【图像评价】基于matlab无参考图像质量评价NIQE【含Matlab源码 681期】

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_rownum*blocksizerow, ...
                   1:block_colnum*blocksizecol);               
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_rownum*blocksizerow,1:block_colnum*blocksizecol);              
[row col]        = size(im);
block_rownum     = floor(row/blocksizerow);
block_colnum     = floor(col/blocksizecol);
im               = im(1:block_rownum*blocksizerow, ...
                   1:block_colnum*blocksizecol);               
window           = fspecial('gaussian',7,7/6);
window           = window/sum(sum(window));
scalenum         = 2;
warning('off')

feat             = [];


  

 

  
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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] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.

文章来源: qq912100926.blog.csdn.net，作者：海神之光，版权归原作者所有，如需转载，请联系作者。

原文链接：qq912100926.blog.csdn.net/article/details/115419488