# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 7 Results and Discussion
### Experiments and Relate Analysis of Image Quality Evaluation
#### 7.2.2 Performance Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **KROCC** & **RMSE** & **SROCC** & **PLCC** \\ \hline
**PSNR** & 0.6674 & 11.2795 & 0.8873 & 0.8866 \\
**SSIM** & 0.7410 & 10.5723 & 0.9201 & 0.9110 \\
**BLIINDS-II** & _0.7619_ & _9.8894_ & _0.9123_ & _0.9145_ \\
**DIIVINE** & _0.7643_ & _8.1542_ & _0.9265_ & _0.9283_ \\
**BRISQUE** & _0.7768_ & _6.5468_ & _0.9314_ & _0.9377_ \\
**ROI-BRISQUE** & _0.8021_ & _4.7613_ & _0.9522_ & _0.9465_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of some IQA methods and the method of this paper. Italicized algorithms are NR IQA algorithms, others are FR IQA algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\lambda}\) & **KROCC** & **RMSE** & **SROCC** & **PLCC** \\ \hline
0.0 & 0.7490 & 6.7257 & 0.9175 & 0.9102 \\
0.1 & 0.7592 & 6.3346 & 0.9258 & 0.9207 \\
0.2 & 0.7709 & 6.0183 & 0.9321 & 0.9288 \\
0.3 & 0.7938 & 5.5164 & 0.9432 & 0.9401 \\
**0.4** & **0.7991** & **5.3783** & **0.9453** & **0.9433** \\
0.5 & 0.7938 & 5.5856 & 0.9439 & 0.9392 \\
0.6 & 0.7918 & 5.5501 & 0.9429 & 0.9399 \\
0.7 & 0.7890 & 5.5012 & 0.9400 & 0.9409 \\
0.8 & 0.7880 & 5.5172 & 0.9404 & 0.9405 \\
0.9 & 0.7804 & 5.6695 & 0.9371 & 0.9372 \\
1.0 & 0.7745 & 5.8075 & 0.9341 & 0.9345 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of ROI-BRISQUE which ROI extracted by GBVS model. The numbers in bold are the best values. In order to verify the accuracy of the method proposed in this paper further, we test five categories image distortion of LIVE image respectively. In addition, 100 images of various types are randomly selected to experiment. We list the median performance parameters for each distortion category and the overall parameters as well. The results are shown in Table 5.  
As a whole it is obvious that ROI-BRISQUE performs well in terms of correlation with human perception, and is competitive with the BRISQUE across distortion types. From Table 5, it can be seen that the ROI-BRISQUE algorithm outperforms the BRISQUE indices in the five different types of distortion. This is a remarkable result, as it shows that algorithms that operate without any reference information can offer performance competitive with the predominant IQA algorithm over the last decades. Figure 14 is scatter plots of five categories. It can be shown that the proposed method has higher accuracy and stability than conventional image quality evaluation methods. We also listed the median correlation values of ROI-BRISQUE as well as other PSNR, SSIM DIIVINE, BLIINDS-II and BRISQUE algorithms. Although the experiment results show some differences according to the median correlation, in this part we evaluate if these differences in correlation is statistically significant. To visualize the statistical significance of the comparison, we show error bars of the distribution of the SROCC values for each of the 1000 experimental trials. The error bars indicate the 95% confidence interval for each algorithm [50]. Plots are shown in  
Figure 13: Scatter plots of DMOS versus different model predictions. Each sample point represents one test image in the entire LIVE database. (**a**) PSNR model; (**b**) SSIM model; (**c**) DIIVINE model; (**d**) BLIINDS-II model; (**e**) BRISQUE model; (**f**) Model of this paper (ROI-BRISQUE).