262 lines
8.0 KiB
Matlab
262 lines
8.0 KiB
Matlab
function calculate_PSNR_SSIM()
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% GT and SR folder
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folder_GT = '/mnt/SSD/xtwang/BasicSR_datasets/val_set5/Set5';
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folder_SR = '/home/xtwang/Projects/BasicSR/results/RRDB_PSNR_x4/set5';
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scale = 4;
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suffix = ''; % suffix for SR images
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test_Y = 1; % 1 for test Y channel only; 0 for test RGB channels
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if test_Y
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fprintf('Tesing Y channel.\n');
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else
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fprintf('Tesing RGB channels.\n');
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end
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filepaths = dir(fullfile(folder_GT, '*.png'));
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PSNR_all = zeros(1, length(filepaths));
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SSIM_all = zeros(1, length(filepaths));
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for idx_im = 1:length(filepaths)
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im_name = filepaths(idx_im).name;
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im_GT = imread(fullfile(folder_GT, im_name));
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im_SR = imread(fullfile(folder_SR, [im_name(1:end-4), suffix, '.png']));
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if test_Y % evaluate on Y channel in YCbCr color space
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if size(im_GT, 3) == 3
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im_GT_YCbCr = rgb2ycbcr(im2double(im_GT));
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im_GT_in = im_GT_YCbCr(:,:,1);
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im_SR_YCbCr = rgb2ycbcr(im2double(im_SR));
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im_SR_in = im_SR_YCbCr(:,:,1);
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else
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im_GT_in = im2double(im_GT);
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im_SR_in = im2double(im_SR);
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end
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else % evaluate on RGB channels
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im_GT_in = im2double(im_GT);
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im_SR_in = im2double(im_SR);
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end
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% calculate PSNR and SSIM
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PSNR_all(idx_im) = calculate_PSNR(im_GT_in * 255, im_SR_in * 255, scale);
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SSIM_all(idx_im) = calculate_SSIM(im_GT_in * 255, im_SR_in * 255, scale);
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fprintf('%d.(X%d)%20s: \tPSNR = %f \tSSIM = %f\n', idx_im, scale, im_name(1:end-4), PSNR_all(idx_im), SSIM_all(idx_im));
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end
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fprintf('\n%26s: \tPSNR = %f \tSSIM = %f\n', '####Average', mean(PSNR_all), mean(SSIM_all));
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end
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function res = calculate_PSNR(GT, SR, border)
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% remove border
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GT = GT(border+1:end-border, border+1:end-border, :);
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SR = SR(border+1:end-border, border+1:end-border, :);
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% calculate PNSR (assume in [0,255])
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error = GT(:) - SR(:);
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mse = mean(error.^2);
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res = 10 * log10(255^2/mse);
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end
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function res = calculate_SSIM(GT, SR, border)
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GT = GT(border+1:end-border, border+1:end-border, :);
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SR = SR(border+1:end-border, border+1:end-border, :);
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% calculate SSIM
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mssim = zeros(1, size(SR, 3));
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for i = 1:size(SR,3)
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[mssim(i), ~] = ssim_index(GT(:,:,i), SR(:,:,i));
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end
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res = mean(mssim);
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end
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function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)
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%========================================================================
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%SSIM Index, Version 1.0
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%Copyright(c) 2003 Zhou Wang
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%All Rights Reserved.
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%
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%The author is with Howard Hughes Medical Institute, and Laboratory
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%for Computational Vision at Center for Neural Science and Courant
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%Institute of Mathematical Sciences, New York University.
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%
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%----------------------------------------------------------------------
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%Permission to use, copy, or modify this software and its documentation
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%for educational and research purposes only and without fee is hereby
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%granted, provided that this copyright notice and the original authors'
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%names appear on all copies and supporting documentation. This program
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%shall not be used, rewritten, or adapted as the basis of a commercial
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%software or hardware product without first obtaining permission of the
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%authors. The authors make no representations about the suitability of
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%this software for any purpose. It is provided "as is" without express
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%or implied warranty.
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%----------------------------------------------------------------------
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%
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%This is an implementation of the algorithm for calculating the
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%Structural SIMilarity (SSIM) index between two images. Please refer
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%to the following paper:
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%
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%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
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%quality assessment: From error measurement to structural similarity"
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%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
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%
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%Kindly report any suggestions or corrections to zhouwang@ieee.org
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%
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%----------------------------------------------------------------------
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%
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%Input : (1) img1: the first image being compared
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% (2) img2: the second image being compared
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% (3) K: constants in the SSIM index formula (see the above
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% reference). defualt value: K = [0.01 0.03]
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% (4) window: local window for statistics (see the above
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% reference). default widnow is Gaussian given by
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% window = fspecial('gaussian', 11, 1.5);
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% (5) L: dynamic range of the images. default: L = 255
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%
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%Output: (1) mssim: the mean SSIM index value between 2 images.
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% If one of the images being compared is regarded as
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% perfect quality, then mssim can be considered as the
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% quality measure of the other image.
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% If img1 = img2, then mssim = 1.
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% (2) ssim_map: the SSIM index map of the test image. The map
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% has a smaller size than the input images. The actual size:
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% size(img1) - size(window) + 1.
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%
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%Default Usage:
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% Given 2 test images img1 and img2, whose dynamic range is 0-255
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%
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% [mssim ssim_map] = ssim_index(img1, img2);
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%
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%Advanced Usage:
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% User defined parameters. For example
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%
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% K = [0.05 0.05];
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% window = ones(8);
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% L = 100;
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% [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
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%
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%See the results:
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%
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% mssim %Gives the mssim value
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% imshow(max(0, ssim_map).^4) %Shows the SSIM index map
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%
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%========================================================================
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if (nargin < 2 || nargin > 5)
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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if (size(img1) ~= size(img2))
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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[M, N] = size(img1);
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if (nargin == 2)
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if ((M < 11) || (N < 11))
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ssim_index = -Inf;
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ssim_map = -Inf;
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return
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end
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window = fspecial('gaussian', 11, 1.5); %
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K(1) = 0.01; % default settings
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K(2) = 0.03; %
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L = 255; %
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end
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if (nargin == 3)
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if ((M < 11) || (N < 11))
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ssim_index = -Inf;
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ssim_map = -Inf;
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return
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end
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window = fspecial('gaussian', 11, 1.5);
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L = 255;
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if (length(K) == 2)
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if (K(1) < 0 || K(2) < 0)
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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else
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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end
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if (nargin == 4)
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[H, W] = size(window);
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if ((H*W) < 4 || (H > M) || (W > N))
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ssim_index = -Inf;
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ssim_map = -Inf;
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return
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end
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L = 255;
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if (length(K) == 2)
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if (K(1) < 0 || K(2) < 0)
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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else
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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end
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if (nargin == 5)
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[H, W] = size(window);
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if ((H*W) < 4 || (H > M) || (W > N))
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ssim_index = -Inf;
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ssim_map = -Inf;
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return
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end
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if (length(K) == 2)
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if (K(1) < 0 || K(2) < 0)
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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else
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ssim_index = -Inf;
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ssim_map = -Inf;
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return;
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end
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end
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C1 = (K(1)*L)^2;
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C2 = (K(2)*L)^2;
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window = window/sum(sum(window));
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img1 = double(img1);
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img2 = double(img2);
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mu1 = filter2(window, img1, 'valid');
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mu2 = filter2(window, img2, 'valid');
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mu1_sq = mu1.*mu1;
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mu2_sq = mu2.*mu2;
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mu1_mu2 = mu1.*mu2;
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sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
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sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
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sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;
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if (C1 > 0 && C2 > 0)
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ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
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else
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numerator1 = 2*mu1_mu2 + C1;
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numerator2 = 2*sigma12 + C2;
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denominator1 = mu1_sq + mu2_sq + C1;
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denominator2 = sigma1_sq + sigma2_sq + C2;
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ssim_map = ones(size(mu1));
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index = (denominator1.*denominator2 > 0);
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ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
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index = (denominator1 ~= 0) & (denominator2 == 0);
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ssim_map(index) = numerator1(index)./denominator1(index);
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end
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mssim = mean2(ssim_map);
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end
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