基于MATLAB的无参考盲图像质量评价算法仿真-源码

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 save(sprintf('local_risquee_prisfeatures\\prisfeatures_local%d.mat',... itr),'feat','sharpness'); end %---------------------------------------------- % Load pristine image features prisparam = []; current = pwd; cd(sprintf('%s','local_risquee_prisfeatures')) names = ls; names = names(3:end,:); cd(current) for itr = 1:size(names,1) % Load the features and select the only features load(sprintf('local_risquee_prisfeatures\\%s',strtrim(names(itr,:)))); IX = find(sharpness(:) >sh_th*max(sharpness(:))); feat = feat(IX,:); prisparam = [prisparam; feat]; end %---------------------------------------------- % Compute model parameters mu_prisparam = nanmean(prisparam); cov_prisparam = nancov(prisparam); %---------------------------------------------- % Save features in the mat file save('modelparameters_new.mat','mu_prisparam','cov_prisparam'); %----------------------------------------------