Patent Publication Number: US-7593040-B2

Title: Image anti-shake in digital cameras

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     The present application is related to U.S. Provisional Patent Application Ser. No. 60/763,516 filed on Jan. 30, 2006, priority to which is claimed. 
    
    
     BACKGROUND 
     Image shake is an issue that degrades performance in digital cameras. Image shake often results from movement by the user of the camera, or from vibrations transmitted through a mounting such as a tripod or bracket. Another source of image shake is from motion of the object to be imaged. As sensors become smaller, while even increasing the numbers of pixels in the image sensor, image shake becomes a larger issue. 
     One approach to avoid image shake is to build in a gyroscopic mount for a sensor array. Thus, the sensor array is kept still even when surrounding parts of a camera are in motion. However, this is relatively costly. Also, compensating for camera motion does not reduce adverse effects of movement of the object to be imaged. 
     While one may expect that a camera is always moving somewhat, the motion of a camera manifesting as image shake may vary during the process of taking a picture. Similarly, the motion of an object can vary, for example when a basketball player jumps or executes an abrupt transient motion. Thus, it may be useful to provide a method and system which takes advantage of times of low motion. Additionally, a low-cost solution for minimizing the effects of image shake can be useful. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is illustrated in an exemplary manner by the accompanying drawings. The drawings should be understood as exemplary rather than limiting, as the scope of the invention is defined by the claims. 
         FIG. 1  illustrates an embodiment of a system for reducing the effects of image motion in a digital camera. 
         FIG. 2  illustrates a subdivision of sequential image frames into sequential subframes of the image frames. 
         FIG. 3  illustrates a 5×5 matrix of high pass filtering coefficients. 
         FIG. 4A  is a flow diagram showing selected steps of an embodiment for improved image capture. 
         FIG. 4B  is a flow diagram showing some further details of selected steps of the embodiment in  FIG. 4A . 
         FIG. 5  illustrates the movement of pixel data in line buffers of an embodiment. 
         FIG. 6  is a flow diagram showing data flow of an embodiment for improved image capture. 
         FIG. 7  is a graph illustrating improved image capture of an embodiment. 
         FIG. 8  illustrates an embodiment of a digital camera. 
     
    
    
     DETAILED DESCRIPTION 
     A system, method and apparatus are provided for image anti-shake in digital still cameras. The specific embodiments described in this document represent exemplary instances of the present invention, and are illustrative in nature rather than restrictive. 
     In one embodiment, a method of capturing an image in a digital camera is presented. The method includes calculating a sharpness value based on an image input. In the embodiment, calculating the sharpness value comprises determining a high frequency value related to the image input. The method also includes predicting the quality of a next image based on the sharpness value. The method further includes deciding whether to capture a next image input data responsive to the prediction. 
     In another embodiment, a digital camera is presented. The camera includes a processor. The camera also includes media for image storage coupled to the processor. The camera further includes an image sensor coupled to the processor. Also, the camera includes an image quality detector for detecting image motion. The image quality detector includes a sharpness detector based on high pass filtering of image data from the digital image sensor. Moreover, the camera includes a predictor of next image motion coupled to the quality detector. Furthermore, the camera includes a decision maker coupled to the predictor. The predictor and decision maker are to evaluate output of the quality detector and capture an image from the digital image sensor in the media, responsive to the output of the quality detector. The quality detector, the predictor and the decision maker can be implemented by the processor in some embodiments. 
     In another embodiment, an apparatus is presented. The apparatus includes means for calculating a sharpness value related to a current image input. The apparatus also includes means for estimating next image quality depending on the sharpness value. The apparatus further includes means for capturing a next image input data frame from the image input. The means for capturing operates responsive to the means for estimating next image quality. 
     A method and apparatus as described and illustrated can improve image quality in digital still cameras. The method and apparatus depend on evaluating motion characteristics of image input data, and then capturing the next image sensed. Thus, when a process determines that criteria for relatively stable pictures are met, the process can then capture the next image with the expectation that the next image will be relatively stable. This allows for rapid evaluation, without the need to store multiple images. It also reduces cost associated with expensive components such a movable image sensors and lenses, for example. 
     In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art, that the invention can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the invention. 
     Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. 
     Various embodiments may be further understood from the Figures.  FIG. 1  illustrates an embodiment of a system for reducing the effects of image motion in a digital camera. System  100  includes optical components  105  that project an image onto an image sensor  110 . The image sensor may be a CCD, CMOS or similar image sensor matrix array. In many embodiments the array is a rectangular array of detectors arranged in rows and columns. Pixels of the image sensor  110  are exposed according to a rolling shutter timing mechanism which is well known in the art. In the illustrative embodiment, a first row of pixels is selected and the image data from individual pixels in the selected row are output sequentially from the image sensor into a line buffer  150 . Next, a following row is copied from the image output to the line buffer in the same manner. At the end of each complete scan of the entire image sensor array, a blanking period of predetermined time occurs before the raster scan is repeated. The set of image data of a complete scan from each and every pixel in the image sensor  110  is termed a frame. It is seen that the image sensor is operable to output image data responsive to the projected image. 
     It will be apparent to those skilled in the art that the referring to a line of sensors in the image sensor as a row or column is somewhat arbitrary. Although the term row often references a line of pixels parallel to the top and bottom of a digital camera, the invention does not depend on any specific designation and a columnwise raster scan can also be performed within the scope and spirit of the invention. 
     Also, various other methods of partitioning the image sensor and outputting data from the image sensor are useful in some embodiments. For example an artificial frame consisting of selected submatrices of image sensor pixel data can be repeatedly scanned in a selected order. Also, a sensor comprising a pixel addressable image buffer is operable to implement aspects of the invention. 
     It is known that the quality of digital images is adversely affected by image shake (also referred to herein as image motion) while the image is exposed. Unintentional camera motion causes image shake and results in decreased sharpness. However image motion can also arise from motion of the subject or object being imaged. Relative changes in the sharpness of successive image frames depends on motion characteristics. Generally, the sharpness of an image is greater when there is less motion. It has been discovered that image motion can be effectively predicted based on relative change in the sharpness of subframes. The invention includes an efficient camera anti-shake method that includes evaluating relative sharpness changes based on the subframes, and predicting the sharpness of a next image based on the relative changes. In another aspect of the invention, the camera anti-shake method is operable to reduce the adverse effects of subject motion on image quality. In still another aspect of the invention, an anti-shake digital camera using the anti-shake method is disclosed. The digital camera is easily implemented with modest hardware resources, resulting in relatively low cost. 
     An embodiment of a method and system for improving image quality in digital cameras according to the present invention is explained further with reference to  FIG. 1 . The system  100  includes an image sensor  110  that forms image data responsive to an image from the optical path  105 . Although the image is of visible light in this embodiment, other embodiments comprise imaging other forms of electromagnetic radiation such as infrared, x-rays, millimeter waves, and so forth. The system  100  further includes machine readable media operable to store instructions and data, and at least one processor operable to perform the instructions and operate on the data. The system also includes an image quality detector  120  that senses image motion based on image data input from the image sensor  110 . 
     In the embodiment, data input from the image sensor  110  is moved into a buffer  150  of machine readable media. The buffer is coupled to the image quality detector  120 . In many embodiments, the quality detector  120  comprises a sharpness detector. The system  100  also includes a motion predictor  130  and a decision maker  140 . Instructions and data in the computer readable media are operable by the processor to implement the sharpness detector  120 , the predictor  130 , and the decision maker  140 . However in various other embodiments, functionality of the detector, predictor or decision maker, in whole or in part, can be implemented with control circuits. In the embodiment, the media comprises line buffers operable for storing a number of lines of image data corresponding to rows of the pixel data input from the image sensor  110 . While lines of image data in the embodiment are rows of data, lines which are columns of pixel data can equivalently be used in various embodiments. Also, detectors comprising non-rectangular pixel configurations and/or other methods operable to receive an image input from an image sensor and store the data in the machine readable media are operable to practice the invention. 
     In an embodiment of the system shown in  FIG. 1 , the image sensor  110  is an XGA resolution sensor which has 768 rows comprising 1024 pixels in each row. Hence each full image frame of the sensor comprises 1024×768 pixels. However an image frame comprising a subset of pixel data that is from the sensor is often selected for processing. For example, a smaller array of pixels containing the subject of interest can be selected, thus avoiding the overhead associated with processing and storing unwanted background from the image input (in some embodiments this is termed “digital zoom”). 
     It has been found that subdividing the image frame into subframes often improves sensitivity for detecting motion. In an embodiment shown in  FIG. 2 , an image frame is divided into four row-wise subframes. Each of the subframes is W pixels wide and H pixels high. Where the image frame is an XGA array, the value of W, which is the number of horizontal pixels or columns in a row, is 1024. H is the number of pixel rows of the image frame divided by the number of subframes. Therefore the value of H in the embodiment is 256. Of course in other embodiments, for example, where the image frames are selected to have a subset of pixels available from the XGA sensor, the image frame will have other resolutions and can be subdivided into a different number of subframes, depending on the application. Accordingly, W and H depend on the selected resolution and the selected number of subframes. 
     In many embodiments, the frame image acquisition rate is 15 or 30 frames per second, responsive to a number of design constraints and industry standards. The corresponding image frame exposure times will be somewhat less than 1/15 s or 1/30 s since the frame rate usually includes a blanking period between exposures. 
     In many embodiments, the successively numbered lines of an image frame are exposed sequentially. As the frame rate decreases, an image frame must be divided into more subframes in order to effectively sample relative motion of the image. On the other hand, if the number of subframes is too high, the reduced height of each subframe can introduce an artificial sensitivity to motion in the vertical direction. Hence it is seen that there is a tradeoff between the image acquisition rate and the number of image subframes. It has been found that using at least four equal horizontal subframes provides effective motion sensing in embodiments having VGA (640×480) or XGA image frame resolution and standard 15 s −1  and 30 s −1  frame rates. 
     In one embodiment illustrated in  FIG. 1 , the sharpness detector  110  detects a sharpness based on the spatial high frequency content of image data. When images of objects have sharper edges (e.g. the objects are not blurred by camera shaking or transient subject movement), they tend to have more high frequency content. Images of the same object with motion tend to have less high frequency content (e.g. motion blur characteristically reduces edge definition). In general, increased spatial high frequency content in an image correlates with sharpness. Therefore a high frequency extracting filter is useful for detecting the sharpness of an image. However other methods of detecting sharpness, or conversely measures of the absence of sharpness, are also useful to sense image motion, depending on the application. 
     In the embodiment, a sharpness detector comprises high pass filtering. A suitable high pass filter comprises convolving the example 5×5 high pass filter matrix in  FIG. 3  with image data f(x,y) to extract high frequency values g(x,y) of the image data. This filter is applied by evaluating the convolution sum: 
                 g   ⁡     (     x   ,   y     )       =       ∑     m   =     -   2       2     ⁢       ∑     n   =     -   2       2     ⁢       w   ⁡     (     m   ,   n     )       ⁢     f   ⁡     (       x   +   m     ,     y   +   n       )               ,         
where w(m,n) is an element of the high pass filter matrix shown in  FIG. 3 . The center of the matrix (m=0,n=0) is positioned is at (x,y). Although the high pass filter matrix comprises 25 values, it can be efficiently stored in only 6 storage cells owing to the 4-fold symmetry: w(m,n)=w(−m,n)=w(m,−n)=w(−m,−n). The high pass filter matrix shown in  FIG. 3  is merely one example of a useful filter for extracting a high frequency portion of the image data. Matrices having a symmetry are particularly effective. Other high frequency extracting filters such as single bandpass filters or multiple bandpass filters in combination, and the like, are also useful in some embodiments, depending on the application.
 
     The sharpness detector embodiment forms a subframe sharpness value for the i th  subframe in the j th  frame comprised of the subframe average high frequency portion given by the following relationship: 
                 s   ij     =         ∑     x   =   1     W     ⁢       ∑     y   =   1     H     ⁢            g   ij     ⁡     (     x   ,   y     )                  W   ×   H         ,         
where W is the number of pixels along the width of the subframe (which is a row of the frame in this embodiment), and H is the number of pixels in a column of the subframe (which is a subframe portion of the full frame column height).
 
     The predictor  130  estimates the expected image motion of a next frame based on the current subframe sharpness value of the current frame and previous subframe sharpness values of previous image frames. An illustrative predictor method in an embodiment that divides an image frame into four subframes operates as follows. The difference between the i th  subframe sharpness value in the current j th  frame and the corresponding subframe sharpness value, determined for the previous (j−1) th  frame is computed according to:
 
 D   ij   =s   ij   −s   i,j−1 ,
 
where i is the current subframe number (i=1,2,3,4). Note that subframes of different frames are said to be corresponding subframes if and only if they have same subframe number. Also, D max , the maximum absolute value of the previous consecutive subframe sharpness value differences D ij  following the capture command, is found according to:
 
 D   max =max(| D   ij |),
 
where the subscript i ranges over subframe numbers (i=1,2,3,4) and the subscript j ranges over image frame numbers from the first image frame following the capture command to the current image frame.
 
     The predictor  130  estimates an image motion of the next image frame based on evaluating the two propositions:
 
D 4j &gt;0 and D 4j &lt;k 1 *D max  and D 4j &gt;D 3j   Proposition 1
 
D 4j &lt;0 and |D 4j |&lt;k 2 *D max  and D 4j &gt;D 3j   Proposition 2
 
where k 1  and k 2  are selected factors. If at least one of these two propositions is true, a prediction that next frame image motion will be less than the current image frame motion is output to the decision module  140 . Otherwise a prediction that the next frame image motion will be equal or greater than the current image frame motion is output to the decision maker. In practice, selecting a constant of ⅔ for k 1  and a constant of ½ for k 2  has been found to be quite effective. Other selections of k 1  and k 2  are operable, although it has been found that selecting values of k 1  and k 2  less than 1 is preferable.
 
     The most recent subframe image data of a frame are considered to provide more accurate estimates of future motion than older data. Therefore Proposition 1 and Proposition 2 in the embodiment are based on D 4j  since the data of subframe  4  are the last and most recent portion of image frame data input from the image sensor. However other methods of estimating motion based on the sharpness of the image frame data can be selected in various embodiments, and other predictors of future image motion can be used within the scope and spirit of the present invention. 
     In an embodiment of the method and apparatus illustrated in  FIG. 1 , the decision module  140  calculates a frame sharpness, S j , of current frame j. The frame sharpness S j  of the current frame (j) is evaluated using the relationship: 
               S   j     =       ∑     i   =   1     4     ⁢       s   ij     .             
However other metrics of the frame sharpness based on other relationships are also operable, depending on the embodiment.
 
     The decision maker  140  decides whether or not to capture a next frame from the image sensor  110  and save it in an image storage  170 . It will make a decision to capture when all of the following three propositions are true: 1) a capture command was received, 2) the predictor predicts that the next frame will have increased sharpness, and 3) S j &gt;S c  (e.g. the current frame is sharper than the first image frame received after the capture command). 
     In practice, image capture must be completed within a limited time after a capture command to be acceptable. It is possible that malfunction or unusual image conditions could lead to an unacceptable delay before the predictor predicts increased sharpness or S j &gt;S c . To prevent unacceptable delay, the decision maker  140  in  FIG. 1  includes a timeout override for capturing the next image frame from the data sensor  110  through the buffer  150  and into image storage  170 . Where a capture command was received and the next frame count is about to exceed a predetermined maximum number of image data frames (“Max” in decision step  470  of  FIG. 4A ), the decision maker  140  decides to timeout and instructs a processor to perform image processing  160  and capture the next image frame from the image sensor  110  into image storage  170 , corresponding to stepping through  460 ,  465 ,  470 , and  480  in  FIG. 4A . 
     Alternatively, where the decision maker  140  does not decide to capture a next image, another subframe of pixels is read from the image sensor  110  and processed by the quality detector  120  and the predictor  130 . Of course the scope and spirit of the present invention includes embodiments comprising other methods and/or algorithms for deciding whether to capture a next image based on an output of the predictor and the number of frames and/or time elapsed after a capture command. 
       FIGS. 4A and 4B  show further details of an embodiment for image evaluation and capturing. First, registers used to retain the quality detector and predictor parameters and variables (e.g. D max ,S c ,s ij ,D ij  etc.) and the frame number counter are initialized at box  410 . Next, a subframe number counter is initialized at box  420 . The number of frames that are received depends on the cumulative total number of subframes that are received. In some embodiments counting functions are combined. Also, the counting comprising incrementing the frame counter  461 , testing the frame counter  470 , incrementing the subframe number  441  and resetting the subframe number  447  may be implemented in other ways including ways having fewer steps. 
     As merely one example, another embodiment comprising the method of  FIGS. 4A and 4B , maintains a count of the cumulative total number of subframes. In the example above, the cumulative total number of subframes comprises the subframe number (the subframe number is equal to the cumulative total subframe number modulo 4), and the frame number (the frame number is integer part of the quotient of cumulative total subframe count divided by 4). 
     Next, a frame is acquired at box  430 , subframe by subframe  442  as sharpness values of the subframes, sharpness value differences, and D max  are evaluated and stored in registers  445 . In some embodiments, the sharpness values are evaluated according to the relationships set forth above. However, within the scope and spirit of the invention, various other methods and measures of sharpness can be used for evaluating the quality of an image, including other measures depending on filtering a high frequency portion of the image. 
     Each of the blocks  430  and  435  of  FIG. 4A  comprises the steps enumerated in blocks  441  through  447  of  FIG. 4B . First, the subframe number is incremented  441 , a portion of subframe data is read from the image sensor  442 , and terms depending on the subframe data are evaluated  442 . Additional portions of the subframe are read and evaluated  442 - 444  until the processing of subframe is complete  445 , and sharpness values characteristic of the subframe are evaluated and stored in processor readable media. These steps  441 ,  442 ,  444 ,  445  are repeated until the complete frame has been processed at block  446 . After the complete frame is processed, the subframe number is reset  447 . 
     After a frame is read and evaluated at  430 , another frame is acquired and evaluated until a capture command is received  450 . After the capture command is received, the predictor estimates the quality of the next frame  455  and evaluates capture criteria based on the estimated quality  460 . If the capture criteria based on the estimated quality are met, the decision maker commands the capture of a next frame into an image memory  480  and the process ends  490 . However, if the capture criteria based on the estimated quality are not met  460 , the frame counter is incremented  465  and the decision maker tests whether the frame count has reached the predetermined maximum number “Max”  470 . If the frame counter has reached Max, the decision maker commands the capture of a next frame into an image memory  480  and the process ends at block  490 . Otherwise at block  435  another frame is acquired and evaluated. 
     Another aspect of the invention is that the method and system are operable in a relatively small amount of memory. A buffer memory for storing pixel data to evaluate terms according to the methods of  FIGS. 1 and 4  is illustrated in  FIG. 5 . According to this embodiment, a relatively small line buffer  510  including memory for storing 4 lines of pixel memory and a small number of word registers are sufficient storage for implementing the sharpness detector  120 , predictor  130  and decision maker  140  illustrated in  FIG. 1 . 
     In an embodiment, the computational operations to evaluate the subframe sums s ij  include accumulating the convolution terms g(x,y) as taught above. In the embodiment the terms are evaluated using the convolution matrix coefficients (shown in  FIG. 3 ), five columns of four consecutive lines (rows) of the current image subframe data  540  ( FIG. 5 ), and a five column portion  530  ( FIG. 5 ) comprising the image data of the current line most recently received from the image sensor. 
       FIG. 5  also illustrates pixel data movement when evaluating the convolution. An incoming pixel value  575 , from a current line (the row being processed) of the current subframe, is received from the image sensor and stored in a rightmost cell  576  of a short logical five register buffer  530 . Four other incoming pixel data of that line are stored adjacently in the register buffer, in order of being read in (least recent to the left, most recent going right). Apart from row segment portion  520 , the consecutive lines (rows) of image data received before the incoming line of the subframe, corresponding to the rows designated by the indices y−2 through y+1 (e.g. in correspondence with n=−2 through n=1 of the convolution terms) have been retained in the four line buffer  510 . As will become apparent, row segment  520  comprises columns of the current line (row) of data that were received from the image sensor into the five column pixel data register buffer  530 . 
     The pixel data in “window”  540  of the line buffers  510  and the short five column register buffer  530  are sufficient for evaluating one term of the convolution sum. The evaluation is by multiplying the elements of the 5×5 mask w(m,n) of  FIG. 3  with corresponding values f(x+m,y+n) stored in the dashed areas including five columns of the line buffers  540  and five column register buffer  530 , and adding the signed products into an accumulation register thereby accumulating the 25 terms comprising g(x,y). This evaluation requires only: the 6 distinguishable coefficients of the matrix in  FIG. 3  (the matrix has 4-fold symmetry), the five columns  540  of the pixel data from 4 consecutive lines of buffered image data, and the five corresponding columns of pixels most recently received pixel data from the image sensor in buffer register  530 . After the term g(x,y) has been evaluated, its absolute value is formed and the absolute value of g(x,y) accumulated into a register comprising a partial sum of the numerator of s ij . 
     After evaluating g(x,y), the window of the line buffers and the most recently received pixel data are advanced to the right by one column to the position  550  as shown in the lower portion of  FIG. 5 . The oldest datum at [x−2,y−2] in window  540  of the top half of the figure is no longer needed for term evaluations. It is overwritten by the datum at [x−2,y−1] (e.g. effectively discarded as depicted by arrow  561  and replaced by the data from the position below as depicted by arrow  562 ). Accordingly, each of the data in column x−2 of the four line buffer  510  is shifted up one row as depicted by the arrows  562 ,  564 , and  566 : the datum at [x−2,y−1] is moved to position [x−2,y−2]  562 , the datum at [x−2,y] is moved to position [x−2,y−1], the datum at [x−2,y+1] is moved to [x−1,y]  564 , and the oldest pixel data [x−2,y+2] in leftmost position of the five column line buffer register as shown in  530 , is transferred into position [x−2,y+1] of the 4 line data buffer, depicted by arrow  568 . Next, the other values in the five column incoming pixel data buffer register  530  are left shifted by one column as depicted schematically by arrows  570 ,  571 ,  572 ,  573 . 
     The next pixel datum received from the image sensor is then read into the rightmost column of buffer register  530 . The next term, g(x+1,y) can then be evaluated as described for g(x,y) above. The process of evaluating a term of the convolution sum, shifting the pixel data positions, and advancing the window right one column is repeated until all of the terms in the row y of s ij  have been accumulated. The window position is then restarted at the left and terms of the next row, row y+1, are evaluated and accumulated in the same way. The sum s ij  is complete when all rows of subframe i have been processed. 
     However, evaluating g(x,y) in the two rows or two columns bordering the edge of a subframe requires data beyond the perimeter of the subframe (formally, the convolution sum for [x,y] requires data from two adjacent rows and column in each direction). In various embodiments, these edge values can be estimated by standard techniques for a boundary (e.g. extrapolation, mirroring, assuming the value of the first available row, truncation, etc.). In the instant embodiment, filtering is limited to the reduced subframe [W−2×H−2] so that physical row and column data from the image sensor are available to evaluate the terms. Of course the numbering of the indices and constants for boundary values are adjusted accordingly using standard techniques. Also, it will be understood that describing the movement of data in terms of “left” and “right,” “above” and “below,” or as rows and column, is only by way of explanation. In various embodiments these terms may be interchanged, or other terms may be used. While these terms are convenient for referencing the logical data structure, physical storage locations of the media are often mapped in various ways, depending on the application. 
     It is seen that a line buffer for a relatively small number of lines and a small number of storage registers are sufficient for implementing the predictor  130  and decision maker  140 . In an embodiment comprising four subframes, four sharpness values s ij  characterize motion in the j th  frame. The predictor in the embodiment depends on D max ,D 3j ,D 4j ,k 1 , and k 2 . The decision maker  140  depends on S j ,S c , predictor output, and the selected maximum number of frames. Hence the predictor and decision maker can be implemented using about 15 register cells for storing constants and values characteristic of the motion. Of course, depending on the application, other filtering methods and/or different filters may be used within the scope and spirit of the invention, including filter convolution matrices that are larger or smaller than the illustrative 5×5 matrix. In one embodiment according to the illustrative example, the number of line buffers operable to evaluate image sharpness (four line buffers in the embodiment of  FIG. 5 ) is one less than the dimensionality of the convolution matrix (5 in the matrix embodiment of  FIG. 3 ). In addition, a small register buffer is required for storing a number of pixel data, the number being equal to the dimensionality of the filtering matrix. Hence embodiments of the method and system have advantageously small buffer and register requirements, thereby reducing cost. 
       FIG. 6  illustrates simplified aspects of data flow in a digital camera embodiment of the invention. The camera has a processor  650 . The camera also has machine readable media including storage for instructions and data operable by the processor to implement a control program  640 , instructions to implement a quality detector, predictor and decision maker  630 , data storage for filter coefficients to implement a high frequency sharpness filter  620 , data storage for selected constants to implement other functions including the quality detector, predictor and decision maker, and data storage registers for storing and receiving values and results by the processor. Also, the camera has an image sensor. Responsive to an image, the image sensor sequentially sends image sensor pixel data  610  comprising lines of image sensor pixels to line buffers  670 . In the embodiment, the processor  650  has no direct access to the data in line buffers  670 . Control circuitry automatically mirrors a portion of the line buffers  670  into mirror registers  660 . The mirror registers  660  are interrogated by the processor. The mirrored data portion  660  includes the matrix of the pixel data, including the data in storage  540  and  530  for evaluating s ij  as described in connection with  FIG. 5 . 
     The camera also includes an image signal processor (ISP)  680  for processing pixel data  610  from a register  675  of the media, and transforming the pixel data into another form, such as compressed Joint Photographic Experts Group form (JPEG), for capturing and storing in a picture storage media  695 . The image sensor data is independently pipelined by control circuits from the image sensor into the line buffers  670 , the register  675  and the ISP. 
     In one embodiment, the control program in media  640  directs operation and data flow between the program modules of the quality detector, predictor and decision maker according to the method of  FIG. 4 . The processor performs the quality detector, accessing filter matrix coefficients and constants stored in media  620 , and saves results in register media  665 . Selected constants of the predictor and decision maker are also in media  620 . The evaluated quantities based on the image data (e.g. g(x,y),s ij ,D max  etc.), on the other hand, are saved into and accessed from register media  665 . When the decision maker decides to capture a next image, it outputs a capture signal on signal line  665  to gating control circuitry  690 . The gating control  690  captures a next image from the ISP into the picture storage  695  if and only the decision maker outputs the capture signal. 
     While the quality detector, predictor and decision maker are implemented by a processor operable to perform program code in the embodiment, in other embodiments various of these functions or all of these functions are implemented using control circuitry. Also, although the mirror registers  660  and quality detector in  630  of the embodiment detect image quality based on high a 5×5 matrix high pass filtering of the image data, in various other embodiments a quality detector is implemented based on wavelet transforms, or various other methods adapted to detect sharpness, depending on the application. 
       FIG. 7  is a graph showing the evolution of frame sharpness S j  (solid line  710 ), and successive differences between the current and previous subframe sharpness D ij =s ij −s ij−1 , (dashed line  720 ) during picture taking with an embodiment of an anti-shake camera. The left hand vertical axis  722  for S j  and the right hand vertical axis  724  for D ij  are marked in arbitrary units. Horizontal axis units are the total number of subframes, N, that have been evaluated since a starting time corresponding to N=0. After the camera is placed into a state preparatory to capturing an image, it begins to evaluate subframes. The line graphs of S j  and D ij  are drawn starting at N=4 (frame  1 ). N=4 is the subframe where the sharpness of the first image frame  781  consisting of the four subframes numbered 1 through 4 is evaluated. The sharpness  781  of image frame  1  is about 167 units. The illustrative camera in the embodiment corresponding to  FIG. 7  has a preset frame timeout parameter of 10 frames. 
     As shown in  FIG. 7 , after the evaluation of image frame  1  four more image frames are read and evaluated. A capture command is received just after processing the 20 th  subframe  734 . The 20 th  subframe corresponds to a fifth complete image frame starting from the beginning of the graph at N=0. Therefore image frame  6 , corresponding to the point  786 , is the first image frame that is evaluated after receiving the capture command. Also, the 10 th  image frame after the capture command is image frame  15  at  796  in  FIG. 7 . This is the image frame where the timeout maximum of 10 frames after the capture command is reached. Therefore the decision maker will not make a decision to capture based on a timeout override until after image frame  14  (subframe  56  at  738 ) has been evaluated. 
     After the capture command is received (N=20 in this example), there is increasing motion which is manifest by the negative sharpness value differences D ij  found in subframes  21  through  24 . Responsive to the increased motion, the sharpness of the next complete frame after receiving the capture command at N=24 (frame  6 )  786  is diminished relative to frame  5 . The sharpness of this first image frame following the capture instruction, referenced by the decision maker as S c =S 6  in the formulae above, has a value S 6 ≈−127. Motion continues to increase during the next 3 subframes,  25 - 27  as evidenced by negative D ij  (D ij &lt;0). At subframe  28  there is a small improvement (less motion, D ij &gt;0), but the increased motion during subframes  25 - 27  outweighs the relatively small improvement at subframe  28 . Hence at subframe  28  the metric of motion S c+1  (S 7 ), which is comprised of contributions from the four subframes N=25−28, has worsened relative to S 6  (S c ), decreasing from S 6 ≈−127 to S 7 ≈−318(S c+1 ). Since S c+1 ≈−318&lt;S c ≈−127, the decision maker does not capture. 
     At subframe  29  the level of motion is relatively stable (D ij =0) and starting at subframe  30  the level of motion significantly diminishes. The subframe sharpness continuously improves (D ij &gt;0) from subframe  30  through subframe  32 . This results in substantially increased sharpness of frame S 8 =S c+2 =788 which reaches a local maximum of about 240. Although S 8 &gt;S c  at this frame, the rate of sharpness improvement between the last successive subframes of frame  8  has decreased (D 4j &lt;D 3j , j=8). Therefore the next image motion is not predicted to improve (since neither Proposition 1 nor Proposition 2 are true) and the decision maker does not decide to capture the next image frame. 
     Starting at subframe  34 ,  790 , motion increases again (D ij &lt;0) resulting in renewed deterioration of the frame sharpness. At frame  9 ,  792 , the rate of sharpness improvement between the last two successive subframes of a frame has decreased again, e.g. an increased rate of sharpness deterioration, (D 4j &lt;D 3j &lt;0, j=9). Therefore the decision maker does not decide to capture a next frame. However after frame  9  (subframe  36 ) the rate of deterioration eases, as evidenced by D ij  increasing monotonically from subframe  36  of frame  10  ( 793 ) through subframe  40  of frame  10  ( 794 ). 
     When frame  10  is evaluated (subframe  40  at  794 ), the predictor predicts that next image frame motion will be less than the current image frame motion because Proposition 2 is true. This is apparent from the following considerations. D 4j &gt;D 3j , (j=10) as required by the last term of both Proposition 1 and Proposition 2. Next, it is seen that D 4j &lt;0 at frame  10  (subframe  40 ). Therefore Proposition 1 is false and it remains to evaluate the second term of Proposition 2 for deciding whether next image motion is predicted to be less than current image frame motion. |D ij |&lt;k 2 *D max  (k 2 =½), D max  is first determined by selecting the maximum value of |D ij | found in the interval that begins after the capture command at frame  20  and ends after the subframes of frame  40 . It is seen that |D ij | reaches a relative maximum value in this interval at subframe  31  ( 787 ) where D i,j ≈240. At frame  10 , (subframe  40 ) D 4j  is about −18 units. Therefore inequality: D 4,10 −18&lt;k 2 *D max ≈½*240=120 is satisfied at frame  10 . Hence, at frame  10  the Proposition 2 is true and the predictor predicts that the next image motion will be less than the current image frame motion. 
     Furthermore, at frame  10  (subframe  40 ), decision maker criteria to capture the next image frame are met because: 1) a capture command was received, 2) increasing sharpness is predicted by the predictor, and 3) the frame sharpness of frame  10  is greater than S c : S 10 ≈−54&gt;S c =S 6 ≈−127. Following the decision to capture at frame  10 , the next frame  796 , frame  11  at subframe  44 , is captured and stored in the image memory. Computed values of D ij  and the sharpness of some successive subframes following the illustrative captured frame are also included  FIG. 7  for comparison. It is seen that frame  11  is characterized by greater sharpness than any of the preceding or succeeding frames. Hence in this illustrative example, the inventive method captured the frame having the greatest sharpness during the time period shown (70 frames comprising about 2.3 sec., e.g. 30 frames per sec.). 
       FIG. 8  shows one example of a personal device that can be used as a digital camera or similar device. Such a device can be used to perform many functions, depending on implementation, such as telephone communications, two-way pager communications, personal organizing, global positioning system, or similar functions. The computer system  800  represents the computer portion of the device comprising a digital camera. The computer  800  interfaces to external systems through the communications interface  820 . This interface is typically some form of cable and/or wireless interface for use with an immediately available personal computer, and may include a radio interface for communication with a network such as an 802.11 wireless network. Of course various networks and communication methods such as a Bluetooth, an infrared optical interface, a cellular telephone interface and others, depending on the application. 
     The computer system  800  includes a processor  810 , which can be a conventional microprocessor such as an Intel Pentium microprocessor, an IBM power PC microprocessor, a Texas Instruments digital signal processor, or some combination of various types of processors, depending on the embodiment. Memory  840  is coupled to the processor  810  by a bus  870 . Memory  840  can be dynamic random access memory (DRAM) and can also include static ram (SRAM), flash memory, magnetic memory (MRAM) and other types, depending on the application. The bus  870  couples the processor  810  to the memory  840 , also to non-volatile storage  850 , to display controller  830 , and to the input/output (I/O) controller  860 . In some embodiments, various combinations of these components are integrated in a single integrated circuit or in a combination of integrated circuits that are combined into a single package. Note that the display controller  830  and I/O controller  860  are often be integrated together, and the display may also provide input. 
     The display controller  830  controls in the conventional manner of a display controller on a display device  825  which typically is a liquid crystal display (LCD) or similar flat-panel, small form factor display. The input/output devices  855  can include a keyboard, or stylus and touch-screen, and may sometimes be extended to include disk drives, printers, a scanner, and other input and output devices, including a mouse or other pointing device, such as when a camera is connected to some form of docking station or personal computer. The display controller  830  and the I/O controller  860  can be implemented with conventional well known technology. A digital image input device  865  can be a digital camera comprising an embodiment of the invention which is coupled to an I/O controller  860  or through a separate coupling in order to allow images to be input into the device  800 . 
     The non-volatile storage  850  is often a FLASH memory or read-only memory, or some combination of the two. A magnetic hard disk, an optical disk, or another form of storage for large amounts of data may also be used in some embodiments, though the form factors for such devices typically preclude installation as a permanent component of the device  800 . Rather, a mass storage device on another computer is typically used in conjunction with the more limited storage of the device  800 . Some of this data is often written, by a direct memory access process, into memory  840  during execution of software in the device  800 . One of skill in the art will immediately recognize that the terms “machine-readable medium” or “computer-readable medium” include any type of storage device that is accessible by the processor  810  and also encompasses a carrier wave that encodes a data signal. 
     The device  800  is one example of many possible devices which have different architectures. For example, devices based on an Intel microprocessor often have multiple buses, one of which can be an input/output (I/O) bus for the peripherals and one that directly connects the processor  810  and the memory  840  (often referred to as a memory bus). The buses are connected together through bridge components that perform any necessary translation due to differing bus protocols. 
     In addition, the device  800  is controlled by operating system software which may include a file management system, such as a disk operating system, which is part of the operating system software. One example of an operating system with its associated file management system software is the family of operating systems known as Windows CE® from Microsoft Corporation of Redmond, Wash., and their associated file management systems. Another example of an operating system with its associated file management system software is the Palm® operating system and its associated file management system. However, it is common for digital cameras to have much less developed file management software and associated user interfaces. The file management system is typically stored in the non-volatile storage  850  and causes the processor  810  to execute the various acts required by the operating system to input and output data and to store data in memory, including storing files on the non-volatile storage  850 . Other operating systems may be provided by makers of devices, and those operating systems typically will have device-specific features which are not part of similar operating systems on similar devices. Similarly, WinCE® or Palm® operating systems may be adapted to specific devices for specific device capabilities. 
     Device  800  may be integrated onto a single chip or set of chips in some embodiments, and typically is fitted into a small form factor for use as a personal device. Thus, it is not uncommon for a processor, bus, onboard memory, and display-I/O controllers to all be integrated onto a single chip. Alternatively, functions may be split into several chips with point-to-point interconnection, causing the bus to be logically apparent but not physically obvious from inspection of either the actual device or related schematics. 
     Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or “evaluating” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. Thus the apparatus may be embodied in a medium. 
     One skilled in the art will appreciate that although specific examples and embodiments of the system and methods have been described for purposes of illustration, various modifications can be made without deviating from the spirit and scope of the present invention. For example, embodiments of the present invention may be applied to many different types of image acquisition systems, imaging devices, databases, application programs and other systems. Moreover, features of one embodiment may be incorporated into other embodiments, even where those features are not described together in a single embodiment within the present document. Accordingly, the invention is described by the appended claims.