function d = cssim4(reference, distorted)

% calculation of CSSIM4 metric
%
% M. Ponomarenko, K. Egiazarian, V. Lukin, V. Abramova, "Structural Similarity Index with Predictability 
% of Image Blocks", in Proceedings of International Conference MMET 2018, July 2-5, 2018, 4p.

  [y,x,z]=size(reference);

  if z>1     % color images
  
    reference = double(rgb2ycbcr(uint8(reference)));
    distorted = double(rgb2ycbcr(uint8(distorted)));

    r1 = imresize(squeeze(reference(:,:,1)),0.5,'bilinear');
    d1 = imresize(squeeze(distorted(:,:,1)),0.5,'bilinear');
    r2 = imresize(squeeze(reference(:,:,1)),0.25,'bilinear');
    d2 = imresize(squeeze(distorted(:,:,1)),0.25,'bilinear');
    r3 = imresize(squeeze(reference(:,:,2)),0.125,'bilinear');
    d3 = imresize(squeeze(distorted(:,:,2)),0.125,'bilinear');
    r4 = imresize(squeeze(reference(:,:,3)),0.125,'bilinear');
    d4 = imresize(squeeze(distorted(:,:,3)),0.125,'bilinear');
    
    d = (ssim4(r1,d1) + ssim4(r2,d2)*0.5 + ssim4(r3,d3)*0.5 + ssim4(r4,d4)*0.5)/2.5;
    
  else       % grayscale images

    reference = double(reference);
    distorted = double(distorted);

    r1 = imresize(reference,0.5,'bilinear');
    d1 = imresize(distorted,0.5,'bilinear');
    r2 = imresize(reference,0.25,'bilinear');
    d2 = imresize(distorted,0.25,'bilinear');

    d = (ssim4(r1,d1) + ssim4(r2,d2)*0.5)/1.5;
    
  end
    
end


function d = ssim4(reference, distorted)
% calculation of SSIM4 metric
% reference and distorted must be grayscale images

  [y,x,z]=size(reference);

  if z>1
    reference = rgb2ycbcr(uint8(reference));
    reference = double(squeeze(reference(:,:,2)));
    distorted = rgb2ycbcr(uint8(distorted));
    distorted = double(squeeze(distorted(:,:,2)));
  else
    reference = double(reference);
    distorted = double(distorted);
  end
    
  reference = imresize(reference,0.5,'bilinear');
  distorted = imresize(distorted,0.5,'bilinear');

  bs = 9; hsz = 9; zp = 2;  % default values

  d1 = mapdissim(reference,bs,hsz,zp);
  d2 = mapdissim(distorted,bs,hsz,zp);

  [loy lox] = size(d1);
  loy=loy-11;lox=lox-11;

  d1 = d1(6:6+loy,6:6+lox); 
  d2 = d2(6:6+loy,6:6+lox); 

  d1s=d1.^0.5; d2s=d2.^0.5;

  K4 = 0.03;   
  L = 255;
  C4 = (K4*L)^2;

  dd = (2*d1s.*d2s+C4)./(d1+d2+C4);

  %dd =ones(loy+1, lox+1);

  d = ssimm(reference, distorted, [0.01, 0.03], fspecial('gaussian', 11, 1.5), 255, dd);

end

function [mssim, ssim_map] = ssimm(img1, img2, K, window, L, dsi)

% Modification with 4-th factor - self-similarity measure
% dsi - similarity of dissimilarity maps

%========================================================================
%SSIM Index, Version 1.0
%Copyright(c) 2003 Zhou Wang
%All Rights Reserved.
%
%The author is with Howard Hughes Medical Institute, and Laboratory
%for Computational Vision at Center for Neural Science and Courant
%Institute of Mathematical Sciences, New York University.
%
%----------------------------------------------------------------------
%Permission to use, copy, or modify this software and its documentation
%for educational and research purposes only and without fee is hereby
%granted, provided that this copyright notice and the original authors'
%names appear on all copies and supporting documentation. This program
%shall not be used, rewritten, or adapted as the basis of a commercial
%software or hardware product without first obtaining permission of the
%authors. The authors make no representations about the suitability of
%this software for any purpose. It is provided "as is" without express
%or implied warranty.
%----------------------------------------------------------------------
%
%This is an implementation of the algorithm for calculating the
%Structural SIMilarity (SSIM) index between two images. Please refer
%to the following paper:
%
%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
%quality assessment: From error visibility to structural similarity"
%IEEE Transactios on Image Processing, vol. 13, no. 4, pp.600-612,
%Apr. 2004.
%
%Kindly report any suggestions or corrections to zhouwang@ieee.org
%
%----------------------------------------------------------------------
%
%Input : (1) img1: the first image being compared
%        (2) img2: the second image being compared
%        (3) K: constants in the SSIM index formula (see the above
%            reference). defualt value: K = [0.01 0.03]
%        (4) window: local window for statistics (see the above
%            reference). default widnow is Gaussian given by
%            window = fspecial('gaussian', 11, 1.5);
%        (5) L: dynamic range of the images. default: L = 255
%
%Output: (1) mssim: the mean SSIM index value between 2 images.
%            If one of the images being compared is regarded as 
%            perfect quality, then mssim can be considered as the
%            quality measure of the other image.
%            If img1 = img2, then mssim = 1.
%        (2) ssim_map: the SSIM index map of the test image. The map
%            has a smaller size than the input images. The actual size:
%            size(img1) - size(window) + 1.
%
%Default Usage:
%   Given 2 test images img1 and img2, whose dynamic range is 0-255
%
%   [mssim ssim_map] = ssim_index(img1, img2);
%
%Advanced Usage:
%   User defined parameters. For example
%
%   K = [0.05 0.05];
%   window = ones(8);
%   L = 100;
%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
%
%See the results:
%
%   mssim                        %Gives the mssim value
%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
%
%========================================================================


if (nargin < 2 | nargin > 6)
   ssim_index = -Inf;
   ssim_map = -Inf;
   return;
end

if (size(img1) ~= size(img2))
   ssim_index = -Inf;
   ssim_map = -Inf;
   return;
end

[M N] = size(img1);

if (nargin == 2)
   if ((M < 11) | (N < 11))
     ssim_index = -Inf;
     ssim_map = -Inf;
      return
   end
   window = fspecial('gaussian', 11, 1.5);  %
   K(1) = 0.01;                     % default settings
   K(2) = 0.03;                     %
   L = 255;                                  %
end

if (nargin == 3)
   if ((M < 11) | (N < 11))
     ssim_index = -Inf;
     ssim_map = -Inf;
      return
   end
   window = fspecial('gaussian', 11, 1.5);
   L = 255;
   if (length(K) == 2)
      if (K(1) < 0 | K(2) < 0)
       ssim_index = -Inf;
      ssim_map = -Inf;
      return;
      end
   else
     ssim_index = -Inf;
    ssim_map = -Inf;
     return;
   end
end

if (nargin == 4)
   [H W] = size(window);
   if ((H*W) < 4 | (H > M) | (W > N))
     ssim_index = -Inf;
     ssim_map = -Inf;
      return
   end
   L = 255;
   if (length(K) == 2)
      if (K(1) < 0 | K(2) < 0)
       ssim_index = -Inf;
      ssim_map = -Inf;
      return;
      end
   else
     ssim_index = -Inf;
    ssim_map = -Inf;
     return;
   end
end

if (nargin == 5)
   [H W] = size(window);
   if ((H*W) < 4 | (H > M) | (W > N))
     ssim_index = -Inf;
     ssim_map = -Inf;
      return
   end
   if (length(K) == 2)
      if (K(1) < 0 | K(2) < 0)
       ssim_index = -Inf;
      ssim_map = -Inf;
      return;
      end
   else
     ssim_index = -Inf;
    ssim_map = -Inf;
     return;
   end
end

C1 = (K(1)*L)^2;
C2 = (K(2)*L)^2;
window = window/sum(sum(window));
img1 = double(img1);
img2 = double(img2);

mu1   = filter2(window, img1, 'valid');
mu2   = filter2(window, img2, 'valid');
mu1_sq = mu1.*mu1;
mu2_sq = mu2.*mu2;
mu1_mu2 = mu1.*mu2;
sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;

if (C1 > 0 & C2 > 0)
   ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2)).*dsi;
else
   numerator1 = 2*mu1_mu2 + C1;
   numerator2 = 2*sigma12 + C2;
  denominator1 = mu1_sq + mu2_sq + C1;
   denominator2 = sigma1_sq + sigma2_sq + C2;
   ssim_map = ones(size(mu1));
   index = (denominator1.*denominator2 > 0);
   ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
   index = (denominator1 ~= 0) & (denominator2 == 0);
   ssim_map(index) = numerator1(index)./denominator1(index);
end

mssim = mean2(ssim_map);

return

end

function map = mapdissim(im,ws,hsz,zp)

% Fast calculation of dissimilarity map
%
% (c) M.Ponomarenko, 2017
%
% map = mapdissim(image,ws,hsz,zp);
% 
% Inputs:
% im  - grayscale image
% ws  - patch size for dissimilarity calculation
% hsz - half of size of search zone of similar patches
% zp  - size of area around of each patch which will be excluded from 
%       searching to provide robustness to spatially correlated noise. 
%       Set zp=0 if the noise is white or there is no noise on the image.
%
% Output:
% map - values of dissimilarity for each possible position of patch [ws ws] pixels
%
% Examples:
% map = mapdissim(im,9,6,0);
% map = mapdissim(im);

  im = double(im);
  [loy,lox]=size(im);
  bige=1e30;

  if nargin < 4
    zp=0;
  end

  if nargin < 3
    hsz=11;
  end

  if nargin < 2
    ws=9;
  end
   
  a=zeros(loy+hsz*2-1,lox+hsz*2-1);    % zeropadding copy of im
  a(hsz+1:hsz+loy,hsz+1:hsz+lox)=im;

  map=zeros(loy,lox)+bige;       % starting dissimilarity
  
  H = fspecial('average', ws);    % patch size 

  for i=1:hsz*2
    for j=1:hsz*2
      if abs(i-hsz-1)>zp || abs(j-hsz-1)>zp 
        delt=(a(i:i+loy-1,j:j+lox-1)-im).^2; % pixel based MSE
        delt=imfilter(delt,H,'replicate');   % mean MSE in window [ws ws]
        map=min(map,delt);
      end
    end
  end

end