Patent Publication Number: US-7916791-B2

Title: Method and system for non-linear motion estimation

Description:
The invention described herein was made in the performance of work under NASA Contract No. NNS05AA75C and is subject to the provisions of Section 305 of the National Aeronautics and Space Act of 1958 (72 U.S.C. 2457). 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates generally to a method and system for non-linear motion estimation. More specifically, the present invention employs a non-linear motion estimation process for improved side information extrapolation and interpolation in Wyner-Ziv video decoding systems. 
     2. Description of the Related Art 
     Extrapolation and interpolation of a visual signal, such as, image, video, and graphic signals, have been widely used in various contexts, including, but not limited to: video-coding, trans-coding, error concealment, frame rate conversion, pre-processing, and interactive rendering. 
     For instance, techniques for extrapolating and interpolating in video-coding applications have been described by Aaron et al., in  Toward Practical Wyner - Ziv Coding of Video , P ROC . IEEE I NT . C ONF ON  I MAGE  P ROCESSING , pp. 869-872, Barcelona, Spain, Spet. (2003), Puri et al.,  PRISM: A New Robust Video Coding Architecture based on Distributed Compression Principles , A LLERTON  C ONFERENCE ON  C OMMUNICATION , C ONTROL AND  C OMPUTING , (2002), and Yaman et al.,  A Low - Complexity Video Encoder with Decoder Motion Estimation , Proc. ICASSP, Montreal, Canada, (2004). 
     Techniques for extrapolating and interpolating in transcoding applications have been described by U.S. Pat. No. 6,058,143 issued on May 2, 2000, to Golin for “Motion Vector Extrapolation for Transcoding Video Sequences.” 
     Further, techniques for extrapolating and interpolating in error concealment for video decoding or post-processing applications have been described by Peng et al.,  Block - Based Temporal Error Concealment for Video Packet Using Motion Vector Extrapolation , International Conf. on Communications, Circuits, Systems and West Sino Expo, pp. 10-14, Jun. 29-Jul. 1, (2002) and by U.S. Pat. No. 6,285,715 issued on Sep. 4, 2001, to Ozcelik for “Methods and Apparatus for Error Concealment While Decoding a Coded Video Bit Stream.” 
     The visual signal extrapolation and interpolation methods that are conventionally used in video coding, trans-coding, error concealment, video decoding, frame rate conversion, and post-processing applications are based on an assumption that motion is linear. Therefore, these methods are referred to as linear motion-based extrapolation and interpolation methods. 
     One example that employs a linear motion-based extrapolation/interpolation method is the Wyner-Ziv video coding technique. A typical Wyner-Ziv video coding system includes a video encoder and a video decoder. The video encoder is a low complexity and low power encoder. The computation-heavy signal processing tasks, such as the motion estimation, are performed at the decoder. 
     In order to decode the received video signals and reconstruct the video, a Wyner-Ziv decoder needs to exploit a correlation between source information and side information, which is only available at the decoder. The source information is the video signal (e.g., a picture) to be encoded by the encoder and transmitted to the decoder for decoding, and the side information is an estimate of the picture to be decoded. The side information is generated at the decoder. 
     The performance of a Wyner-Ziv coding system depends heavily on the fidelity and reliability of the side information. The closer the side information to the source, the better the performance of the system. Therefore, the method and apparatus used by the decoder to generate the side information plays a very crucial role in a Wyner-Ziv video coding system. 
     Typically, the decoder first performs motion estimation on previously reconstructed pictures (termed reference pictures) to generate a set of motion vectors and then uses these motion vectors to generate an estimate of the picture to be decoded by motion based extrapolation or interpolation. This estimate is used as the side information by the decoder for decoding and reconstructing the current picture. 
       FIG. 1  is a diagram illustrating a conventional linear motion-based temporal extrapolation process  100 . Specifically, in order to extrapolate a current Picture N  106 , motion estimation is first performed on at least two reference pictures, namely, Pictures N−2  102  and N−1  104 , to generate a motion vector  108  for each pixel or a block of pixels in Picture N−1  104 , which are indicative of the motion of the pixel or the block of pixels between Picture N−1  104  and Picture N−2  102 . Then, the motion vectors  108  are manipulated according to a predetermined function that is established upon an underlying motion model or assumption. For example, if a constant linear displacement motion model is assumed, the motion vector  108  is shifted, and the pixel or the block of pixels associated with the motion vector  108  is extrapolated (i.e., mapped) from its location in Picture N−1  104  to a location defined by the motion vectors in an estimate of the current Picture N  106 . 
     Note that a motion vector  108  can also be constructed for each pixel or a block of pixels in Picture N−2  102  to indicate the motion between Picture N−2  102  and Picture N−1  104 . In such an incident, the motion vector  108  should then be shifted, and the pixel or the block of pixels associated with the motion vector  108  should be extrapolated or mapped from its location in Picture N−1  104  to a location defined by the shifted motion vectors in an estimate of the current Picture N  106 . 
     The linear motion-based temporal extrapolation process as described above, therefore, creates an estimate of the current Picture N  106 , after all the pixels or the blocks of pixels in Picture N−1  104  (or Picture N−2  102 ) are mapped. 
       FIG. 2  illustrates another conventional linear motion-based temporal interpolation process  200 . Motion estimation is first performed on at least two reference pictures, namely, Pictures N−1  202  and N+1  206 , to obtain a motion vector  208  for each pixel or a block of pixels in Picture N−1  202 , which is indicative of the motion of the pixel or the block of pixels from Picture N−1  202  to Picture N+1  206 . Then, the motion vector is scaled down (e.g., by a factor of 2) based on an underlying assumption for a constant linear displacement motion model, and the pixels or the blocks of pixels associated with the motion vectors are interpolated from their locations in Picture N−1  202  and/or N+1  206  to a location defined by the scaled motion vector in an estimate of the current Picture N  204 . 
     Note that a motion vector  208  can also be constructed for each pixel or block of pixels in Picture N+1  206  to indicate the motion between Picture N+1  206  and Picture N−1  202 . In such an incident, the motion vector  208  should also be scaled down (e.g., by a factor of 2), and the pixels or the blocks of pixels associated with the motion vector  208  should be interpolated from their locations in Picture N−1  202  and/or Picture N+1  206  to a location defined by the scaled motion vectors in an estimate of the current Picture N  204 . The linear motion-based temporal interpolation process as described above also creates an estimate of the current Picture N  204 , after all the pixels or the blocks of pixels in Picture N+1  206  (or Picture N−1  202 ) are mapped. 
       FIG. 3  illustrates a conventional linear-motion based temporal video frame (i.e., picture) extrapolation/interpolation system  300 . The system  300  includes a linear motion estimation unit  302  and a motion-based extrapolation/interpolation unit  304 . The linear motion estimation unit  302  receives the reference pictures and generates a motion vector based on the linear motion assumption or model. The extrapolation/interpolation unit  304  receives the motion vector from the linear motion estimation unit  302  and the reference pictures and outputs an estimated picture. 
     The above-described conventional linear motion-based extrapolation and interpolation methods have a serious drawback: the underlying assumption that the objects follow a linear motion model from picture to picture often does not hold true for real visual signals. 
     Further, conventional motion-based extrapolation and interpolation systems do not have a one-to-one mapping property. As a result, there may be empty holes and/or superimposed positions in the estimated picture. 
     Another problem with these conventional motion-based extrapolation and interpolation systems is that the intensity of an object may vary from picture-to-picture due to lighting changes. As a result, estimated pictures may have inaccurate pixel values. 
     It is, therefore, desirable to provide an improved method and apparatus of motion estimation for visual signal extrapolation and interpolation, without the drawback of the conventional linear motion model based extrapolation and interpolation methods. 
     SUMMARY OF THE INVENTION 
     In view of the foregoing and other exemplary problems, drawbacks, and disadvantages of the conventional methods and systems, an exemplary feature of the present invention is to provide a method and system in which side information is generated using an adaptive non-linear motion model. 
     In a first exemplary aspect of the present invention, a method for extrapolating and interpolating a visual signal includes determining a first motion vector between a first pixel position in a first image to a second pixel position in a second image, determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image, determining a third motion vector between one of the first pixel position in the first image and the second pixel position in the second image, and the second pixel position in the second image and the third pixel position in the third image using a non-linear model, and determining a position of the fourth pixel in a fourth image based upon the third motion vector. 
     In a second exemplary aspect of the present invention a system for extrapolating and interpolating a visual signal, includes means for determining a first motion vector between a first pixel position in a first image to a second pixel position in a second image, means for determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image, means for determining a third motion vector between one of the first pixel position in the first image and the second pixel position in the second image, and the second pixel position in the second image and the third pixel position in the third image using a non-linear model, and means for determining a position of the fourth pixel in a fourth image based upon the third motion vector. 
     In a third exemplary aspect of the present invention a program embodied in a computer readable medium executable by a digital processing unit, includes instructions for determining a first motion vector between a first pixel position in a first image to a second pixel position in a second image, instructions for determining a second motion vector between the second pixel position in the second image and a third pixel position in a third image, instructions for determining a third motion vector between one of the first pixel position in the first image and the second pixel position in the second image, and the second pixel position in the second image and the third pixel position in the third image using a non-linear model, and instructions for determining a position of the fourth pixel in a fourth image based upon the third motion vector. 
     Other aspects, features and advantages of the invention will be more fully apparent from the ensuing disclosure and appended claims. 
     These and many other advantages may be achieved with the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other exemplary purposes, aspects and advantages will be better understood from the following detailed description of an exemplary embodiment of the invention with reference to the drawings, in which: 
         FIG. 1  illustrates a linear motion based temporal extrapolation method  100 ; 
         FIG. 2  illustrates a linear motion based temporal interpolation method  200 ; 
         FIG. 3  illustrates a linear motion based temporal extrapolation or interpolation system  300 ; 
         FIG. 4  illustrates an exemplary non-linear motion estimation based visual signal extrapolation or interpolation system  400  in accordance with the present invention; 
         FIG. 5  is a diagram describing the exemplary non-linear motion estimation system  402  of  FIG. 4 ; 
         FIG. 6  illustrates an exemplary application of the present invention; 
         FIG. 7  illustrates an exemplary hardware/information handling system  700  for incorporating the present invention therein; 
         FIG. 8  illustrates a signal bearing medium  800  (e.g., storage medium) for embodying a program that is executable by a digital processing unit according to the present invention; and 
         FIG. 9  is a flowchart illustrating one exemplary method  900  in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS OF THE INVENTION 
     Referring now to the drawings, and more particularly to  FIGS. 4-9 , there are shown exemplary embodiments of the methods and systems of the present invention. 
     The present invention provides improved methods and systems for extrapolation and interpolation using non-linear motion based estimation. 
     An exemplary embodiment of the present invention performs motion estimation on picture signals obtained from previously reconstructed pictures, i.e., reference pictures (or portions of pictures), to generate a set of motion vectors, which are then used to generate an estimate picture by either extrapolation, interpolation, or both from one or more of the reference pictures. 
       FIG. 4  illustrates an exemplary non-linear motion estimation-based visual signal extrapolation or interpolation system  400  in accordance with the present invention. The system  400  includes a non-linear motion estimation unit  402  and an extrapolation/interpolation unit  404 . The non-linear motion estimation unit  402  receives the reference pictures (or portions of pictures) and generates a set of motion vectors MVs based upon a non-linear model. The extrapolation/interpolation unit  404  receives the motion vectors MVs from the non-linear motion estimation unit  402  and the reference pictures and outputs an estimated picture N (or portions of a picture). 
       FIG. 5  illustrates components of the non-linear motion estimation unit  402  of  FIG. 4 . The non-linear motion estimation unit  402  includes a motion estimation unit  406  and a motion vector filtering unit  408 . These components  406  and  408  perform the methods, which are described in detail below. 
       FIG. 6  illustrates an application  600  of an exemplary embodiment of the present invention which uses non-linear motion estimation to generate motion vectors for constructing an estimate picture by extrapolation or interpolation. 
     First, a plurality of (e.g., preferably at least three) previously decoded and reconstructed pictures, which are referred to hereinafter as the reference pictures, are obtained and stored by the decoder. These three reference pictures are referred to as N+1  608 , N−1  604  and N−2  602 . 
     For each block of pixels  610  in the reference picture N−1  604 , a search process is performed to find its best match in another of the reference pictures such as, for example, picture N−2  602  (or picture N+1  608 ). 
     In order to find the best matching block B*  612  in the reference picture N−2  602  for a specific block Bi  610  in the reference picture N−1  604 , a search process picks a same size block of pixels, Bp  612  from the reference picture N−2  602  and computes a distortion measure which is indicative of an error E, which, for example, can be the sum of the absolute or squared differences in pixel values, the similarity of a set of statistical features, or a combination of them between Bi  610  and Bp  612 . The error E is then used to determine the best matching block B*  612  in the reference picture N−2  602  by minimizing the error E in the search process. 
     Once the best matching block B*  612  in the reference picture N−2  602  is determined, a set of motion vectors can be generated for the block Bi  610  in the reference picture N−1  204 , which are indicative of the movement of block Bi  610  in relation to B*  612 . The motion vectors can be generated from various parameters associated with blocks Bi  610  and B*  612 . 
     In an exemplary embodiment, they are generated by taking the spatial differences (i.e., the horizontal and vertical coordinates) of blocks Bi  610  and B*  612 . 
     The motion vectors are then filtered to reflect the non-linearity of the real motion. The output of the filtering process is a motion vector for extrapolating or interpolating a location in the picture to be decoded and reconstructed, which is referred to hereinafter as the “estimated picture” N  606 , where the estimate of the block Bi resides. The pixel values of the estimate block are derived from the pixel values of blocks Bi  610  and B*  612 , for example, by averaging the pixel values of these blocks or by temporally filtering pixel values. 
     The above-described process is repeated for each block of pixels in the reference picture N−1  604 , so that the estimate of each block of pixels in the reference picture N−1  604  is mapped, thereby forming an estimated picture N  606 . 
     Various computations as described above can be readily performed by a computer-based visual signal analyzer, which may include a general-purpose computer, a specific-purpose computer, a central processor unit (CPU), a microprocessor, or an integrated circuit that is arranged and constructed to collect and process visual signal data. 
     Such a visual signal analyzer may use a visual signal extrapolation or interpolation protocol for performing the above-described visual signal extrapolation or interpolation to generate estimated pictures, according to an exemplary embodiment of the present invention. 
     The visual signal extrapolation or interpolation protocol can be embodied in any suitable form, such as software operable in a general-purpose computer, a specific-purpose computer, or a central processor unit (CPU). Alternatively, the protocol may be hard-wired in circuitry of a microelectronic computational module, embodied as firmware, or available on-line as an operational applet at an Internet site for phase analysis. 
     An exemplary embodiment of the present invention may use a quadratic model to describe the motion trajectory rather than a conventional linear motion model. 
     A linear motion model is described by:
 
 y   t   =ax   t   +b   (1)
 
where the coordinates y and x are functions of time and the temporal variable t represents the time instances when a frame sample is taken. The derivative of y t  with respect to x t  is:
 
                       ⅆ     y   t         ⅆ     x   t         =   a           (   2   )               
where a is a constant.
 
     In accordance with an exemplary embodiment of the present invention a non-linear motion estimation is used to describe the temporal motion trajectory. For example a quadratic model may be used. A quadratic model may be described by:
 
 y   t   =ax   t   2   +bx   t   +c.   (3)
 
The coefficients a, b, and c may be estimated through pixel point data. For example, through the motion estimation method described above, the points (x t , y t ) are (x N−2 , y N−2 ), (x N−1 , y N−1 ), and (x N+1 , t N+1 ) at the time instants N−2, N−1, and N+1, respectively, can be found. These points reside on Pictures N−2, N−1, and N+1 and form a motion trajectory described by Equation (3). Then, from Equation (3), are the followings equations:
 
 y   N−2   =ax   N−2   2   +bx   N−2   +c;   (4)
 
 y   N−1   =ax   N−1   2   +bx   N−1   +c;  and  (5)
 
 y   N+1   =ax   N+1   2   +bx   N+1   +c.   (6)
 
Subtracting (5) or (6) from (4), provides, respectively:
 
 y   N−2   −y   N−1   =a ( x   N−2   2   −x   N−1   2 )+ b ( x   N−2   −x   N−1 ), and  (7)
 
 y   N−2   −y   N+1   =a ( x   N−2   2   −x   N+1   2 )+ b ( x   N−2   −x   N−1 ).  (8)
 
Solving (7) and (8) for a and b, provides:
 
     
       
         
           
             
               
                 
                   
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     Once a and b are determined, the parameter c can be solved using, for example Equation (4):
 
 c=ax   N−2   2   +bx   N−2   −y   N−2 .  (11)
 
The coordinates (x,y) in the estimated picture N where the motion trajectory passes at the time instant N can be determined as follows.
 
     Taking the derivative of y with respect to x, results in: 
     
       
         
           
             
               
                 
                   
                     
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     Using the known corresponding points in Picture N−1 and Picture N+1:
 
 y   N   −y   N−1 =(2 ax   N   +b )( x   N   −x   N−1 ),  (13)
 
 y   N+1   −y   N =(2 ax   N   +b )( x   N+1   −x   N ),  (14)
 
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                           ⁢         x   N     =         y     N   +   1       -     y     N   -   1       +     b   ⁡     (       x     N   -   1       -     x     N   +   1         )           2   ⁢     a   ⁡     (       x     N   +   1       -     x     N   -   1         )             ,                               y   N     =         [       y     N   +   1       -     y     N   -   1       +       b   (     (       x     N   -   1       -     x     N   +   1         )     ]     2           2   ⁢       a   ⁡     (       x     N   +   1       -     x     N   -   1         )       2         +         (     b   -     2   ⁢     ax     N   +   1           )     ⁡     [       y     N   +   1       -     y     N   -   1       +     b   ⁡     (       x     N   -   1       -     x     N   +   1         )         ]         2   ⁢     a   ⁡     (       x     N   +   1       -     x     N   -   1         )           +     y     N   +   1       -     bx     N   +   1.                 (   15   )               
The motion vectors MV 1 =(mvx 1 , mvy 1 ) and MV 2 =(mvx 2 , mvy 2 ) in  FIG. 5  can be calculated, respectively, by
 
 mvx   1   =x   N−1   −x   N−2   , mvy   1   =y   N−1   −y   N−2 ; and  (16)
 
 mvx   2   =x   N+1   −x   N−1   , mvy   2   =y   N+1   −y   N−1 .  (17)
 
Substitute these values into Equation (15), provides
 
     
       
         
           
             
               
                 
                   
                       
                   
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     In this manner, an exemplary embodiment of the invention may determine the motion vector MV or the coordinates (x N , y N ) of the point in Picture N in accordance with the present invention. 
     While the invention has been described in terms of an exemplary embodiment, those skilled in the art will recognize that the invention can be readily extended to estimate motions with other non-linear models. 
     Referring now to  FIG. 7 , system  700  illustrates a typical hardware configuration that may be used for implementing an exemplary embodiment of the present invention. The configuration may have preferably at least one processor or central processing unit (CPU)  710 . The CPUs  702  are interconnected via a system bus  712  to a random access memory (RAM)  714 , read-only memory (ROM)  716 , input/output (I/O) adapter  718  (for connecting peripheral devices such as disk units  721  and tape drives  740  to the bus  712 ), user interface adapter  722  (for connecting a keyboard  724 , mouse  726 , speaker  728 , microphone  732 , and/or other user interface device to the bus  712 ), a communication adapter  734  for connecting an information handling system to a data processing network, the Internet, and Intranet, a personal area network (PAN), etc., and a display adapter  736  for connecting the bus  712  to a display device  738  and/or printer  739 . Further, an automated reader/scanner  741  may be included. Such readers/scanners are commercially available from many sources. 
     In addition to the system described above, a different aspect of the invention includes a computer-implemented method for performing the above method. As an example, this method may be implemented in the particular environment discussed above. 
     Such a method may be implemented, for example, by operating a computer, as embodied by a digital data processing apparatus, to execute a sequence of machine-readable instructions. These instructions may reside in various types of signal-bearing media. 
     Thus, this aspect of the present invention is directed to a programmed product, including a program embodied in a computer readable medium executable by a digital processor. Such a method may be implemented, for example, by operating the CPU  710  to execute a sequence of machine-readable instructions. These instructions may reside in various types of signal bearing media. Thus, this aspect of the present invention is directed to a program embodied in a computer readable medium executable by a digital processor incorporating the CPU  710  and hardware above, to perform a method in accordance with the present invention. 
     This signal-bearing media may include, for example, a RAM (not shown) contained within the CPU  710 , as represented by the fast-access storage for example. 
     Alternatively, the instructions may be contained in another signal-bearing media, such as a magnetic data storage diskette  800 , CD-ROM  802 , or the like as illustrated by  FIG. 8 . These instructions may be directly or indirectly accessible by the CPU  710 . 
     Whether contained in the computer server/CPU  710 , or elsewhere, the instructions may be stored on a variety of machine-readable data storage media, such as DASD storage (e.g., a conventional “hard drive” or a RAID array), magnetic tape, electronic read-only memory (e.g., ROM, EPROM, or EEPROM), an optical storage device (e.g., CD-ROM, WORM, DVD, digital optical tape, etc.), paper “punch” cards, or other suitable signal-bearing media. In an illustrative embodiment of the invention, the machine-readable instructions may comprise software object code, complied from a language such as “C,” etc. 
       FIG. 9  illustrates a flowchart for a method  900  in accordance with an exemplary embodiment of the invention. The flowchart starts at step  902  and continues to step  904  where a first motion vector between a first pixel position in a first image to a second pixel position in a second image is determined. The flowchart continues to step  904  where a second motion vector between the second pixel position in the second image and a third pixel position in a third image is determined. The flowchart continues to step  908  where a third motion vector between one of the first pixel position in the first image and the second pixel position in the second image, and the second pixel position in the second image and the third pixel position in the third image using a non-linear model is determined. The flowchart continues to step  920  where a position of the fourth pixel in a fourth image based upon the third motion vector is determined. 
     Although the invention has been variously disclosed herein with reference to illustrative embodiments and features, it will be appreciated that the embodiments and features described above are not intended to limit the invention, and that other variations, modifications and alternative embodiments will readily suggest themselves to those of ordinary skill in the art. The invention therefore is to be broadly construed, as including such variations, modifications and alternative embodiments. 
     While the invention has been described in terms of several exemplary embodiments, those skilled in the art will recognize that the invention can be practiced with modification. 
     For example, an exemplary embodiment of this invention can also be advantageously applied to other cases that may involve temporal extrapolation and interpolation, such as video frame rate up-conversion, error concealment, visual signal rendering, etc 
     It is noted that “images” used herein cam be a portion of an image. 
     Further, it is noted that, Applicant&#39;s intent is to encompass equivalents of all claim elements, even if amended later during prosecution.