# 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  
In this paper, all the procedures are performed using the Matlab (R2011a) toolbox. We use the LIBSVM package [48] to implement the SVR and the radial bias function (RBF) kernel for regression. The operating environment is Intel(r) Core(tm) i5 and 2.67 GHz processor with 6 GB of RAM. First of all, in order to make the method proposed in this paper can get better results in performance, we need to choose a reasonable weight value of \(\lambda\) in formula (15). We use different values of \(\lambda\) to test the proposed model. The results are shown in Table 1. As observed from Table 1, we can see the experimental results obtained by the Formula (15) are relatively good when the \(\lambda\) = 0.7. In addition, we also utilize ITTI and GBVS model to extract the ROI of the image and test the proposed model. The performance indices are tabulated in Tables 2 and 3 respectively. In these two tables, since Itti and GBVS model cannot accurately describe the scope of the region of interest, these performances are inferior to the improved Itti model. In order to test the proposed algorithm performance better, we report the performance of two FR IQA algorithms peak-signal-to-noise-ratio (PSNR), and the structural similarity index (SSIM). Since the two algorithms have a good performance in FR IQA, they have been used as a benchmark for many years. We also select three NR IQA algorithms BLIINDS-II, DIIVINE and BRISQUE to compare the performance, because these three methods are general-purpose methods and have a good performance in DCT, wavelet and spatial domain respectively. These performance indices are tabulated in Table 4.  
As seen from Table 4, ROI-BRISQUE performs quite well in terms of correlation with human perception, beating present day full-reference and no-reference image quality assessment indices. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\lambda\) & **KROCC** & **RMSE** & **SROCC** & **PLCC** \\ \hline
0.0 & 0.7674 & 7.9544 & 0.8805 & 0.8818 \\
0.1 & 0.7692 & 7.7801 & 0.8936 & 0.8958 \\
0.2 & 0.7725 & 6.9801 & 0.9169 & 0.9220 \\
0.3 & 0.7802 & 6.2725 & 0.9234 & 0.9267 \\
0.4 & 0.7816 & 5.9544 & 0.9303 & 0.9352 \\
0.5 & 0.7899 & 5.5036 & 0.9439 & 0.9459 \\
0.6 & 0.7907 & 5.0113 & 0.9443 & 0.9450 \\
**0.7** & **0.8021** & **4.7825** & **0.9495** & **0.9494** \\
0.8 & 0.7981 & 4.9130 & 0.9493 & 0.9482 \\
0.9 & 0.7910 & 5.1800 & 0.9454 & 0.9444 \\
1.0 & 0.7841 & 5.5767 & 0.9413 & 0.9396 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of ROI-BRISQUE which ROI extracted by the improved Itti model. The numbers in bold are the best values. Usually we hope the scatter plot should define a cluster, that means the subjective score and objective evaluating score are tightly correlative since the ideal IQA algorithm should accurately reflect the subjective score, such as the DMOS [49]. The scatter plots of different IQA models are shown in Figure 13, where each point represents one test image, with its vertical and horizontal axes representing its predict score and the given objective quality score, respectively. We can see the scatter plots of full reference methods PSNR and SSIM are scattered, while the scatter plots of no reference methods DIIVINE, BLIINDS-II and BRISQUE are evenly distributed along the curve in the low distortion. The prediction is relatively accurate. When the degree of distortion of the image is too large, there is a deviation between the prediction results and the true value, scatter points more dispersed. In this paper, the method can accurately predict the image with different degree of distortion. We can see the distributions cluster along the curve.