and circumstances. In other arrangements, the relative sizes of the target and remaining portions may be adjusted, either in fixed increments or infinitely in a fixed range. 
     As shown, template  900  in  FIG. 9  has a substantially rectangular target portion  905  that is disposed in an area that fills approximately the central two-thirds of the screen. Target portion  905  is positioned to allow the remaining portion  910  to fill the top and sides of the screen. This template makes use of the observation that most videophone users position themselves to fill the central portion of the videophone camera&#39;s field of view. Accordingly, the areas of potential privacy concern will tend to be at the tops and sides of the captured image. As noted above, in optional arrangements the relative size between the target portion  905  and remaining portion  910  may be configured to be user adjustable as indicated by the dashed rectangle  925  in  FIG. 9 . 
       FIG. 10  shows a template  1000  that is similar to that shown in  FIG. 9  (by occupying approximately the central two-thirds of the screen) except the top portion of the target portion  1005  is curved. Thus, the target portion  1005  is substantially arched shaped. Use of this shape increases the area of the remaining portion  1010  and may provide a better fit between in-focus and blurred portions for a particular user&#39;s application. 
       FIG. 11  shows a template  1100  in which the target portion is substantially oval shaped. In this case, the remaining portion  1110  surrounds the target portion  1105  so that privacy blurring will be performed at the bottom center of the rendered image (unlike templates  900  and  1000 ) along with the top and side areas of the screen. 
       FIG. 12  shows an illustrative template  1200  having a transition area  1202  between the target portion  1205  in which focus is kept intact and remaining portion  1210  that is blurred using the present techniques described herein. The transition area  1202  is configured with an intermediate degree of circle of confusion between the target portion  1205  and remaining portion  1210 . This enables a softer transition between focus and blurred areas to be achieved which may help to make the rendered image appear more natural in some situations. The size of the transition area  1202  is a design choice that will normally be selected according to the requirements of a particular application. Although the transition area is shown being used with a template having an oval target portion, it is emphasized that such transition area may be used with any target portion shape in both fixed templates and dynamic object detection embodiments. 
     Once a captured video image is segregated into a portion of interest and a remaining portion, digital image processing is performed to increase the circle of confusion for groups of pixels in the remaining portion to thereby blur it and render it indistinct. In this illustrative example, the digital image processing comprises filtering in either the spatial domain or frequency domain. 
     The spatial domain is normal image space in which an image is represented by intensities at given points in space. The spatial domain is a common representation for image data. A convolution operator is applied to blur the pixels in the remaining portion. Convolution is a simple mathematical operation which is fundamental to many common image processing operations. Convolution provides a way of multiplying together two arrays of numbers, generally of different sizes, but of the same dimensionality, to produce a third array of numbers of the same dimensionality. This can be used in image processing to implement operators whose output pixel values are simple linear combinations of certain input pixel values. 
     In an image processing context, one of the input arrays is typically a set of intensity values (i.e., gray level) for one of the color components in the video image, for example using the RGB (red green blue) color model. The second array is usually much smaller, and is also two-dimensional (although it may be just a single pixel thick), and is known as the kernel.  FIG. 13  shows an example image  1305  and kernel  1310  used to illustrate convolution. 
     The convolution is performed by sliding the kernel over the image, generally starting at the top left corner, so as to move the kernel through all the positions where the kernel fits entirely within the boundaries of the image. (Note that implementations differ in what they do at the edges of images, as explained below.) Each kernel position corresponds to a single output pixel, the value of which is calculated by multiplying together the kernel value and the underlying image pixel value for each of the cells in the kernel, and then adding all these numbers together to produce the output, O. Thus, in the example shown in  FIG. 13 , the value of the bottom right pixel in the output image will be given by:
 
 O   57   =I   57   K   11   +I   58   K   12   +I   59   K   13   +I   67   K   21   +I   68   K   22   +I   69   K   23  
 
     If the image I has M rows and N columns, and the kernel has m rows and n columns, then the size of the output image will have M−m+1 rows, and N−n+1 columns. Mathematically, the convolution is written as: 
               O   ⁡     (     i   ,   j     )       =       ∑     k   =   1     m     ⁢       ∑     l   =   1     n     ⁢       I   ⁡     (       i   +   k   -   1     ,     j   +   l   -   1       )       ⁢     K   ⁡     (     k   ,   l     )                   
where i runs from 1 to M−m+1 and j runs from 1 to N−n+1.
 
     In one illustrative example, the convolution filter applied is called a mean filter where each pixel in the image is replaced by an average value of its neighbors, including itself. Mean filters are also commonly referred to as “box,” “smoothing,” or “averaging” filters. The kernel used for the mean filter represents the size and shape of the neighborhood to be sampled when calculating the mean. Often, a 3×3 square kernel as indicated by reference numeral  1410  in  FIG. 14 , although larger 5×5, 7×7 etc., kernels may also be used to create more blurring. The kernel  1405  may also be applied more than once. 
     A median filter is alternatively utilized in which the average value used in the mean filter is replaced by the median value of neighboring pixels. 
     In another illustrative example, a Gaussian filter is applied to blur the remaining portions other than the portion of interest in the image to be rendered in focus. This filter uses a kernel having a shape that represents a Gaussian (i.e., bell-shaped curve) as represented by: 
               G   ⁡     (   x   )       =       1       2   ⁢   πσ         ⁢     ⅇ     -       x   2       2   ⁢     σ   2                     
where σ is the standard deviation of the distribution (which is assumed to have a mean of zero, i.e., centered on the line x=0).
 
     The effect of Gaussian smoothing is to blur an image, in a similar fashion to the mean filter described above. The degree of smoothing is determined by the standard deviation of the Gaussian. Larger standard deviation Gaussians require larger convolution kernels in order to be accurately represented. 
     The Gaussian outputs a “weighted average” of each pixel&#39;s neighborhood, with the average weighted more towards the value of the central pixels. This is in contrast to the mean filter&#39;s uniformly weighted average. Because of this, a Gaussian filter generally provides gentler smoothing and preserves edges better than a similarly sized mean filter. 
     The frequency domain is the domain in which a video image is represented by a sum of periodic signals with varying frequency. The applied filter function is shaped so as to attenuate some frequencies and enhance others. Generally, since the multiplication in the Fourier space is identical to convolution in the spatial domain, all frequency filters can also be implemented in the spatial domain. In addition, if there exists a simple kernel for the desired filter effect, it is computationally less expensive to perform the filtering in the spatial domain. Frequency filtering is more appropriate if no straightforward kernel can be found in the spatial domain, and may also be more efficient. 
     To apply a filter in the frequency domain, groups of pixels in the remaining portion other than the area of interest in the image are Fourier transformed from the spatial domain to the frequency domain:
 
 G ( k,l )= F ( k,l ) H ( k,l )
 
where F(k,l) is the input image in the Fourier domain, H(k,l) the filter function and G(k,l) is the filtered image. To obtain the resulting image in the spatial domain, G(k,l) has to be re-transformed using the inverse Fourier Transform.
 
     The form of the filter function determines the effects of the operator. In the present arrangement, a low-pass filter is used to attenuate high frequencies and retain low frequencies unchanged. The result in the spatial domain is equivalent to that obtained by application of the mean filter in the spatial domain: as the blocked high frequencies correspond to sharp intensity changes, i.e., to the fine-scale details in the spatial domain image. 
     The most simple lowpass filter is the ideal lowpass. It suppresses all frequencies higher than the cutoff frequency D 0  and leaves the smaller frequencies unchanged. 
               H   ⁡     (     k   ,   l     )       =     {             1   ⁢           ⁢   if   ⁢           ⁢         k   2     +     l   2           &lt;     D   0                   0   ⁢           ⁢   if   ⁢           ⁢         k   2     +     l   2           &gt;     D   0                     
In most applications, D 0  is given as a fraction of the highest frequency represented by the Fourier domain image.
 
     In applications of the present arrangement, the application of a Gaussian filter in the frequency domain will produce more desirable results than the ideal lowpass filter. A commonly used discrete approximation to the Gaussian is known as the Butterworth filter. Applying this filter in the frequency domain shows a similar result to the Gaussian smoothing in the spatial domain. 
       FIG. 15  is an illustrative architecture  1500  for the videophones  408  and/or  426  ( FIG. 4 ). The CCD imaging sensor forming camera  514 , which captures a video image having a long depth of field (i.e., one that is substantially infinite), is included in an abstracted hardware layer  1502  in architecture  1500 . Hardware layer  1502  also includes a frame buffer  1504  and output interface  1512 . The captured video image is processed by an image processing module  1516  having a number of components including image segregation logic  1520 , blurring logic  1526 , blending logic  1532 , and a user interface  1535 . Image processing module  1516  and the components therein may be discretely embodied in some applications, using for example hardware such as one or more application-specific integrated circuits and/or firmware. Alternatively, image processing module  1516  may be constructed as a logical device that is implemented using software resident on the videophone  408 , or a combination of hardware, firmware, or software. 
     Segregation logic  1520  is arranged to segregate the video image from the camera  514  into a target portion (for which focus is maintained) and remaining portion (for which blurring is imposed to thereby render that portion indistinct). The segregation is performed using object detection described in the text accompanying  FIG. 8 , or alternatively using one of the templates described in the text accompanying  FIGS. 9-12 . 
     Blurring logic  1526  is arranged to blur the remaining portion of the captured video image to increase the circle of confusion of pixels therein to thereby render the subject matter indistinct. Such blurring is accomplished using one or more of the digital filtering techniques described in the text accompanying  FIGS. 13 and 14 . Blurring logic is optionally configured to adjust the degree of blurring responsively to input from a user. Blurring logic  1526  is alternatively arranged to replace the remaining portion of the captured video image with a predefined image (e.g., wallpaper) as described above. 
     Blending logic  1532  is arranged to generate a composite video image in which the target portion that is unblurred is combined with the blurred remaining portion. In some applications, blending logic  1532  and blurring logic  1526  are configured to enable multiple blurring levels as shown in  FIG. 12  and described in the accompanying text. 
     The user interface  1535  is arranged to provide a user of the videophone  408  with user-selectable control over the present privacy features. For example, the user may selectively enable and/or disable the privacy feature so that the background portion of the captured image is rendered indistinct or kept in focus. The degree to which blurring is implemented, whether object detection/tracking or fixed templates are utilized, and template shape may also be user-selectable in some applications of the present arrangement. 
     The frame buffer  1504  is utilized in the videophone architecture  1500  to temporarily store video images during processing. The output interface  1512  reads video images from the frame buffer  1504  for real time transmission as a video output stream over the network  418  ( FIG. 4 ). In most applications, the output stream is formatted in accordance with MPEG-4. Alternatively, MPEG-2, 
       FIG. 16  is a flowchart of an illustrative method  1600  for simulating depth of field effects in a video image. Method  1600  may be utilized by either videophone  408  or  426  in  FIG. 4 . The method starts at block  1605 . At block  1611 , a camera (e.g., camera  514 ) captures a video image having long or substantially infinite depth of field that is typical for most videophones sold into the consumer market. 
     At block  1616 , the captured video image is spatially segregated into a target portion for which focus is maintained and a remaining portion for which blurring is applied using one of the techniques described in the text accompanying  FIG. 8 . Typically, the video image is buffered (e.g., in frame buffer  1504  in  FIG. 15 ) during the image processing steps of segregating, blurring, and compositing. 
     At block  1620 , the remaining portion of the captured video image is blurred to increase the circle of confusion of pixels therein to thereby render the subject matter indistinct using one or more of the digital filtering techniques described in the text accompanying  FIGS. 13 and 14 . The blurred remaining portion is combined with the unblurred target portion to create a composite video image as indicated in block  1622  which is then refreshed in the frame buffer at block  1625 . 
     Block  1631  shows an optional step (as indicated by the dashed rectangle in  FIG. 16 ) in which the composite video image is displayed locally on the videophone&#39;s display screen. This optional step is shown in the screen shot of  FIG. 17  and is performed to enable the videophone user (i.e., videophone user  430  using videophone  426  in  FIG. 4 ) to receive positive feedback that the privacy feature is enabled. That is, the user can see him or herself in the small image  1705  at the bottom of display screen  1710 . Image  1705  includes the rendered composite image. In applications where privacy is arranged to be user-selectively enabled and disabled, image  1705  would include a normal video image (i.e., not processed to implement privacy) that would include the typical long depth of field when the privacy feature is disabled. 
     Returning again to  FIG. 16 , block  1635  shows that the composite video image is transmitted to the videophone at the far end of the call. Typically, the composite video image is sent as part of a video stream that is compliant with MPEG-4. Illustrative method  1600  ends at block  1640 . 
     Although a discrete and dedicated videophone arrangement has been provided in the description above, it is noted that the feature and functions described are alternatively implementable using general purpose personal computers (“PCs”). In this scenario, webcam and microphone hardware are used to supplement the processing capabilities and network access provided by the PC. In addition, the videophone architecture  1500  shown in  FIG. 15  is typically implemented in software to provide a “soft” videophone that runs as an application on the PC. 
     In another alternative illustrative embodiment, an arbitrary or predetermined image, effect, or pattern may be used to replace the remaining portion of a captured video image (i.e., the portion of the captured video image other than the target portion that is kept in focus). Instead of blurring pixels in the remaining portion by increasing their circle of confusion to thereby render the remaining portion indistinct as described above, all or part of the remaining portion may be replaced, for example, with a featureless image. The featureless image may be arranged with an arbitrary or user selectable color, for example, that would make the videophone user appear to be sitting in front of a plain wall. In other examples, an arbitrary or user selectable image is selected such as a photograph or illustration. For example, a garden photograph may be selected to provide background scenery for the videophone user. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.