How to measure quality of an image?
I need to find quality of an image before and after pre-processing. For this, i need to calculate PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error),MAXERR (Maximum Squared Error) and L2RAT (Ratio of Squared Norms) X = imread('africasculpt.jpg'); Xapp = X; Xapp(X<=100) = 1; [psnr,mse,maxerr,L2rat] = measerr (X,Xapp) figure; subplot(1,2,1); image(X); subplot(1,2,2); image(Xapp); This is the program that i got in Help menu in matlab. However it is showing an error. Can anyone help me in this......
Neeta Dsouza answered .
2025-11-20
There are psnr() and ssim() functions for image quality in the Image Processing Toolbox. If you don't have a recent version, I have some code you can use in older versions in the file.
% Demo to calculate MSE and PSNR of a gray scale image.
% http://en.wikipedia.org/wiki/PSNR
% Clean up.
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
%------ GET DEMO IMAGES ----------------------------------------------------------
% Read in a standard MATLAB gray scale demo image.
grayImage = imread('cameraman.tif');
[rows columns] = size(grayImage);
% Display the first image.
subplot(2, 2, 1);
imshow(grayImage, []);
title('Original Gray Scale Image', 'FontSize', fontSize);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
% Get a second image by adding noise to the first image.
noisyImage = imnoise(grayImage, 'gaussian', 0, 0.003);
% Display the second image.
subplot(2, 2, 2);
imshow(noisyImage, []);
title('Noisy Image', 'FontSize', fontSize);
%------ PSNR CALCULATION ----------------------------------------------------------
% Now we have our two images and we can calculate the PSNR.
% First, calculate the "square error" image.
% Make sure they're cast to floating point so that we can get negative differences.
% Otherwise two uint8's that should subtract to give a negative number
% would get clipped to zero and not be negative.
squaredErrorImage = (double(grayImage) - double(noisyImage)) .^ 2;
% Display the squared error image.
subplot(2, 2, 3);
imshow(squaredErrorImage, []);
title('Squared Error Image', 'FontSize', fontSize);
% Sum the Squared Image and divide by the number of elements
% to get the Mean Squared Error. It will be a scalar (a single number).
mse = sum(sum(squaredErrorImage)) / (rows * columns);
% Calculate PSNR (Peak Signal to Noise Ratio) from the MSE according to the formula.
PSNR = 10 * log10( 256^2 / mse);
% Alert user of the answer.
message = sprintf('The mean square error is %.2f.\nThe PSNR = %.2f', mse, PSNR);
msgbox(message);