function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L) %======================================================================== %Edited code by Adam Turcotte and Nicolas Robidoux %Laurentian University %Sudbury, ON, Canada %Last Modified: 2011-01-22 %---------------------------------------------------------------------- %This code implements a refactored computation of SSIM that requires %one fewer blur (4 instead of 5), the same number of pixel-by-pixel %binary operations (10), and two fewer unary operations (6 instead of 8). %As a result, it is about 20% faster. % %In addition, this version reduces memory usage with in-place functions. %As a result, it supports larger input images. %======================================================================== %======================================================================== %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 measurement to structural similarity" %IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 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 > 5) 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); ssim_map = filter2(window, img1, 'valid'); % gx w1 = filter2(window, img2, 'valid'); % gy w2 = ssim_map.*w1; % gx*gy w2 = 2*w2+C1; % 2*(gx*gy)+C1 = num1 w1 = (w1-ssim_map).^2+w2; % (gy-gx)^2+num1 = den1 ssim_map = filter2(window, img1.*img2, 'valid'); % g(x*y) ssim_map = (2*ssim_map+(C1+C2))-w2; % 2*g(x*y)+(C1+C2)-num1 = num2 ssim_map = ssim_map.*w2; % num img1 = img1.^2; % x^2 img2 = img2.^2; % y^2 img1 = img1+img2; % x^2+y^2 if (C1 > 0 && C2 > 0) w2 = filter2(window, img1, 'valid'); % g(x^2+y^2) w2 = w2-w1+(C1+C2); % den2 w2 = w2.*w1; % den ssim_map = ssim_map./w2; % num/den = ssim else w3 = filter2(window, img1, 'valid'); % g(x^2+y^2) w3 = w3-w1+(C1+C2); % den2 w4 = ones(size(w1)); index = (w1.*w3 > 0); w4(index) = (ssim_map(index))./(w1(index).*w3(index)); index = (w1 ~= 0) & (w3 == 0); w4(index) = w2(index)./w1(index); ssim_map = w4; end mssim = mean2(ssim_map); return