# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Overall Performance Comparison  
The proposed algorithms were evaluated in comparison with the state-of-the-art BIQA algorithms including DIIVINE [12], BLIINDS-II [13], BRISQUE [15], ILNIQE [17], and GMLOG [19]. The overall performance on individual databases in terms of SROCC, PLCC, and RMSE are listed in Table 2. For each performance measure, the two best algorithms are highlighted in boldface. In the experiments, although there are pristine images in some databases, we only used distorted images for training and testing. Each database was randomly divided into a training subset and a test subset without overlapping, where the training subset contained 80% of distorted images in the database and the test subset contained the remaining 20% of distorted images in the database. To eliminate the performance bias, this train-test procedure was implemented for 1000 times, and the median values across 1000 trials were taken as the final performance evaluation. It can be observed in Table 2 that the proposed algorithms achieved encouraging results on all the databases. The top two algorithms were BIQA-SC-II and BIQA-SC, which indicates that the proposed algorithms correlate well with human subjective judgements of image quality on all the databases. In terms of the three performance metrics, BIQA-SC-II had similar results to BIQA-SC on LIVE, CSIQ, MD1, and MD2 databases, and was obviously inferior to BIQA-SC on TID2013 and WIQCD databases. The proposed BIQA-SC-II, which employs the correlation statistics and Weibull parameters, was better than BIQA-SC-I, which employs only the GGD and AGGD parameters extracted from the NFMs. The comparison results demonstrate that the multiple NFMs, the DFMs, and the HSV color image contain useful information for characterizing the perception quality of images with various distortion types. For the purpose of evaluating the statistical significance between BIQA-SC and other competing BIQA algorithms, a \(t\)-test was conducted at 95% significance level between the SROCC results generated by the competing algorithms across the 1000 train-test trials. The results of the \(t\)-test are shown in Table 3. The symbol 1 (\(-1\)) indicates that BIQA-SC is statistically superior (inferior) to the compared algorithm, and 0 indicates that BIQA-SC and the compared algorithm are statistically indistinguishable. It can be seen that BIQA-SC was superior to all the compared algorithms on all the databases. To visualize the statistical significance comparison, Figure 7 shows the box plots of the SROCC and PLCC distributions of the competing BIQA algorithms over 1000 train-test trials on LIVE database. It is clear that the quality scores produced by the proposed BIQA-SC correlated well with human subjective opinions on LIVE database. The proposed BIQA-SC was statistically superior to the state-of-the-art BIQA approaches. The scatter plots and the fitted lines of the DMOS values versus the scores predicted by the competing methods are shown in Figure 8. It can be observed that the predicted scores of the proposed BIQA-SC were nearly linear with the DMOS.