% Computes motion vectors using Three Step 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
% TSScomputations: The average number of points searched for a macroblock
%
% Written by Aroh Barjatya
function [motionVect, TSScomputations] = motionEstTSS(imgP, imgI, mbSize, p)
[row col] = size(imgI);
vectors = zeros(2,row*col/mbSize^2);
costs = ones(3, 3) * 65537;
computations = 0;
% we now take effectively log to the base 2 of p
% this will give us the number of steps required
L = floor(log10(p+1)/log10(2));
stepMax = 2^(L-1);
% 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 three step search starts
% we will evaluate 9 elements at every step
% read the literature to find out what the pattern is
% my variables have been named aptly to reflect their significance
x = j;
y = i;
% In order to avoid calculating the center point of the search
% again and again we always store the value for it from teh
% previous run. For the first iteration we store this value outside
% the for loop, but for subsequent iterations we store the cost at
% the point where we are going to shift our root.
costs(2,2) = costFuncMAD(imgP(i:i+mbSize-1,j:j+mbSize-1), ...
imgI(i:i+mbSize-1,j:j+mbSize-1),mbSize);
computations = computations + 1;
stepSize = stepMax;
while(stepSize >= 1)
% m is row(vertical) index
% n is col(horizontal) index
% this means we are scanning in raster order
for m = -stepSize : stepSize : stepSize
for n = -stepSize : stepSize : stepSize
refBlkVer = y + m; % row/Vert co-ordinate for ref block
refBlkHor = x + n; % col/Horizontal co-ordinate
if ( refBlkVer < 1 | refBlkVer+mbSize-1 > row ...
| refBlkHor < 1 | refBlkHor+mbSize-1 > col)
continue;
end
costRow = m/stepSize + 2;
costCol = n/stepSize + 2;
if (costRow == 2 & costCol == 2)
continue
end
costs(costRow, costCol ) = 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
% shift the root for search window to new minima point
x = x + (dx-2)*stepSize;
y = y + (dy-2)*stepSize;
% Arohs thought: At this point we can check and see if the
% shifted co-ordinates are exactly the same as the root
% co-ordinates of the last step, then we check them against a
% preset threshold, and ifthe cost is less then that, than we
% can exit from teh loop right here. This way we can save more
% computations. However, as this is not implemented in the
% paper I am modeling, I am not incorporating this test.
% May be later...as my own addition to the algorithm
stepSize = stepSize / 2;
costs(2,2) = costs(dy,dx);
end
vectors(1,mbCount) = y - i; % row co-ordinate for the vector
vectors(2,mbCount) = x - j; % col co-ordinate for the vector
mbCount = mbCount + 1;
costs = ones(3,3) * 65537;
end
end
motionVect = vectors;
TSScomputations = computations/(mbCount - 1);