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Matlab图像处理（进阶版）
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⛄一、无参考图像质量评价NIQE简介
理论知识参考：通用型无参考图像质量评价算法综述
⛄二、部分源代码
function [mu_prisparam cov_prisparam] = estimatemodelparam(folderpath,…
blocksizerow,blocksizecol,blockrowoverlap,blockcoloverlap,sh_th)
% Input
% folderpath - Folder containing the pristine images
% blocksizerow - Height of the blocks in to which image is divided
% blocksizecol - Width of the blocks in to which image is divided
% blockrowoverlap - Amount of vertical overlap between blocks
% blockcoloverlap - Amount of horizontal overlap between blocks
% sh_th - The sharpness threshold level
%Output
%mu_prisparam - mean of multivariate Gaussian model
%cov_prisparam - covariance of multivariate Gaussian model
% Example call
%[mu_prisparam cov_prisparam] = estimatemodelparam(‘pristine’,96,96,0,0,0.75);
%----------------------------------------------------------------
% Find the names of images in the folder
current = pwd;
cd(sprintf(‘%s’,folderpath))
names = ls;
names = names(3:end,:);%
cd(current)
% ---------------------------------------------------------------
%Number of features
% 18 features at each scale
featnum = 18;
% ---------------------------------------------------------------
% Make the directory for storing the features
mkdir(sprintf(‘local_risquee_prisfeatures’))
% ---------------------------------------------------------------
% Compute pristine image features
for itr = 1:size(names,1)
itr
im = imread(sprintf(‘%s\%s’,folderpath,names(itr,:)));
if(size(im,3)==3)
im = rgb2gray(im);
end
im = double(im);
[row col] = size(im);
block_rownum = floor(row/blocksizerow);
block_colnum = floor(col/blocksizecol);
im = im(1:block_rownumblocksizerow, …
1:block_colnumblocksizecol);
window = fspecial(‘gaussian’,7,7/6);
window = window/sum(sum(window));
scalenum = 2;
warning(‘off’)
feat = [];
for itr_scale = 1:scalenum
mu = imfilter(im,window,‘replicate’);
mu_sq = mu.*mu;
sigma = sqrt(abs(imfilter(im.*im,window,‘replicate’) - mu_sq));
structdis = (im-mu)./(sigma+1);
feat_scale = blkproc(structdis,[blocksizerow/itr_scale blocksizecol/itr_scale], …
[blockrowoverlap/itr_scale blockcoloverlap/itr_scale], …
@computefeature);
feat_scale = reshape(feat_scale,[featnum …
size(feat_scale,1)*size(feat_scale,2)/featnum]);
feat_scale = feat_scale’;
if(itr_scale == 1)
sharpness = blkproc(sigma,[blocksizerow blocksizecol], …
[blockrowoverlap blockcoloverlap],@computemean);
sharpness = sharpness(😃;
end
feat = [feat feat_scale];
im =imresize(im,0.5);
end
function quality = computequality(im,blocksizerow,blocksizecol,…
blockrowoverlap,blockcoloverlap,mu_prisparam,cov_prisparam)
% Input1
% im - Image whose quality needs to be computed
% blocksizerow - Height of the blocks in to which image is divided
% blocksizecol - Width of the blocks in to which image is divided
% blockrowoverlap - Amount of vertical overlap between blocks
% blockcoloverlap - Amount of horizontal overlap between blocks
% mu_prisparam - mean of multivariate Gaussian model
% cov_prisparam - covariance of multivariate Gaussian model
% For good performance, it is advisable to use make the multivariate Gaussian model
% using same size patches as the distorted image is divided in to
% Output
%quality - Quality of the input distorted image
% Example call
%quality = computequality(im,96,96,0,0,mu_prisparam,cov_prisparam)
% ---------------------------------------------------------------
%Number of features
% 18 features at each scale
featnum = 18;
%----------------------------------------------------------------
%Compute features
if(size(im,3)==3)
im = rgb2gray(im);
end
im = double(im);
[row col] = size(im);
block_rownum = floor(row/blocksizerow);
block_colnum = floor(col/blocksizecol);
im = im(1:block_rownumblocksizerow,1:block_colnumblocksizecol);
[row col] = size(im);
block_rownum = floor(row/blocksizerow);
block_colnum = floor(col/blocksizecol);
im = im(1:block_rownumblocksizerow, …
1:block_colnumblocksizecol);
window = fspecial(‘gaussian’,7,7/6);
window = window/sum(sum(window));
scalenum = 2;
warning(‘off’)
feat = [];
⛄三、运行结果
⛄四、matlab版本及参考文献
1 matlab版本
2014a
2 参考文献
[1]杨璐,王辉,魏敏.基于机器学习的无参考图像质量评价综述[J].计算机工程与应用. 2018,54(19)
3 备注
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