I am working on a project which is combination of cryptography and steganography
on image in which is required PSNR of simple image and encrypted image.
You may do this easily with the following code:
A = double(A); B = double(B);
d = sum((A(:)-B(:)).^2) / prod(size(A));
psnr = 10*log10(255*255/d);
Above code if very simplistic, however, You may visit my webpage in researchgate where I have a despeckle filtering toolbox as well as an image quality evalution toolbox in MATLAB to download. There are a lot functions available including psnr.
You may do this easily with the following code:
A = double(A); B = double(B);
d = sum((A(:)-B(:)).^2) / prod(size(A));
psnr = 10*log10(255*255/d);
Above code if very simplistic, however, You may visit my webpage in researchgate where I have a despeckle filtering toolbox as well as an image quality evalution toolbox in MATLAB to download. There are a lot functions available including psnr.
//Find MSE and then you can go for PSNR & SNR.
//For the gray image (0 to 255), convert both the image to double.
im1 = double(im1);
im2 = double(im2);
//Find the mean squared error
mse = sum((im1(:)-im2(:)).^2) / prod(size(im1));
// now find the psnr, as peak=255
psnr = 10*log10(255*255/mse);
//save this matlab function as mse_psnr.m
//To calculate PSNR follow instruction
function [mse,psnr] = mse_psnr(im1,im2)
// find the size of image1
N = size(im1);
//convert image into double
x=im2double(im1);
y=im2double(im2);
//Now calculate MSE then PSNR for a original and encrypted image
acc = 0;
for k1=1:N(1)
for k2=1:N(2)
acc = acc+ ( x(k1,k2) - y(k1,k2) )^2;
end
end
mse = acc/(N(1)*N(2));
//calculate psnr for the original and encrypted image
psnr = 10*log10((255^2)/mse);
ref = imread('pout.tif');
A = imnoise(ref,'salt & pepper', 0.02);
[peaksnr, snr] = psnr(A, ref);
fprintf('\n The Peak-SNR value is %0.4f', peaksnr);
fprintf('\n The SNR value is %0.4f \n', sn
http://www.mathworks.com/help/images/ref/psnr.html#bt5uhgi-2_1
Will there be any difference in programming, while calculating the PSNR of a gray image with one band and a multispectral image with three or more bands?
function PSNR = psnr(distImg, origImg) origImg = double(origImg); distImg = double(distImg); [M N] = size(origImg); error = origImg - distImg; MSE = sum(sum(error .* error)) / (M * N); if(MSE > 0) PSNR = 10*log(255*255/MSE) / log(10); else PSNR = 99; end end
peaksnr = psnr(A,ref) calculates the peak signal-to-noise ratio for the image A, with the image ref as the reference. A and ref must be of the same size and class.
Suppose you have two images A and B of size mxn. Now write the following code:
for i=i:1:m
for j=1:1:n
P=power((A(i,j)-B(i,j)),2);
end
end
MSE=P/(m*n);
PSNR=10*log(255*255/MSE);
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