% Computes motion vectors using exhaustive search method
%
% Input
% imgP : The image for which we want to find motion vectors
% imgI : The reference image
% mbSize : Size of the macroblock
% p : Search parameter (read literature to find what this means)
%
% Ouput
% motionVect : the motion vectors for each integral macroblock in imgP
% EScomputations: The average number of points searched for a macroblock
%
% Written by Aroh Barjatya
function [motionVect, EScomputations] = motionEstES(imgP, imgI, mbSize, p)
[row col] = size(imgI);
vectors = zeros(2,row*col/mbSize^2);
costs = ones(2*p + 1, 2*p +1) * 65537;
computations = 0;
% we start off from the top left of the image
% we will walk in steps of mbSize
% for every marcoblock that we look at we will look for
% a close match p pixels on the left, right, top and bottom of it
mbCount = 1;
for i = 1 : mbSize : row-mbSize+1
for j = 1 : mbSize : col-mbSize+1
% the exhaustive search starts here
% we will evaluate cost for (2p + 1) blocks vertically
% and (2p + 1) blocks horizontaly
% m is row(vertical) index
% n is col(horizontal) index
% this means we are scanning in raster order
for m = -p : p
for n = -p : p
refBlkVer = i + m; % row/Vert co-ordinate for ref block
refBlkHor = j + n; % col/Horizontal co-ordinate
if ( refBlkVer < 1 || refBlkVer+mbSize-1 > row ...
|| refBlkHor < 1 || refBlkHor+mbSize-1 > col)
continue;
end
costs(m+p+1,n+p+1) = costFuncMAD(imgP(i:i+mbSize-1,j:j+mbSize-1), ...
imgI(refBlkVer:refBlkVer+mbSize-1, refBlkHor:refBlkHor+mbSize-1), mbSize);
computations = computations + 1;
end
end
% Now we find the vector where the cost is minimum
% and store it ... this is what will be passed back.
[dx, dy, min] = minCost(costs); % finds which macroblock in imgI gave us min Cost
vectors(1,mbCount) = dy-p-1; % row co-ordinate for the vector
vectors(2,mbCount) = dx-p-1; % col co-ordinate for the vector
mbCount = mbCount + 1;
costs = ones(2*p + 1, 2*p +1) * 65537;
end
end
motionVect = vectors;
EScomputations = computations/(mbCount - 1);