Method and related apparatus for motion estimation

Abstract

Disclosed is a motion estimation method for selecting a target motion vector from a plurality of candidate motion vectors in a search range of a target picture for an encoding block having a lot of pixels in a picture is disclosed. The method comprises: utilizing a distortion function to calculate difference between the pixels of the encoding block and the pixels in the search range of a target picture to generate at least one distortion value; utilizing an entropy function to determine the distribution of the difference between the pixels of the encoding block and the pixels in the search range of blocks corresponding to candidate motion vectors to generate at least one distribution values; summing up the distortion values and the distribution values to generate at least one sum value; and selecting the target motion vector according to the sum value.

1 . A motion estimation method for selecting a target motion vector from a plurality of candidate motion vectors in a search range of a target picture for an encoding block having a lot of pixels, the method comprising; 
 utilizing a distortion function to calculate difference between the pixels of the encoding block and the pixels in the search range to generate at least one distortion value;    utilizing an entropy function to determine the distribution of the difference between the pixels of the encoding block and the pixels in the search range corresponding to candidate motion vectors to generate at least one distribution value;    summing up the distortion value and the distribution value to generate at least one sum value; and    selecting the targetmotion vector according to the sum value.    
   
   
       2 . The method of  claim 1 , wherein the distortion function is |C i,j −P i+x,j+y |, wherein C ij  is the pixel in the encoding block, and P i+x,j+y  is the pixel in the search range of the target picture.  
   
   
       3 . The method of  claim 1 , wherein the entropy function is g(|C i,j , P i+x,j+y |), wherein C ij  is the pixel in the encoding block, and P i+x,j+y  is the pixel in the search range of the target picture.  
   
   
       4 . The method of  claim 1 , wherein the entropy function is h(Qp, C i,j , P i+x,j+y ), wherein C ij  is the pixel in the encoding block, and P i+x,j+y  is the pixel in the search range of the target picture.  
   
   
       5 . The method of  claim 4  wherein h(Qp, C i,j , P i+x,j+y ) is K×f(Qp)×g(|C i,j −P i+x,j+y |), where K is a constant and the function f(Qp) is a monotonic increasing function.  
   
   
       6 . The method of  claim 4  wherein the larger Qp is, the larger the function h(Qp, C i,j , P i+x,j+y ) is.  
   
   
       7 . A motion estimation device for selecting a target motion vector from a plurality of candidate motion vectors in a search range for an encoding block having a lot of pixels, the device comprising; 
 a distortion calculator for utilizing a distortion function for calculating difference between the pixels of the encoding block and the pixels in the search range of a target picture to generate at least one distortion value;    a spatial variation calculating module for utilizing an entropy function to calculate the distribution of the difference between the pixels of the encoding block and the pixels in the search range corresponding to candidate motion vectors to generate at least one distribution value; and    a motion vector determining module, coupled with the distortion calculator and the spatial variation calculating module, for summing up the distortion value and the distribution value to generate at least one sum value and for determining the target motion vector according to the sum value.    
   
   
       8 . The device of  claim 7 , wherein the distortion function is |C i,j −P i+x,j+y |, wherein C ij  is the pixel in the encoding block, and Pi+x,j+y is the pixel in the search range of the target picture.  
   
   
       9 . The device of  claim 7 , wherein the entropy function is g(|C i,j , P i+x,j+y |), wherein C ij  is the pixel in the encoding block, and P i+x,j+y  is the pixel in the search range of the target picture.  
   
   
       10 . The device of  claim 7 , wherein the entropy function is h(Qp, C i,j , P i+x,j+y ), wherein C ij  is the pixel in the encoding block, and P i+x,j+y  is the pixel in the search range of the target picture.  
   
   
       11 . The device of  claim 10 , wherein h(Qp, C i,j , P i+x,j+y ) can be represented as K×f(Qp)×g(|C i,j −P i+x,j+y |), K being a constant, and function f(Qp) being a monotonic increasing function.  
   
   
       12 . The device of  claim 11 , wherein the spatial variation calculating module comprises: 
 a first calculator for calculating function g(|C i,j −P i+x,j+y |);    a second calculator for calculating function f(Qp); and    a multiplier coupled with the first calculator and the second calculator for calculating K×f(Qp)×g(|C i,j −P i+x,j+y |).    
   
   
       13 . The device of  claim 10 , wherein the larger Qp is, the larger the function h(Qp, C i,j , P i+x,j+y ) is.  
   
   
       14 . The device of  claim 7 , wherein the motion vector determining module comprises: 
 an adder, coupled with the distortion calculator and the spatial variation calculating module for adding the distortion value to the distribution value;    an accumulator, coupled with the adder, for calculating the sum value; and    a determiner, coupled with the accumulator for determining the target motion vector.    
   
   
       15 . The device of  claim 14 , wherein the determiner comprises: 
 a comparator, coupled with the accumulator, for outputting the target motion vector; and    a storage, coupled with the comparator for storing the minimum value of the sum values that ever appears.