官方网站：https://ece.uwaterloo.ca/~z70wang/research/ssim/
1、SSIM
structural similarity index
经常用到图像处理中，特别在图像去噪处理中在图像相似度评价上全面超越SNR（signal to noise ratio）和PSNR（peak signal to noise ratio）。
作为结构相似性理论的实现，结构相似度指数从图像组成的角度将结构信息定义为独立于亮度、对比度的，反映场景中物体结构的属性，并将失真建模为亮度、对比度和结构三个不同因素的组合。用均值作为亮度的估计，标准差作为对比度的估计，协方差作为结构相似程度的度量。
2.峰值信噪比（PSNR）
峰值信噪比（PSNR）是最普遍，最广泛使用的评鉴画质的客观量测法，PSNR的单位为dB。所以PSNR值越大，就代表失真越少
PSNR=10*log10((2^n-1)^2/MSE)，MSE是原图像与处理图像之间均方误差。
源代码：
function s=csnr(A,B,row,col)%%峰值信噪比（PSNR）是最普遍，最广泛使用的评鉴画质的客观量测法，PSNR的单位为dB。所以PSNR值越大，就代表失真越少
%%PSNR=10*log10((2^n-1)^2/MSE)，MSE是原图像与处理图像之间均方误差。
%%row和col表示图像的边界像素数，A表示元图像，B表示处理后图像，返回值是性噪比
[n,m,ch]=size(A);
if ch==1 %%二维灰度图像
e=A-B;
e=e(row+1:n-row,col+1:m-col);
me=mean(mean(e.^2)); %%每个元素平方后，先求每列的均值，再求向量的均值，结果相当于求每个元素平方后的均值，即均方误差
s=10*log10(255^2/me);
else %%表示二维彩色图像，具有三个通道，相当于有三层二维灰度图像，计算PSNR时每层分别进行计算
e=A-B;
e=e(row+1:n-row,col+1:m-col,:);
e1=e(:,:,1);e2=e(:,:,2);e3=e(:,:,3);
me1=mean(mean(e1.^2)); %R
me2=mean(mean(e2.^2)); %G
me3=mean(mean(e3.^2)); %B
s(1)=10*log10(255^2/me1);
s(2)=10*log10(255^2/me2);
s(3)=10*log10(255^2/me3);
end
return;
function ssim = cal_ssim( im1, im2, b_row, b_col )
[h w] = size( im1 );
ssim = ssim_index( im1( b_row+1:h-b_row, b_col+1:w-b_col ), im2( b_row+1:h-b_row, b_col+1:w-b_col ) );
return;
function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)
%========================================================================
%SSIM Index, Version 1.0
%Copyright(c) 2003 Zhou Wang
%All Rights Reserved.
%
%The author was with Howard Hughes Medical Institute, and Laboratory
%for Computational Vision at Center for Neural Science and Courant
%Institute of Mathematical Sciences, New York University, USA. He is
%currently with Department of Electrical and Computer Engineering,
%University of Waterloo, Canada.
%
%----------------------------------------------------------------------
%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.
%----------------------------------------------------------------------
% 是一个计算两幅图像结构相似性指数SSIM的算法
%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. 4, 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 > 5)
mssim = -Inf;
ssim_map = -Inf;
return;
end
if (size(img1) ~= size(img2))
mssim = -Inf;
ssim_map = -Inf;
return;
end
[M N] = size(img1);
if (nargin == 2)
if ((M < 11) | (N < 11))
mssim = -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))
mssim = -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)
mssim = -Inf;
ssim_map = -Inf;
return;
end
else
mssim = -Inf;
ssim_map = -Inf;
return;
end
end
if (nargin == 4)
[H W] = size(window);
if ((H*W) < 4 | (H > M) | (W > N))
mssim = -Inf;
ssim_map = -Inf;
return
end
L = 255;
if (length(K) == 2)
if (K(1) < 0 | K(2) < 0)
mssim = -Inf;
ssim_map = -Inf;
return;
end
else
mssim = -Inf;
ssim_map = -Inf;
return;
end
end
if (nargin == 5)
[H W] = size(window);
if ((H*W) < 4 | (H > M) | (W > N))
mssim = -Inf;
ssim_map = -Inf;
return
end
if (length(K) == 2)
if (K(1) < 0 | K(2) < 0)
mssim = -Inf;
ssim_map = -Inf;
return;
end
else
mssim = -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));
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
衡量两幅图像相似度的指标SNR（signal to noise ratio）和PSNR（peak signal to noise ratio）SSIM（structural similarity in
原文地址：http://blog.csdn.net/u013467442/article/details/44857657
