Patent Publication Number: US-7916173-B2

Title: Method for detecting and selecting good quality image frames from video

Description:
FIELD OF THE INVENTION 
     The present invention relates to image analysis and applications based upon this analysis, particularly based upon the detection of motion, image blur arising from imperfect focus, and image exposure. 
     BACKGROUND 
     Amateur “home” video footage often contains scenes of poor quality. Thus, for example, a significant amount of home video footage contains out-of-focus subject matter, over-exposed and under-exposed scenes, or excess camera motion. This is often the case even when the capture device is equipped with modern day intelligent camera features that simplify and enhance the filming process. Poor quality scenes not only degrade the overall integrity of the video subject matter, but can exacerbate the difficulty of searching for good quality images or frames from within the video footage. Extraction of good quality frames from a long sequence of video footage can be performed manually. However since current storage media can archive 3 or more hours of video footage, this is laborious and time consuming. 
     SUMMARY OF THE INVENTION 
     According to a first aspect of the invention, there is provided a method of determining a quality value of a frame, the method comprising the steps of:
         dividing the frame into a plurality of tiles:   determining attributes of each said tile based upon (i) pixel values of the tile and (ii) pixel values of a corresponding tile of a preceding frame; and   establishing the quality value of the frame by:
           testing the tile attributes of the frame against pre-determined criteria; and   defining the quality value of the frame depending upon results of said testing.   
               

     According to another aspect of the invention, there is provided an apparatus for determining a quality value of a frame, the apparatus comprising:
         means for dividing the frame into a plurality of tiles:   means for determining attributes of each said tile based upon (i) pixel values of the tile and (ii) pixel values of a corresponding tile of a preceding frame; and   means for establishing the quality value of the frame comprising:
           means for testing the tile attributes of the frame against pre-determined criteria; and   means for defining the quality value of the frame depending upon results of said testing.   
               

     According to another aspect of the invention, there is provided a computer program product having a computer readable medium having a computer program recorded therein for directing a processor to execute a method of determining a quality value of a frame, said computer program product comprising:
         code for dividing the frame into a plurality of tiles:   code for determining attributes of each said tile based upon (i) pixel values of the tile and (ii) pixel values of a corresponding tile of a preceding frame; and   code for establishing the quality value of the frame comprising:
           code for testing the tile attributes of the frame against pre-determined criteria; and   code for defining the quality value of the frame depending upon results of said testing.   
               

     According to another aspect of the invention, there is provided a computer program for directing a processor to execute a method of determining a quality value of a frame, said computer program product comprising:
         code for dividing the frame into a plurality of tiles:   code for determining attributes of each said tile based upon (i) pixel values of the tile and (ii) pixel values of a corresponding tile of a preceding frame; and   code for establishing the quality value of the frame comprising:
           code for testing the tile attributes of the frame against pre-determined criteria; and   code for defining the quality value of the frame depending upon results of said testing.   
               

     According to another aspect of the invention, there is provided a video frame selected from a sequence of video frames dependent upon a quality value of the frame, the frame being selected by a method of determining a quality value of a video frame, said method being applied to each frame in the sequence, said method comprising, in regard to a particular frame in the sequence, the steps of:
         dividing the frame into a plurality of tiles:   determining attributes of each said tile based upon (i) pixel values of the tile and (ii) pixel values of a corresponding tile of a preceding frame; and   establishing the quality value of the frame by:
           testing the tile attributes of the frame against pre-determined criteria; and   defining the quality value of the frame depending upon results of said testing.   
               

     According to another aspect of the invention, there is provided a method of estimating quality of an image in a video, the video comprising a plurality of images, each image comprising a plurality of pixels, said method comprising the steps of:
         arranging, for a current image of said video, pixels of the current image into a plurality of tiles, each said tile containing a plurality of pixels;   determining the number of the tiles of the current image having a defined characteristic; and   estimating quality of the current image based on the number of said tiles having the characteristic as a proportion of the number of tiles in the current image.       

     According to another aspect of the invention, there is provided a method of selecting an image from a video comprising a plurality of images, each image comprising a plurality of pixels, and said method comprising the steps of:
         (a) performing, for each image of said plurality of images, the steps of:
           arranging pixels of the image into a plurality of tiles, each said tile containing a plurality of pixels;   determining the number of the tiles of the image having a defined characteristic; and   estimating quality of the image based on the number of said tiles having the characteristic as a proportion of the number of tiles in the image; and   
           (b) selecting an image from said video in accordance with the estimated quality.       

     Other aspects of the invention are disclosed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       One or more embodiments of the present invention will now be described with reference to the drawings, in which: 
         FIG. 1  is functional block diagram of a system for selecting good quality frames from a video data stream; 
         FIG. 2  is a schematic block diagram of a general purpose computer upon which the disclosed method can be practiced; 
         FIG. 3  shows the correspondence between sub-processes of the disclosed method and the associated drawings in this description; 
         FIG. 4  shows a schematic block diagram of a system for detecting motion, focus and quality in video; 
         FIG. 5  shows a flow diagram of a method of calculating statistical data, motion data and focus data for each tile of tiled video frame image data in the motion and focus analysis unit  122  in  FIG. 4 ; 
         FIG. 6  shows a schematic diagram of a tiled video frame image comprising sample data from the luminance channel Y; 
         FIG. 7  shows a flow diagram of a method of calculating the statistical data of a tile at step  206  in  FIG. 5 ; 
         FIG. 8  shows a flow diagram of a method of calculating the motion data of a tile at step  210  in  FIG. 5 ; 
         FIG. 9  shows a flow diagram of a method of calculating the focus data of a tile at step  212  in  FIG. 5 ; 
         FIG. 10  shows a flow diagram of a method of calculating image quality data of a tiled image in the image quality analysis unit  126  in  FIG. 4 ; 
         FIG. 11  shows an alternative embodiment of a flow diagram of a method of calculating image quality data of a tiled image in the image quality analysis unit  126  in  FIG. 4 ; and 
         FIG. 12  shows a flow diagram of a method of classifying a tile at step  220  in  FIG. 5 . 
     
    
    
     DESCRIPTION OF PREFERRED EMBODIMENT 
     Where reference is made in any one or more of the accompanying drawings to steps and/or features, which have the same reference numerals, those steps and/or features have for the purposes of this description the same function(s) or operation(s), unless the contrary intention appears. 
     It is to be noted that the discussions contained in the “Background” section and that above relating to prior art arrangements relate to discussions of processes or devices which form public knowledge through their respective publication and/or use. Such should not be interpreted as a representation by the present inventor(s) or patent applicant that such processes or devices in any way form part of the common general knowledge in the art. 
     The described arrangements assess individual frames in a video sequence of frames of image data on the basis of the quality of the frames. Each frame is divided into tiles of pixels, and the quality of the frame is assessed by estimating the amount of motion and the degree of focus within each tile, as well as the histogram of pixel luminance values within the frame. As will be described in relation to equations [9] and [15], provided that certain criteria are met, a tile can be analyzed for motion attributes to yield a motion magnitude M MAG , and for focus attributes to yield a focus magnitude F MAG . 
     Turning to the above-mentioned criteria, if the range of pixels in a particular tile is too small, the amount of detail discernable within that tile is too small to enable a meaningful analysis to be performed. In this case, meaningful estimates of motion magnitude M MAG  or focus magnitude F MAG  cannot be made. Furthermore, meaningful estimates of focus magnitude cannot be made if there is too much motion detected in a particular tile. In such cases, an estimation of focus blur is not meaningful, as additional effects will be introduced by motion blurring. 
     Tiles that do not have a meaningful motion or focus magnitude are classified as being “motion-undefined” or “focus-undefined” respectively. Conversely, tiles that do have a meaningful motion or focus magnitude are classified as being “motion-defined” or “focus-defined” respectively. A tile which is both “motion-defined” and “focus-defined” is classified as “known”. If the motion magnitude is below a threshold of acceptable motion, and the focus magnitude is above a threshold of acceptable focus, the tile is classified as “good”, and otherwise it is classified as “bad”. 
     As will be described later, a frame is analyzed to determine whether it has acceptable exposure, being neither under-exposed nor over-exposed. This analysis uses an exposure metric, EM. 
     A frame is classified as being of “acceptable” or “unacceptable” quality. Classification of a frame as “acceptable” depends on three criteria: first, the proportion of known tiles must exceed a threshold; second, the proportion of good tiles must exceed a threshold; and third, the exposure metric must exceed a threshold. Additionally, each frame is assigned a quality metric, QM, that measures the overall quality of the frame. The quality metric is calculated from the exposure metric, EM, the proportions of “good” and “bad” tiles, and the focus magnitudes of the tiles that are “good”. 
     Video frame images of the video stream  110  can be ranked by comparing their respective quality metric values QM. Unacceptable images, i.e., those images that do not meet the aforementioned criteria, may be discarded as soon as the frame fails one of the three criteria. This reduces the processing required to search video data for good frames. 
       FIG. 1  is functional block diagram of an embedded system in a video camera for selecting good quality frames from a video data stream. An optical signal  1101  is captured by an optical/electronic conversion module  1103  and converted to an electronic data stream  110  which is also referred to as the video data stream  110 . The raw captured video footage contained in the stream  110  is stored in an onboard image store  1105  which can be removable, or fixed in which case the images can be downloaded to an external computer system  1000  (see  FIG. 2 ) via a data port (not shown). A reference numeral  1106  depicts the stored raw captured video footage. 
     The disclosed arrangement for identifying good quality frames in an image stream processes the data stream  110  in a quality analysis module  120 . The module  120  analyzes the data stream  110  in real time, and outputs quality information at  1112  to a frame identification/selection module  1113 . According to one arrangement, the frame identification/selection module  1113  identifies, on the basis of the quality information  1112 , good quality images which are then stored according to an arrow  1111  in the image store  1105  as depicted by  1109 . According to another arrangement, the frame identification/selection module  1113  generates and stores, on the basis of the quality information  1112 , meta-data pointers to good quality frames in the data stream  110 . The module  1113  stores the pointers according to an arrow  1110  in the image store  1105 . A reference numeral  1108  depicts the stored meta-data pointers. An arrow  1107  depicts the fact that the pointer meta-data  1108  points to images in the stored raw footage  1106 . 
     According to another arrangement, the stored image data  1106  can be provided according to an arrow  1114  to the quality analysis module  120  for off-line post processing, instead of performing the identification of good quality images on the fly using the data stream  110 . 
     The disclosed arrangement in  FIG. 1  enables the operator of the video camera  1100  to operate a control (not shown) for an appropriate length of time in order to capture desired subject matter. In one arrangement, the captured sequence of images  110  is buffered in the memory store  1105  and subsequently processed (as depicted by the arrow  1114 ) to identify and select the “best” quality frame in that captured sequence  1106 . In an alternate arrangement, the desired still image can be identified in the captured video data bit stream  1106 , and the meta-data pointer can be inserted into metadata  1108  associated with the captured video data bit stream  1106 . The pointer would point, as depicted by the arrow  1107 , to the appropriate position in the stored data  1106  that corresponds to the video frame that is considered to be of good quality. The pointer(s) can be accessed from the video metadata  1109  for subsequent post-processing. 
     The disclosed arrangements increase the probability of capturing a better shot or image of the desired subject matter by providing a selection of candidate frames that can be processed for relative quality to thus identify the best frame capturing the desired subject matter. 
       FIG. 2  shows an alternate arrangement which performs post-processing of the raw video footage  1106  from  FIG. 1 . In this arrangement the method of identifying good quality images is implemented as software, such as an application program, executing within a computer system  1000 . In particular, the method steps of a method of identifying good quality images, as described in relation to  FIGS. 3 ,  5 , and  7 - 11  are effected by instructions in the software that are carried out by the computer. The instructions may be formed as one or more code modules, each for performing one or more particular tasks. The software may also be divided into two separate parts, in which a first part performs the methods for identifying good quality images and a second part manages a user interface between the first part and the user. The software may be stored in a computer readable medium, including the storage devices described below, for example. The software is loaded into the computer from the computer readable medium, and is then executed by the computer. A computer readable medium having such software or computer program recorded on it is a computer program product. The use of the computer program product in the computer preferably effects an advantageous apparatus for identifying good quality images. 
     The computer system  1000  comprises a computer module  1001 , input devices such as a keyboard  1002  and mouse  1003 , output devices including a printer  1015 , a display device  1014  and loudspeakers  1017 . A Modulator-Demodulator (Modem) transceiver device  1016  is used by the computer module  1001  for communicating to and from a communications network  1007 , for example connectable via a telephone line  1006  or other functional medium. The modem  1016  can be used to obtain access to the Internet, and other network systems, such as a Local Area Network (LAN) or a Wide Area Network (WAN), and may be incorporated into the computer module  1001  in some implementations. 
     The computer module  1001  typically includes at least one processor unit  1005 , and a memory unit  1006 , for example formed from semiconductor random access memory (RAM) and read only memory (ROM). The module  1001  also includes an number of input/output (I/O) interfaces including an audio-video interface  1007  that couples to the video display  1014  and loudspeakers  1017 , an I/O interface  1013  for the keyboard  1002  and mouse  1003  and optionally a joystick (not illustrated), and an interface  1008  for the modem  1016  and the printer  1015 . In some implementations, the modem  1016  may be incorporated within the computer module  1001 , for example within the interface  1008 . A storage device  1009  is provided and typically includes a hard disk drive  1010  and a floppy disk drive  1011 . A magnetic tape drive (not illustrated) may also be used. A CD-ROM drive  1012  is typically provided as a non-volatile source of data. The components  1005  to  1013  of the computer module  1001  typically communicate via an interconnected bus  1004  and in a manner that results in a conventional mode of operation of the computer system  1001  known to those in the relevant art. Examples of computers on which the described arrangements can be practiced include IBM-PCs and compatibles, Sun Sparcstations or like computer systems evolved therefrom. 
     Typically, the application program for identifying good quality images, and the media item files associated with the raw captured video footage  1106 , are resident on the hard disk drive  1010  and read and controlled in its execution by the processor  1005 . Intermediate storage of the program and any data fetched from the computer memory  1009  or the network  1007  may be accomplished using the semiconductor memory  1006 , possibly in concert with the hard disk drive  1010 . In some instances, the application program for identifying good quality images may be supplied to the user encoded on a CD-ROM  1021  or a floppy disk  1020  and read via the corresponding drive  1012  or  1011 , or alternatively may be read by the user from the network  1007  via the modem device  1016 . Still further, the software for identifying good quality frames can also be loaded into the computer system  1000  from other computer readable media. The term “computer readable medium” as used herein refers to any storage or transmission medium that participates in providing instructions and/or data to the computer system  1000  for execution and/or processing. Examples of storage media include floppy disks, magnetic tape, CD-ROM, a hard disk drive, a ROM or integrated circuit, a magneto-optical disk, or a computer readable card such as a PCMCIA card and the like, whether or not such devices are internal or external of the computer module  1001 . Examples of transmission media include radio or infra-red transmission channels as well as a network connection to another computer or networked device, and the Internet or Intranets including e-mail transmissions and information recorded on Websites and the like. 
     The method of identifying good quality frames may alternatively be implemented in dedicated hardware such as one or more integrated circuits performing the functions or sub functions of identifying good quality images. Such dedicated hardware may include graphic processors, digital signal processors, or one or more microprocessors and associated memories. 
       FIG. 3  shows the correspondence between sub-processes of the disclosed method and the associated drawings in this description. Given the sequence of video frames  110 , a particular frame  903  is considered, as depicted by a dashed arrow  902 , and is subjected, as depicted by a dashed arrow  904 , to an analysis process that is initially to be described in relation to  FIG. 5 . The frame  903  is tiled, in a manner to be described in relation to  FIG. 6 , and a tile  906  is processed, as depicted by a dashed arrow  907 , to produce a histogram of pixel values, a tile of difference quotients, and maximum and minimum pixel values as will be described in relation to  FIG. 7 . The disclosed method then proceeds, as depicted by a dashed arrow  909 , to perform tile classification in relation to “motion” attributes, as will be described in relation to  FIG. 8 . The disclosed method then proceeds, as depicted by a dashed arrow  911 , to perform tile classification in relation to “focus” attributes, as will be described in relation to  FIG. 9 . The disclosed method then proceeds, as depicted by a dashed arrow  913 , to perform tile classification as “good”, “bad”, or “unknown”, as will be described in relation to  FIG. 12 . The aforementioned tile-based processes are repeated for all or most tiles in the frame  903 , after which the disclosed method proceeds, as depicted by a dashed arrow  915 , to determine frame based parameters as will be described in relation to  FIG. 10 . 
       FIG. 4  shows a functional block diagram of the quality analysis module  120  which is used to detect attributes relating to motion, focus, and quality in the video stream  110 . The video sequence  110 , which comprises frames of image data, is analyzed in the quality analysis module  120 . This analysis produces image quality data  130  which is output from the module  120  for each frame of the video  110 . The input video frame image data  110  comprises an array of image pixel values for each frame. Each pixel of a frame in a typical video sequence has a number of color components, each component being represented by 8 bits of data. In the described arrangement, each pixel is represented by one 8-bit value, typically based upon the luminance channel (Y) of the video color space which may comprise YUV or YCbCr. 
     The data for the chrominance channels (U, V or Cb, Cr) are typically discarded in this arrangement. Retaining only the luminance component of a pixel ensures that the most important information (i.e., luminance) is used for analyzing motion and focus. Another benefit of this approach is that the quality processing of each frame of the input data  110  is simplified and fast, and the buffer requirements on the quality analysis module  120  are reduced. 
     Each frame of the video frame image data  110  is passed to a motion and focus analysis unit  122 . In the unit  122  the frame is divided into tiles, and motion and focus data  124  is determined for each tile of the frame. The motion and focus data  124  for the frame is further processed in an image quality analysis unit  126 . In the unit  126 , the image quality data  130  is determined for the frame, based on the amount and distribution of the focus and motion information among the tiles within the frame. 
       FIG. 5  shows a flow diagram of a process  218  that determines statistical data, motion data and focus data for each tile of a current frame of the video frame image data  110 . The motion and focus analysis unit  122  of  FIG. 4  performs the process  218  for each frame it is given. The process  218  begins at a step  200 . A following step  202  divides the current frame of the video data  110  into a plurality of tiles in a manner described in relation to  FIG. 6 . The step  204  acquires a current tile for processing. A following step  206  determines statistics for the current tile as is described in more detail in regard to  FIG. 7 . A following step  222  stores the statistics of the current tile, for later use. 
     If a subsequent testing step  208  determines that the frame currently being processed is the first frame in a sequence of frames of the video frame image data  110 , then the process  218  proceeds to a decision step  214 . This test relates to the fact that comparison with previous frames is performed in the disclosed method. If the frame being considered is the first frame of the video data stream  110 , or alternately the first frame of a particular video shot in the stream, then comparison cannot be performed. If the step  214  determines that the current tile is not the last tile in the current frame, then the process  218  proceeds back to the step  204 . If, however, the step  214  determines that the current tile is the last tile in the current frame, then the process  218  terminates at a step  216 . 
     Returning to the step  208 , if it is determined that the frame currently being analyzed by the motion and focus analysis unit  122  is not the first frame in a sequence of frames of the video frame image data  110 , then the process  218  proceeds to a step  224  that retrieves the tile statistics of the corresponding tile in the previous video frame image. A following step  210  determines motion data for the current tile as is described in more detail in regard to  FIG. 8 . The process then proceeds to a step  212  that determines focus data for the current tile as described in more detail in regard to  FIG. 9 . After the tile focus data is calculated by the step  212 , the process then proceeds to a step  220  that classifies the tile as “good”, “bad”, or “unknown”, as described in more detail in regard to  FIG. 12 . After the tile classification is performed by the step  220 , the process proceeds to a step  214 . 
     If it is determined by the step  214  that the current tile is not the last tile in the current frame, then the method is directed to the step  204 . Otherwise, if it is determined at the step  214  that the current tile is the last tile in the current frame, then the process  218  terminates at the step  216 . 
       FIG. 6  is a schematic diagram of a tiled video frame  300  made up only from the luminance channel Y. The width and height of the video frame image  300 , in units of pixels, are W 1  and H 1  respectively. Each tile  301  of the frame  300  comprises a fixed size rectangular array of pixels having a width W T  pixels and a height H T  pixels. The frame  300  is divided into N tiles across the width of the frame  300  and M tiles down the height of the frame  300 , where
 
 N =└( W   I −3)/ W   T ┘,  [1]
 
 M =└( H   I −3)/ H   T ┘,  [2]
 
and └ . . . ┘ is the floor operator. The floor operator applied to a number x (i.e., └x┘) yields the largest integer less than or equal to x. Some pixels near the edges of the frame are not a part of any tiles, but instead are part of the frame border. Borders  302  and  305  of width two pixels are left between the top and left edges of the frame and the tiles in the frame. Borders  303  and  304  of width not less than one pixel are left between the bottom and right edges of the frame and the tiles in the frame.
 
       FIG. 7  shows a flow diagram of the process  206  in  FIG. 5  for determining statistical data for the current tile. The process  206  begins at a step  400 . A following step  402  generates a histogram with N b  bins for the luminance values of the tile pixels. In the typical case, luminance values are represented as 8-bit numbers, so that the possible luminance values lie in the range 0 to 255. Thus for 0≦i&lt;N b , the value of the i-th histogram bin is the number of pixels in the tile that have luminance value in the range from 255i/N b  to 255(i+1)/N b −1. 
     A following step  404  determines difference quotients (D V ) i,j  and (D H ) i,j , representing a measure of image focus, for each 0≦i&lt;H T  and 0≦j&lt;W T  as follows:
 
( D   V ) i,j   =f (S i−2,j   , S   y−1,j   , S   i,j   , S   i+1,j )  [3]
 
( D   H ) i,j   =f (S i,j−2   , S   i,j−1   , S   i,j   , S   i,j+1 )  [4]
 
where S i,j  is the value of the pixel with a displacement of i pixels vertically and j pixels horizontally from the top-left pixel in the current tile, and f(x 0 , x 1 , x 2 , x 3 ) is a function whose value is undefined if for any i=0, 1, 2, or 3, x 1  is greater than a threshold T H ; otherwise, if |x 1 −x 21  is less than a threshold T W  the value of f(x 0 , x 1 , x 2 , x 3 ) is 0; otherwise, the value is given by
 
     
       
         
           
             
               
                 
                   
                     f 
                     ⁡ 
                     
                       ( 
                       
                         
                           x 
                           0 
                         
                         , 
                         
                           x 
                           1 
                         
                         , 
                         
                           x 
                           2 
                         
                         , 
                         
                           x 
                           3 
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                        
                       
                         
                           x 
                           1 
                         
                         - 
                         
                           x 
                           2 
                         
                       
                        
                     
                     
                       
                          
                         
                           
                             x 
                             0 
                           
                           - 
                           
                             x 
                             1 
                           
                         
                          
                       
                       + 
                       
                          
                         
                           
                             x 
                             1 
                           
                           - 
                           
                             x 
                             2 
                           
                         
                          
                       
                       + 
                       
                          
                         
                           
                             x 
                             2 
                           
                           - 
                           
                             x 
                             3 
                           
                         
                          
                       
                     
                   
                 
               
               
                 
                   [ 
                   
                     4 
                     ⁢ 
                     A 
                   
                   ] 
                 
               
             
           
         
       
     
     Use of T H  discards difference quotients in the vicinity of saturated pixels, where focus determination is unreliable. Use of T W  discards difference quotients which may be large in value due to the presence of image noise. 
     Note that the values of the difference quotients (D V ) i,j  and (D H ) i,j  will depend on the values of pixels outside the current tile, which may lie in neighbouring tiles, or in the border. Note also that it can be shown that the values of (D V ) i,j  and (D H ) i,j  will always lie between 0 and 1. 
     At a following step  406 , the maximum S max  and minimum S min  pixel luminance values of the current tile are obtained as follows:
 
 S   max =Max ( S   i,j ) [5]
 
 S   min =Min ( S   i,j ) [6]
 
where S max  and S min  are respectively the maximum and minimum pixel luminance values from S i,j .
 
     The process  206  terminates at a following step  408 . 
       FIG. 8  shows a flow diagram of the process  210  for calculating the motion data of the current tile at the step  210  in  FIG. 5 . The process  210  begins at a step  500 . A subsequent decision step  502  tests whether the luminance ranges of the current tile and the corresponding previous tile are not “too small”. For the luminance ranges not to be too small, the Sample Value Range (SVR) for either of the current tile and the corresponding tile of the previous video frame must exceed a predefined luminance threshold R T . This is expressed mathematically as follows:
 
 SVR   c   =S   c   max   −S   c   min   &gt;R   T   [7] OR
 
 SVR   p   =S   p   max   −S   p   min   &gt;R   T   [8]
 
where:
 
     SVR c  and SVR p  are the respective pixel value ranges for the current tile of the current and previous video frames;
         S c   max  and S p   max  are the respective maximum pixel luminance values for the current tile of the current and previous video frames; and   S c   min  and S p   min  are the respective minimum pixel luminance values for the current tile of the current and previous video frames.       

     If the step  502  determines that the luminance range of one of the corresponding “current” tiles of the current video frame and the previous video frame is not “too small”, i.e., at least one of the equations [7] and [8] is satisfied, then the process  210  proceeds to a step  504  that classifies the current tile of the current frame as having “defined” motion, i.e., as being “motion defined”. Otherwise, if the luminance range of one of the corresponding “current” tiles of the current video frame and the previous video frame is found to be too small, i.e., neither one of the Equations [7] and [8] are satisfied, then the process  210  proceeds to a step  508 . The step  508  determines if the absolute value of the difference of Mid-Range pixel Values (MRV) for the corresponding “current” tiles of the current and previous frame is not “too small”. For the absolute value of the difference of Mid-Range pixel Values not to be too small, the absolute value in Equation [10] must exceed the predetermined mid-range difference threshold MR T  as follows:
 
| MRV   c   −MRV   p   |&gt;MR   T   [10]
 
where:
 
 MRV   c =( S   c   max   +S   c   min )/2[11]
 
 MRV   p =(S p   max   +S   p   min )/2  [12]
 
and:
 
     MRV c  and MRV p  are the respective Mid-Range pixel Values for the current tile of the current and previous video frames;
         S c   max  and S p   max  are the respective maximum pixel luminance values for the current tile of the current and previous video frames; and   S c   min  and S p   min  are the respective minimum pixel luminance values for the current tile of the current and previous video frames.       

     If the absolute value of the difference between the mid-range values of the corresponding current tiles is not too small, i.e., if the Equation [10] is satisfied, then the process  210  proceeds to the step  504 . Otherwise, if at the step  508  the Equation [10] is not satisfied then the process  210  proceeds to a step  510  that classifies the current tile of the current video frame as having “undefined” motion. In this case it is not possible to determine whether any motion has occurred, because the subject matter in that tile contains no detail. Even if the subject moved, there may be no discernable change. The process  210  is then directed to the terminating step  512 . 
     A step  504  classifies the current tile of the current frame as having “defined” motion, i.e., as being “motion defined”. The motion magnitude M MAG  for the current tile is then determined by a following step  506  as follows: 
                     M   MAG     =         ∑   i       N   b       ⁢            H   i   c     -     H   i   p                      [   9   ]               
where:
         M MAG  is the motion magnitude for the current tile;   N b  is the number of histogram bins, as previously described in reference to  FIG. 7 ; and   H i   p  and H i   c  are the i-th histogram bins of the current and previous video frame histograms.       

     The process  210  then proceeds to terminating step  512 . 
     Apart from calculating a measure of motion, Equation [9] has an additional advantage that M MAG  will increase when lighting changes, due to either a change in scene brightness or camera exposure. This will have the effect that frames during which lighting is changing are more likely to be discarded. 
       FIG. 9  shows a flow diagram of the process  212  for determining focus data of the current tile at the step  212  in  FIG. 5 . The process  212  begins at a step  600 . A following step  602  determines if either (i) the Sample Value Range (SVR) of the current tile is less than the predetermined luminance threshold R T  (see Equations [7] and [8]) or (ii) there is too much motion in the current tile. The requirement “too much motion” requires that the current tile be “motion defined” AND the also that motion magnitude M MAG  (see equation [9]) exceed a pre-determined motion blur threshold MB T . Thus for the answer in step  602  to be YES, either (A) the following equation [13] must be satisfied OR (B) the current tile must have defined motion according to the step  504  in  FIG. 8  AND Equation [14] must be satisfied: 
                       SVR   c     =         S   max   c     -     S   min   c       &lt;     R   T         ⁢     
     ⁢   OR           [   13   ]                 M   MAG     =           ∑   i       N   b       ⁢            H   i   c     -     H   i   p              &gt;     MB   T               [   14   ]               
where:
         SVR c  is the sample value range of the current tile;   M MAG  is the motion magnitude of the current tile of the current frame relative to the corresponding “current” tile of the process frame; and   MB T  is the predetermined motion blur threshold.       

     If the answer in step  602  is NO, then a following step  604  determines if the number of difference quotients, (D V ) i,j  and (D H ) i,j , that are defined is less than a threshold T M . 
     If the answer in step  604  is NO, a following step  606  classifies the current tile as having “defined” focus, i.e., as being “focus defined”. Following step  606 , a step  608  calculates the focus magnitude F MAG  for the present tile to be the average of the T N  largest difference quotients in the present tile, where T N  is a constant parameter, i.e., 
                     F   MAG     =       1     T   N       ⁢       ∑     n   =   1       T   N       ⁢     x   n                 [   15   ]               
where the x n  are the largest T N  values of (D V ) i,j  and (D H ) i,j . Since (D V ) i,j  and (D H ) i,j  have values between 0 and 1, F MAG  also has a value between 0 and 1.
 
     The process  212  is then directed to a termination step  612 . 
     Returning to the steps  602  and  604 , if the answers to either of these steps is YES, then the process  212  proceeds to a following step  610 , which attributes the current tile with “undefined” focus. The process  212  then proceeds to a termination step  612 . 
     In the described arrangement, tiles that contain “too much” motion are classified as “focus-defined” because the focus calculations for each tile should result from focus blur only, and not motion blur. Motion may lead to high difference quotients which may falsely indicate sharp (i.e., not blurred) image features. 
       FIG. 12  shows a flow diagram of the process  220  for classifying the current tile as either “good”, “bad”, or “unknown” at the step  220  in  FIG. 5 . The process  220  begins at a step  1200 . A following step  1202  checks whether the tile has “defined” motion. If the result is NO, a following step  1214  classifies the current tile as “unknown” quality. The process is then directed to a terminating step  1212 . 
     Returning to the step  1202 , if the result is YES, then the process proceeds to a following step  1204 . The step  1204  checks whether the tile has too much motion, by checking whether the motion magnitude M MAG  exceeds the predetermined motion blur threshold MB T . (See Equation [14].) Thus, for the step  1204  to be YES, Equation [16] must be satisfied:
 
M MAG ≧MB T   [16]
 
     If the step  1204  is YES, a following step  1216  classifies the tile as “bad” quality. The process is then directed to a terminating step  1212 . 
     Returning to the step  1204 , if the result is NO, then the process proceeds to a following step  1206 . The step  1206  checks whether the tile has “defined” focus. If the result is NO, a following step  1218  classifies the tile as “unknown” quality. The process is then directed to a terminating step  1212 . 
     Returning to the step  1206 , if the result is YES, then the process proceeds to a following step  1208 . The step  1208  checks whether the tile has an acceptably large focus magnitude F MAG . This occurs when the focus magnitude is larger than a predetermined focus blur threshold FBT, that is, when
 
F MAG ≧FB T   [17]
 
     If the step  1208  is NO, then a following step  1220  classifies the tile as “bad” quality. The process is then directed to a terminating step  1212 . 
     Returning to the step  1208 , if the result is YES, then the process proceeds to a following step  1210  which classifies the tile as “good” quality. The process is then directed to a terminating step  1212 . 
     Returning to  FIG. 4 , the motion and focus data  124  that is generated by the motion and focus analysis unit  122 , according to the processes described in relation to  FIGS. 5 ,  7 - 9 , and  12 , is further analyzed by the image quality analysis unit  126 . In the described arrangement, the data  124  typically comprises, for each tile in the current frame, (a) a classification of each tile as being good, bad, or unknown; and (b) the focus magnitude value F MAG , if it is defined. 
       FIG. 10  shows a flow diagram of a process  701  for calculating image quality data  130  for the current video frame using the motion and the focus data  124  that is produced by the motion and focus analysis unit  122  in  FIG. 4 . The process  701  is performed by the image quality analysis unit  128 . The process  701  begins at a step  700 . A following step  702  determines the number of “good” tiles in the current frame. This number is hereinafter referred to as the Good Tile Count, N GOOD . Similarly, the step  702  also determines the number of “bad” tiles in the current frame. This number is hereinafter referred to as the Bad Tile Count, N BAD . Additionally, the step  702  also determines the number of “unknown” tiles in the current frame. This number is hereinafter referred to as the Unknown Tile Count, N UNKNOWN . 
     A following step  704  determines, for the current frame, an exposure metric value, EM. The exposure metric is intended to measure the extent to which the frame is suitably exposed, in other words, whether the brightness and contrast levels are suitable. This is achieved by calculating the entropy of the image luminance values. The step calculates a luminance histogram f Y (y) for the current frame, and uses f Y (y) to set the exposure metric value to be a measure of the entropy of the luminance of the current frame. In regard to (a), a luminance histogram counts the number of pixels with each luminance value. The pixel luminance values are typically 8-bit integers, i.e., integers in the range from 0 to 255. Thus, the luminance histogram f Y (y) is defined for y=0, 1, . . . , 255 as follows:
 
f Y (y)=the number of pixels whose luminance value is y. [18]
 
     In regard to (b), an entropy measure of the luminance channel can be calculated by: summing the values of
 
f Y (y) log(f Y (y))  [18A]
 
for each y=0, 1, . . . , 255; dividing this number by the number of pixels in the frame, n Y  say; subtracting the result from log(n Y ); and diving by log  256 . The exposure metric, EM, is set to the result of this calculation. Thus,
 
     
       
         
           
             
               
                 
                   EM 
                   = 
                   
                     
                       1 
                       
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         256 
                       
                     
                     ⁢ 
                     
                       ( 
                       
                         
                           log 
                           ⁡ 
                           
                             ( 
                             
                               n 
                               Y 
                             
                             ) 
                           
                         
                         - 
                         
                           
                             1 
                             
                               n 
                               Y 
                             
                           
                           ⁢ 
                           
                             
                               ∑ 
                               
                                 y 
                                 = 
                                 0 
                               
                               255 
                             
                             ⁢ 
                             
                               
                                 
                                   f 
                                   Y 
                                 
                                 ⁡ 
                                 
                                   ( 
                                   y 
                                   ) 
                                 
                               
                               ⁢ 
                               
                                 log 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     
                                       f 
                                       Y 
                                     
                                     ⁡ 
                                     
                                       ( 
                                       y 
                                       ) 
                                     
                                   
                                   ) 
                                 
                               
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   [ 
                   19 
                   ] 
                 
               
             
           
         
       
     
     It can be shown that the value of EM will always lie between 0 and 1. 
     Many other exposure metrics are known in the art, and could be used here. For example, the exposure metric EM could be calculated as the deviation from a uniform histogram, thus: 
                   EM   =       ∑     y   =   0     255     ⁢              f   Y     ⁡     (   y   )       -     f   av                      [     19   ⁢   A     ]               
where f av  is the average frequency n Y /256.
 
     A following step  706  classifies the current frame as either “acceptable” or “unacceptable”. There are three criteria that must be met if the frame is to be considered “acceptable”. If the frame does not meet all of the criteria, then it is classified “unacceptable”. The criteria are as follows. 
     First, the ratio of the number of “unknown” tiles to the total number of tiles must be less than a predetermined threshold T UNKNOWN , i.e., 
     
       
         
           
             
               
                 
                   
                     
                       N 
                       UNKNOWN 
                     
                     
                       
                         N 
                         UNKNOWN 
                       
                       + 
                       
                         N 
                         GOOD 
                       
                       + 
                       
                         N 
                         BAD 
                       
                     
                   
                   &lt; 
                   
                     T 
                     UNKNOWN 
                   
                 
               
               
                 
                   [ 
                   
                     19 
                     ⁢ 
                     B 
                   
                   ] 
                 
               
             
           
         
       
     
     Second, the ratio of the number of “bad” tiles to the number of known tiles (i.e., tiles that are not “unknown”) must be less than a predetermined threshold T BAD , i.e., 
     
       
         
           
             
               
                 
                   
                     
                       N 
                       BAD 
                     
                     
                       
                         N 
                         GOOD 
                       
                       + 
                       
                         N 
                         BAD 
                       
                     
                   
                   &lt; 
                   
                     T 
                     BAD 
                   
                 
               
               
                 
                   [ 
                   
                     19 
                     ⁢ 
                     C 
                   
                   ] 
                 
               
             
           
         
       
     
     Third, the exposure metric, EM, must exceed a predetermined threshold T EM , i.e.,
 
EM&gt;T EM   [19D]
 
     If all three of the criteria are satisfied, the frame is classified as “acceptable”. Conversely, if any of the three criteria are unsatisfied, the frame is classified “unacceptable”. 
     A following step  708  calculates a quality metric, QM, for the current frame. The quality metric consists of three parts, first a exposure metric, EM, calculated in step  704 , second a motion metric, MM, described below, and third a focus metric, FM, also described below. 
     As a motion metric for the frame, MM, the step  708  calculates the ratio of the number of “good” tiles, N GOOD , to the number of known tiles (i.e., the number of tiles that are not “unknown”). Thus, 
                   MM   =       N   GOOD         N   GOOD     +     N   BAD                 [   20   ]               
The value of MM will always lie between 0 and 1.
 
     As a focus metric for the frame, FM, the step  708  calculates the average of the focus magnitudes, F MAG , of the “good” tiles. Thus, 
                   FM   =       1     N   GOOD       ⁢       ∑     i   =   1       N   GOOD       ⁢     X   i                 [   21   ]               
where the X i  are the focus magnitudes, F MAG , of each of the “good” tiles. Note that FM will always have a value between 0 and 1.
 
     After the step  708  has calculated the values of MM and FM, the step  708  calculates the quality metric, QM, for the current frame, which is a weighted average of the exposure metric, the motion metric, and the focus metric. Thus,
 
 QM=w   EM   EM+w   MM   MM+w   FM   FM   [22]
 
where W EM , w MM , and W FM  are positive weights whose sum is 1. Typically, W EM , w MM  and w FM  will all equal ⅓. The value of QM will always lie between 0 and 1.
 
     Returning to  FIG. 10 , following completion of the step  708 , the process  701  terminates at a step  710 . 
     Returning to  FIG. 4 , the image quality data  130  that is output from the quality analysis module  120  has, for each frame, a classification of either “acceptable” or “unacceptable”, and also a quality metric value, QM, between 0.0 and 1.0. A value of 0.0 represents a frame with the lowest or worst quality metric value QM or rating. A value of 1.0 represents a frame with the highest or best quality metric value QM or rating. 
     In an alternative arrangement, sub-regions of some or all the video frames are analyzed for “good” quality. In relation to operations requiring a comparison with data from previous frames, this approach involves comparing a current sub-region of a current frame with a corresponding sub-region of a previous frame. 
     For a given frame, the sub-region that contains the “best” quality rating (QR) R  is selected as a “good” quality region of the particular image. Preferably, the sub-regions are rectangular and are an integer number of tiles in height and width, and each image sub-region tile corresponds with a video frame image tile. Defining the “best” quality rating for a sub-region typically requires balancing the quality metric for that sub-region (QM) R  against the size of the selected sub-region (S) R . This can be expressed, in one example as follows:
 
( QR ) R   =W   QMR ×( QM ) R   +W   SR ×( S ) R   [23]
 
where W QMR  and W SR  are weighting factors establishing the relative importance of the sub-region quality and the sub-region size. The value of (QR) R  is then maximized for different sub-region rectangle sizes and positions within the corresponding video frame image. For example, the maximum value of (QR) R  could be found by exhaustive search.
 
     In an alternative arrangement, the method of analyzing the quality of a frame from the video frame image data  110  not only utilizes the data  124  generated by the motion and focus analysis unit  122  of the preferred arrangement, but also utilizes utilizes additional information relating to the spatial distribution of motion and/or focus defined tiles in each frame. The alternative arrangement operates in substantially the same manner as the arrangement described in relation to  FIG. 4 , differing only in the method for calculating the image quality data  130  in the image quality analysis unit  126 . 
       FIG. 11  shows an alternative flow diagram of a method  801  for calculating image quality data of a tiled frame in the image quality analysis unit  126  in  FIG. 5 .  FIG. 11  shows the method  801  which is an alternate method to the method  701  in  FIG. 10 . The method  801  begins at a step  800 . A next step  802  determines, across the current video frame, the spatial distribution of tiles that have been classified as having “defined” motion and focus. In this manner, an array of classification values indicating the “defined” status of each tile, together with a parameter indicating the relative location of that tile within the image, is obtained for each frame of the video frame image data  110 . At a next step  804 , the respective motion and focus magnitude values M MAG  and F MAG  of each tile are incorporated into the array. The array is then buffered for subsequent processing. The aforementioned array process is performed for each frame of the video frame image data  110 . 
     A following step  806  determines the motion metric MM, the focus metric FM, and the exposure metric EM, for each frame. In this arrangement, a user may specify a sub-region of the frame as being especially significant. For example, the user may specify the top-left quarter of the video frame. The motion, focus, and exposure metrics for the current frame are modified so that in Equations [20], [21], and [19], only the tiles that lie inside the user-specified sub-region are considered. The frame is then classified as acceptable or unacceptable, and a quality metric is calculated as described before. The process  801  then proceeds to a terminating step  808 . 
     In another arrangement, in calculating the focus metric FM for the current frame, the Equation [21] is modified so that the focus magnitude F MAG  of each tile is weighted depending on the distance of the tile in question to the centre of the current frame. For instance, a tile at the centre of the frame can have a focus magnitude weighting of 1.0, whereas a tile on the border of the frame can have a weighting of 0.0, with all tiles located between the border and centre of the frame being given an appropriate weighting value for the focus magnitude. 
     In another arrangement, an alternative image quality metric QM is calculated by the step  806 . The Equation [22] is modified so that the quality metric value QM is defined as a function of (a) the distribution of the defined tiles and (b) the distribution and value of the associated motion and focus magnitude values. One definition of the quality metric value QM for this alternate arrangement is to assign a weighting to the respective motion and focus metric values MM and FM, and also to include the dependence of the spatial distribution of the defined tiles within the image, as follows:
 
 QM=W   S ×( W   M ×(1.0 −MM )+ W   F   ×FM ).  [24]
 
where, W S  is a weighting value derived from the spatial distribution of the defined tiles, and the other variables have their previous definitions. Thus, for instance, the weighting value W s  can be defined to indicate the amount of clustering that occurs between defined motion and/or focus tiles within the video frame image. If there is a large number of defined tiles grouped into a sub-region of the frame, and the remaining sub-regions of the frame contains only undefined tiles, then the weighting W s , and hence the quality metric, can be assigned a higher value.
 
     In another alternative arrangement, each tile of a frame is assigned an “exposure” attribute. This attribute is assigned to a tile if that tile is undefined AND the mid-range luminance value MRV (see Equations [11] and [12]) of the tile falls within a predefined range of values. Over-exposed tiles typically exhibit a significant amount of saturated colour, and as a result, many pixel luminance values are close to white (a value of 255 for an 8-bit sample). Under-exposed tiles typically exhibit a significant amount of darkness, and as a result, many pixel luminance values close to black (a value of 0 for an 8-bit sample). In this arrangement, the quality metric value QM of each frame is dependent on the “exposure” attribute, along with the motion and focus metric and defined tile values. 
     In yet another alternative arrangement using the “exposure” attribute, the exposure attribute can be calculated based on statistics of the histogram values of some or all of the tiles in the frame. Thus, for example, corresponding bins of each tile histogram of a frame can be summed, and the resultant histogram analysed to ensure that there is a “significant” amount of data present in each bin. Too much data present in too few histogram bins would indicate a poor dynamic range of colour within the video frame. The exposure attribute need not be limited to calculations of luminance values only, but could be performed on other colour channels as well as, or in place of, the luminance component. 
     Suitable values for the aforementioned parameters are:
     W T =16   H T =16   T N =5   T M =20   T H =235   T W =20   R T =16   N b =16   MB T =0.3   FB T =0.55   T EM =4.4   T UNKNOWN =0.61   T BAD =0.78   

     INDUSTRIAL APPLICABILITY 
     It is apparent from the above that the arrangements described are applicable to the video processing industry. 
     The foregoing describes only some embodiments of the present invention, and modifications and/or changes can be made thereto without departing from the scope and spirit of the invention, the embodiments being illustrative and not restrictive.