PATENT DOCUMENT

Abstract:
In a motion compensated prediction process, a parametric interpolation filter (PIF) device is provided that takes into account the time-variant statistics of video sources, the filter being represented by a model determined by five parameters instead of by individual coefficients. The parameters are calculated and coded on a frame-by-frame basis to minimize the energy of the prediction error for each frame. The model design is based on the fact that high frequency energy of an HD video source is mainly distributed along the vertical and horizontal directions of a frame. A PIF device with the method according to the invention overcomes this obstacle because it represents each filter using only five parameters, all of which are encoded using sufficiently high precision without substantially increasing overhead.

Full Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     NOT APPLICABLE 
     STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT 
     NOT APPLICABLE 
     REFERENCE TO A “SEQUENCE LISTING,” A TABLE, OR A COMPUTER PROGRAM LISTING APPENDIX SUBMITTED ON A COMPACT DISK 
     NOT APPLICABLE 
     BACKGROUND OF THE INVENTION 
     This invention relates to digital video and more particularly to coding efficiency of high definition (HD) video data. 
     Motion compensated prediction (MCP) is the key to the success of modern video coding standards. With MCP, the video signal to be coded is predicted from the temporally neighboring signals, and only the prediction error and the motion vector (MV) are transmitted. However, due to the finite sampling rate, the actual position of the prediction in the neighboring frames may be out of the sampling grid, where the intensity is unknown, so the intensities of the positions between the integer pixels, called sub-positions, must be interpolated and the resolution of the MV must be increased accordingly. In the existing video coding standards, the interpolation filter is designed to fit the general statistics of various video sources, so the filter coefficients are fixed. 
     In prior work, adaptive interpolation filter (AIF) techniques have been used wherein the coefficients are analytically calculated for each frame using a linear minimum mean squared error (LMMSE) estimator. However, AIF techniques code the filter coefficients individually, and therefore trade-offs between the accuracy of coefficients and the size of side information must be made. These two conflicting aspects are the major obstacles to improving the performance, as inaccurate coefficients greatly degrade the performance of the interpolation filter. 
     Due to higher data rates associated with HD video coding, there is a need to improve the coding efficiency of HD video. 
     SUMMARY OF THE INVENTION 
     According to the invention, in a motion compensated prediction process, a parametric interpolation filter (PIF) is provided that takes into account the time-variant statistics of video sources, the filter being represented by a model determined by multiple parameters instead of by individual coefficients and has a diamond shaped passband with peaks in the horizontal direction and the vertical direction such that high frequency components in the horizontal direction and in the vertical direction are not filtered out. The parameters are calculated and coded on a frame-by-frame basis to minimize the energy of the prediction error for each frame. The model design is based on the fact that high frequency energy of a high definition (HD) video source is mainly distributed along the vertical and horizontal directions of a frame. A PIF device operative with the underlying method according to the invention overcomes this obstacle because it represents each filter using only a limited number of parameters (five), all of which are encoded using sufficiently high precision without substantially increasing overhead. 
     Experimental results show that PIF techniques significantly outperform the known adaptive interpolation filter (AIF) techniques. While the invention is primarily suited to improve motion compensated prediction of HD video formats, other video formats may benefit as well. 
     The invention will be better understood by reference to the following detailed description in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of the interpolation process of an adaptive interpolation filter, including a parametric interpolation filter h, wherein the frame is interpolated to four times horizontal and four times vertical directions. 
         FIG. 2(   a ) is a three-dimensional graph of the frequency response of an ideal interpolation filter h of  FIG. 1 . 
         FIG. 2(   b ) is a three-dimensional graph of the frequency response of an ideal interpolation filter h f  of the type of  FIG. 1  according to the invention having a diamond-shaped passband in accordance with the invention. 
         FIG. 3  is a two-dimensional graphical representation of the passband of a parametric interpolation filter h f  according to the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A parametric interpolation filter (PIF) has three features. First, in order to keep track of the time-variant statistics, the interpolation filter is optimized for each frame such that the energy of prediction error for each frame is minimized. Second, as the name implies, PIF represents the interpolation filters by a function determined by parameters, in this instance five parameters. The parameters are transmitted in the frame header with very high precision, but the overhead size remains small. Third, the function is designed for HD video coding based on the fact that high frequency energies of HD videos are mainly distributed in the vertical and horizontal directions. Without the loss of generality, the following description assume ¼-pixel motion-compensated prediction (MCP) and each sub-position is supported by the surrounding 6×6 integer pixels. The case can be easily extended to other resolution MCP and other support regions. 
     A. Optimal AIF Design 
     It is helpful to understand the coding process in the context of adaptive interpolation filter (AIF) design to appreciate the invention. As indicated by  FIG. 1 , the interpolation process of the optimal filter  10  comprises two steps: upsampling  12 , which comprises upsampling the original reference frame to 16 times the size to increase spatial resolution of the motion vector and interpolated at 16 times size for motion compensated prediction (MCP) by inserting zero-valued samples in the half-pixel and quarter-pixel sampling grids. This, however, produces undesired spectra in the frequency domain, Therefore, the undesired spectra are removed by a low pass filter h  14 . As explained hereafter, filter h  14  may be a parametric interpolation filter h f  according to the invention. The optimal h, denoted as h opt , achieving the minimum prediction error energy, can be obtained by using a linear minimum mean square error (LMMSE) estimator. The size of h in  FIG. 1  is 23×23. As hereinafter explained, the parametric interpolation filter h f  according to the invention provides a good approximation of the optimal filter h opt , but with considerably fewer computations 
     1) Optimal AIF for P-Frames 
     Let P and S be the reference frame and the current frame, respectively. Upsampling P and S by a factor 16 using zero-insertion and zero-order holding, respectively, we get P 16  and S 16 . Note P 16  is where h in  FIG. 1  applies. Then the prediction error energy is given by (1): 
                     σ   e   2     =     E   ⁢     ⌊       (         ∑     i   -=   11     11     ⁢           ⁢       ∑     j   =     -   11       11     ⁢           ⁢       h   ⁡     (     i   ,   j     )       ⁢       P   16     ⁡     (       x   -   i   +     d   x       ,     y   -   j   +     d   y         )             -       S   16     ⁡     (     x   ,   y     )         )     2     ⌋               (   1   )               
where (x,y) indicates the spatial coordinate and (d x ,d y ) includes the two components of the MV, which has quarter-pixel resolution. Letting ∂σ e   2 /∂h(m,n) equal 0, one can derive the minimum σ e   2  and the optimal interpolation filter h opt ; the solution converges to the Wiener-Hopf equations as in (2),
 
                         ∑     i   =     -   11       11     ⁢           ⁢       ∑     j   =     -   11       11     ⁢           ⁢       h   ⁡     (     i   ,   j     )       ⁢       R   pp     ⁡     (       i   -   m     ,     j   -   n       )             =       R     p   ⁢           ⁢   s       ⁡     (     m   ,   n     )         ,     
     ⁢     ∀   m     ,   n   ,           ⁢       -   11     ≤   m     ,           ⁢     n   ≤   11             (   2   )               
where R pp  and R ps  represent the autocorrelation of P 16  and the motion-compensated cross-correlation of P 16  and S 16 , respectively. R pp  and R ps  are calculated with all the MVs for the current frame known; therefore, motion estimation (ME) is performed before starting coding the current frame. Since the LMMSE estimator is commonly used in statistics and signal processing, the detailed reasoning steps are skipped here.
 
     2) Optimal AIF for B-Frames 
     For B-frames, forward, backward, and bi-directional MCPs are allowed, where the former two can follow the above solution to derive the optimal AIF. For bi-directional MCP, the solution is modified. Denoting P 16,f  and (d x,f ,d y,f ) as the upsampled reference frame and MV for forward MCP, respectively, and P 16,b  and (d x,b ,d y,b ) for the backward case, one should re-write (1) to (3). 
     
       
         
           
             
               
                 
                   
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                     e 
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                         2 
                       
                       ⌋ 
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     Similarly, letting ∂σ e   2 /∂h(m,n) equal 0, one finally derives h opt  by (4) and obtains the minimum σ e   2 . 
     
       
         
           
             
               
                 
                   
                     
                       
                         0 
                         = 
                           
                         ⁢ 
                         
                           
                             
                               ∂ 
                               
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                           = 
                             
                           ⁢ 
                           
                             
                               
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                             - 
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                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     In (4), R ff  and R bb  represent the autocorrelations of the forward and backward upsampled reference frames, P 16,f  and P 16,b , respectively, and R fb , R fs , and R bs  are the motion-compensated cross-correlations of P 16,f  and P 16,b , P 16,f  and S 16 , and P 16,b  and S 16 , respectively. 
     B. Approximation Effects 
     If no symmetry constraint and quantization are imposed, the optimal AIF has 23 2  different real-valued coefficients. This number of coefficients makes it too expensive to be coded for each frame. The prior art teaches of an effort to reduce the side information representing AIF, including restricting the support region, imposing the symmetry constraints, and quantizing the coefficients, which lead to an approximation of h opt , denoted as {tilde over (h)}, and larger prediction error compared with that produced by h opt . The difference between h opt  and {tilde over (h)} is denoted as h Δ , equal to h opt −{tilde over (h)}, and the increased energy of prediction error introduced by h 66 , is given in (5). 
                           Δ   ⁢           ⁢   err     =       σ   e   2     ⁢              h   ~       ⁢     -     σ   e   2              h   opt                     =       ∑     i   ,   j       ⁢           ⁢       ∑     m   ,   n       ⁢         h   Δ     ⁡     (     i   ,   j     )       ⁢       h   Δ     ⁡     (     m   ,   n     )       ⁢       R   pp     ⁡     (       i   -   m     ,     j   -   n       )                           (   5   )               
C. Function Representation of Interpolation Filters
 
     According to the invention, the impulse response of interpolation filters is represented by a function h f  determined by five parameters, and the filter coefficients are calculated as the function values. The five parameters should make the resulting h f  approximate h opt  such that Δerr in (5) is minimized. Obviously, the side information for coding five parameters is very small. Yet the accuracy of the coefficients can also be guaranteed if the parameters are quantized with enough precision. 
     Let F(e jω     x   ,e jω     y   ) be the Fourier transform of the reference frame P. After upsampling P using zero-insertion, shown in  FIG. 1  as element  12 , the Fourier transform of the upsampled frame P 16 , denoted as F 16 (e jω     x   ,e jω     y   ), is given by (6):
 
 F   16 ( e   jω     x     ,e   jω     y   )= F ( e   j4ω     x     ,e   j4ω     y   )  (6)
 
     According to (6), F 16  is a frequency-scaled version of F. In F 16 , the undesired spectra centering at integer multiples of (π/2,π/2), i.e., the original sampling rate, are introduced by the zero-insertion upsampling and should be removed. This requires a low pass filter h  14  (see  FIG. 1 ) with a gain of 16 and a cutoff frequency. The ideal frequency response is shown in  FIG. 2(   a ). The interpolation filter with such a frequency response can preserve all the information in F. 
     According to the invention, a PIF is provided where the frequency response of h f  is based on the special energy distribution of HD videos in the frequency domain, compared with low resolution videos. A random field in the frequency domain is represented by power spectral density (PSD), denoted as S pp (ω x , ω y ) which by definition is the Fourier transform of the autocorrelaiton. In practice, autocorrelation is an estimate based on a set of videos. Applying Fourier transform to the estimated autocorrelation produces an estimated PSD, which may not be consistent. Here, PSD is estimated by the periodgram of the random field, as given in (7). 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             
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                             2 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     To achieve a more accurate result, each video sequence, taken as a realization of a random field, has 30 frames involved whose periodgrams are averaged to produce the estimated PSD. Two differences on PSD between HD and CIF sequences can be observed. First, the low frequency energy in CIF sequences is smaller then that in HD sequences. Second, the high frequency energies in HD sequences are mainly distributed in the horizontal and vertical directions, whereas the high frequency energies in CIF sequences are distributed in arbitrary directions. 
     Considering the special PSD of HD videos, the desired filter according to the invention has a diamond-shaped passband (see  FIG. 2(   b )), such that high frequency components neither in the horizontal direction nor in the vertical direction are filtered out. There are two reasons for proposing the filter with such a passband. First, interpolation in the context of video coding is for a better MCP, and the high frequency components are more likely to introduce large prediction error, thus exerting negative influence on MCP. Second, high frequency components outside the diamond-shaped passband have very low energy and contribute little to the content of the frame. This passband shape also accords with the observation on a number of optimal AIFs. 
     Theoretically, the cutoff frequency should be π/4, such as the one in  FIG. 2(   b ), although, in practice, they may vary around π/4. As shown in  FIG. 3 , two parameters, ω 1  and ω 2 , are used to denote the cutoff frequencies at two axes and the shaded diamond-shaped area σ represents the passband. The corresponding impulse response h d  can be obtained by inverse Fourier transform, as expressed in (8), 
                       h   d     ⁡     (     m   ,   n     )       =       1     4   ⁢     π   2         ⁢       ∫       [       -   π     ,   π     ]     2       ⁢         H   d     ⁡     (       ⅇ     j   ⁢           ⁢   u       ,     ⅇ     j   ⁢           ⁢   v         )       ⁢     ⅇ       j   ⁢           ⁢   mu     +     j   ⁢           ⁢   nv         ⁢     ⅆ   u     ⁢     ⅆ   v                   (   8   )               
where H d  is given as below.
 
     
       
         
           
             
               
                 
                   
                     
                       H 
                       d 
                     
                     ⁡ 
                     
                       ( 
                       
                         
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                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     Substituting (9) into (8), one can finally find the formula of h d  as shown in (10): 
                         h   d     ⁡     (     m   ,   n     )       =         8   ⁢     ω   1     ⁢     ω   2         π   2       ⁢   Sin   ⁢           ⁢     c   ⁡     (           ω   1     ⁢   m     +       ω   2     ⁢   n       2     )       ⁢   Sin   ⁢           ⁢     c   ⁡     (           ω   1     ⁢   m     -       ω   2     ⁢   n       2     )           ,     
     ⁢       -   ∞     &lt;   m     ,     n   &lt;   ∞             (   10   )               
where function Sinc(.) is defined as follows:
 
     
       
         
           
             
               
                 
                   
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                   ( 
                   11 
                   ) 
                 
               
             
           
         
       
     
     The filter function h d  as noted in Equation (10) is infinite, so it must be truncated by an appropriate window function w before used for interpolation. If the filter is simply truncated by limiting the range of m, n to [−11, 11], deleterious effects occur that degrade the performance. According to this invention, w is defined in (12), using three limiting parameters a, b and c. This function for w in m and n is empirically better than other widely used functions, such as rectangular, triangular, Harming, and Blackman window functions. 
     
       
         
           
             
               
                 
                   
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                           otherwise 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   12 
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     Parameter a specifies the DC level of w. Parameters b and c specify the shape. Small b and c values lead to a flat shape, whereas large b and c values lead to a sharp shape. The approximation of the desired filter, denoted as h f , is obtained as the product of h d  and w, herein written out as. 
     
       
         
           
             
               
                 
                   
                     
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                                   ( 
                                   
                                     
                                       
                                         
                                           ω 
                                           1 
                                         
                                         ⁢ 
                                         m 
                                       
                                       - 
                                       
                                         
                                           ω 
                                           2 
                                         
                                         ⁢ 
                                         n 
                                       
                                     
                                     2 
                                   
                                   ) 
                                 
                               
                               ⁢ 
                               
                                 ( 
                                 
                                   a 
                                   + 
                                   
                                     Sin 
                                     ⁢ 
                                     
                                         
                                     
                                     ⁢ 
                                     
                                       c 
                                       ⁡ 
                                       
                                         ( 
                                         
                                           
                                             b 
                                             ⁢ 
                                             
                                                
                                               m 
                                                
                                             
                                           
                                           + 
                                           
                                             c 
                                             ⁢ 
                                             
                                                
                                               n 
                                                
                                             
                                           
                                         
                                         ) 
                                       
                                     
                                   
                                 
                                 ) 
                               
                             
                             , 
                           
                         
                       
                       
                         
                           
                             
                               
                                 if 
                                 ⁢ 
                                 
                                     
                                 
                                 - 
                                 11 
                               
                               ≤ 
                               m 
                             
                             , 
                             
                               n 
                               ≤ 
                               11 
                             
                           
                         
                       
                       
                         
                           
                             0 
                             , 
                             otherwise 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   13 
                   ) 
                 
               
             
           
         
       
     
     Therefore, h f  is determined by a five-parameter set, x={ω 1 , ω 2 , a, b, c}. The factor N guarantees the filter gain is 16. According to the invention, an interpolation filter h f  is defined and completely specified by Equation 13. Since this represents filtering by parameters rather than by individual coefficients, it therefore is named a parametric interpolation filter (PIF). 
     D. Parameter Determination and Coding 
     As the faun of h f  has been given in (13), the parameter set x has to be determined for each frame and coded. The optimal x should make h f  reduce as much prediction error as h opt  can, by achieving the minimum of Δerr in (5), i.e., 
                     x   ^     =     arg   ⁢       min   x     ⁢     Δ   ⁢           ⁢   err                 (   14   )               
where h Δ  in (5) can be expressed as in (15)
 
 h   Δ ( i,j )= h   opt ( i,j )− h   f ( i,j|x ),−11 ≦i,j≦ 11  (15)
 
     The minimum is achieved by using a quasi-Newton method, which is based on Newton&#39;s method to find the stationary point of the objective function, where the gradient is zero. Different from Newton&#39;s method, which uses the first and second derivatives, i.e., gradient and Hessian, to find the stationary point, the quasi-Newton method does not directly compute the Hessian matrix of the objective function. Instead, it updates the Hessian matrix by analyzing successive gradient vectors. Here, the BFGS algorithm is used for the quasi-Newton method to update the Hessian matrix. The Broyden-Fletcher-Goldfarb-Shanno algorithm is a known quasi-Newton optimization method f The detailed description is omitted here, as this method is known and not a part of the contribution in this invention. 
     Since this numerical method solves local minimum problems, the initial estimate of (the vector) x becomes critical. At the beginning of the coding of a video sequence, the initial estimates of ω 1  and ω 2  are both π/4, and empirical initial values 0.1, 0.15, and 0.15 are assigned to a, b, and c, respectively. During the coding process, the initial estimate keeps updated by the value of the estimate {circumflex over (x)} of the latest P-frame using PIF mode. 
     As the BFGS method is for unconstrained optimization, the solution x will theoretically have any real numbers. However, the values of ω 1  and ω 2  are expected to be around π/4 and the valid range is [0,π]. The values of a, b, and c are around zero. Therefore, in case any of ω 1  and ω 2  is larger than π or any of a, b, and c has the absolute value larger than 1, the solution is discarded, and consequently the PIF mode in the current frame is disabled. However, based on experiments, such case has never occurred and is therefore not expected to occur. The magnitude of each parameter is uniformly quantized to 8196 steps, coded by 13-bit fixed length coding (FLC). To indicate the signs of a, b, and c, three additional bits are used. Hence, the side information is exactly 68 bits for each frame. 
     In summary, according to the invention, an interpolation filter for sampled video signal processing to reduce prediction error is provided having a diamond-shaped transfer function h as given by equation 13 optimized by five parameters as given in equation 13 as two cutoff frequencies and three filter function limiting parameters each calculated over a limited parametric range for each video frame. 
     The invention has been explained with reference to specific embodiments. Other embodiments and implementations of the invention will be evident to those of ordinary skill in the art. It is therefore not intended that the invention be limited, except as indicated by appended claims.