修改后的SSIM图像质量评价算法

function [mssim, ssim_map] = ssim(img1, img2, K, window, L) % ======================================================================== % SSIM Index with automatic downsampling, Version 1.0 % Copyright(c) 2009 Zhou Wang % All Rights Reserved. % % ---------------------------------------------------------------------- % 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 and the website with suggested usage % % 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. % % http://www.ece.uwaterloo.ca/~z70wang/research/ssim/ % % Note: This program is different from ssim_index.m, where no automatic % downsampling is performed. (downsampling was done in the above paper % and was described as suggested usage in the above website.) % % Kindly report any suggestions or corrections to [email protected] % %---------------------------------------------------------------------- % %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 % depends on the window size and the downsampling factor. % %Basic Usage: % Given 2 test images img1 and img2, whose dynamic range is 0-255 % % [mssim, ssim_map] = ssim(img1, img2); % %Advanced Usage: % User defined parameters. For example % % K = [0.05 0.05]; % window = ones(8); % L = 100; % [mssim, ssim_map] = ssim(img1, img2, K, window, L); % %Visualize the results: % % mssim %Gives the mssim value % imshow(max(0, ssim_map).^4) %Shows the SSIM index map %输入：（1）img1：正在比较的第一个图像 % （2）img2：比较第二个图像 % （3）K：SSIM指数公式中的常数（见上文参考）。 defualt值：K = [0.01 0.03] % （4）窗口：用于统计的本地窗口（参见上面 % 参考）。默认widnow为高斯给定 % window = fspecial（'gaussian'，11，1.5）; % （5）L：图像的动态范围。默认值：L = 255 % % 输出：（1）mssim：2个图像之间的平均SSIM指数值。 % 如果正在比较的图像之一被视为 % 完美质量，那么mssim可以算作 % 其他图像的质量测量。 % 如果img1 = img2，那么mssim = 1。 % （2）ssim_map：测试映像的SSIM索引映射。地图 % 具有比输入图像小的尺寸。实际尺寸 % 取决于窗口大小和下采样因子。 % % 基本用法： % 给出2个测试图像img1和img2，其动态范围为0-255 % % [mssim，ssim_map] = ssim（img1，img2）; % % 高级用法： % 用户定义的参数。例如 % % K = [0.05±0.05]; % window = ones（8）; % L = 100; % [mssim，ssim_map] = ssim（img1，img2，K，window，L）; % % 可视化结果： % % mssim％提供mssim值 % imshow（max（0，ssim_map）。^ 4）％显示SSIM索引映射 %======================================================================== 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 img1 = double(img1); img2 = double(img2); % automatic downsampling f = max(1,round(min(M,N)/256)); %downsampling by f %use a simple low-pass filter if(f>1) lpf = ones(f,f); lpf = lpf/sum(lpf(:)); img1 = imfilter(img1,lpf,'symmetric','same'); img2 = imfilter(img2,lpf,'symmetric','same'); img1 = img1(1:f:end,1:f:end); img2 = img2(1:f:end,1:f:end); end C1 = (K(1)*L)^2; C2 = (K(2)*L)^2; window = window/sum(sum(window)); 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)); 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