svdCompress.m

function [cmpImg] = svdCompress(img,k)

  % Obtains SVD
  [u,s,v] = svd(img);

  % Vectorized sum of k rank one matrices
  cmpImg = u(:,1:k)*s(1:k,1:k)*v’(1:k,:);

end;

svdVisualize.m

function [normError] = svdVisualize(FILENAME,k,plot)

% Loads Image and takes one of RBG matrix
img = imread(FILENAME)(:,:,1);

% Obtains SVD
[u,s,v] = svd(img);

% Vectorized sum of rank = k Matrix
cmpImg = uint8(u(:,1:k)*s(1:k,1:k)*v’(1:k,:));

if plot == 1
  % original image
  subplot(1,3,1);
  image(img./5)

  axis off

  % compressed image of rank k
  subplot(1,3,2);
  image(cmpImg./5)

  axis off
  % difference error
  subplot(1,3,3);
  image(img-cmpImg);

  axis off

else

  errMatrix = double(img-cmpImg);
  normError = norm(errMatrix);
end

end;