NonlinearFittingTID2013.rar_about7ck_datepfo_非线性拟合

%this script is used to calculate the pearson linear correlation %coefficient and root mean sqaured error after regression %get the objective scores computed by the IQA metric and the subjective %scores provided by the dataset matData = load('SSIMOnTID2013.mat'); SSIMOnTID2013 = matData.SSIMOnTID2013; objectiveValues = SSIMOnTID2013(:,1); mos = SSIMOnTID2013(:,2); %plot objective-subjective score pairs p = plot(objectiveValues,mos,'+'); set(p,'Color','blue','LineWidth',1); %initialize the parameters used by the nonlinear fitting function beta(1) = 1; beta(2) = 0; beta(3) = mean(objectiveValues); beta(4) = 0.1; beta(5) = 0.1; %fitting a curve using the data [bayta ehat,J] = nlinfit(objectiveValues,mos,@logistic,beta); %given an objective value, predict the correspoing mos (ypre) using the fitted curve [ypre junk] = nlpredci(@logistic,objectiveValues,bayta,ehat,J); RMSE = sqrt(sum((ypre - mos).^2) / length(mos));%root meas squared error corr_coef = corr(mos, ypre, 'type','Pearson') %pearson linear coefficient %draw the fitted curve t = min(objectiveValues):0.01:max(objectiveValues); [ypre junk] = nlpredci(@logistic,t,bayta,ehat,J); hold on; p = plot(t,ypre); set(p,'Color','black','LineWidth',2); legend('Images in TID2013','Curve fitted with logistic function', 'Location','NorthWest'); xlabel('Objective score by SSIM'); ylabel('MOS');