# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Comparison with the State-of-the-Arts
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **SROCC (\(p\)-Values)** & **PLCC (\(p\)-Values)** & **RMSE (\(p\)-Values)** \\ \hline PSNR & 0.9366 (0.000068) & 0.9249 (0.002634) & 0.1090 (0.001652) \\ SSIM [1] & 0.9089 (0.003128) & 0.8861 (0.014146) & 0.1328 (0.007789) \\ BIQI [14] & **0.9182** (0.001193) & 0.8974 (0.000621) & 0.2851 (0.006433) \\ BRISQUE [4] & 0.9033 (0.015126) & **0.9279** (0.004072) & **0.1068** (0.000032) \\ TIP [2] & 0.8996 (0.007822) & 0.9014 (0.018125) & 0.2678 (0.005821) \\ NIQE [17] & 0.8951 (0.005408) & 0.9222 (0.009684) & 0.1108 (0.000019) \\ BLIIND-II [3] & 0.8915 (0.000362) & 0.9011 (0.000067) & 0.1243 (0.005118) \\ NR-CSR [18] & 0.8822 (-) & 0.8492 (-) & 0.1513 (-) \\ MCNN [19] & 0.8751 (-) & 0.8882 (-) & 0.1317 (-) \\ IQA-CWT [7] & 0.8010 (-) & - (-) & 0.1626 (-) \\
**NSSIM** & 0.8546 & 0.8971 & 0.1266 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison with ten existing IQA algorithms on CSIQ dataset (150 Gaussian blur images). We take Spearman’s rank order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE) as evaluation indexes. In each column, the best performance value is marked in boldface. Figure 7: Analysis of performance of SROCC, PLCC and RMSE with \(\lambda\) varying from 0 to 5 on LIVE II dataset (145 Gaussian blur images), where \(\lambda\) represents the exponent coefficient of blurriness comparison function in Equation (10). As seen from Table 9, the SROCC of NSSIM is 0.8995, which beats other FR or NR IQA algorithms on TID2013 [28] dataset. A paired-sample \(t\)-test was performed as we done on CSIQ and LIVE II dataset. The \(p\)-values give the demonstration of the robust superior SROCC performance of NSSIM than those of the other algorithms. Furthermore, Table 10 gives means and standard deviations of SROCC, PLCC, and RMSE of tested IQA algorithms on four datasets. Only IQA algorithms tested on all the four datasets are \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **SROCC (\(p\)-Values)** & **PLCC (\(p\)-Values)** & **RMSE (\(p\)-Values)** \\ \hline PSNR & 0.7810 (0.000031) & 0.6427 (0.000004) & 1.6266 (0.000001) \\ SSIM [1] & 0.8727 (0.005351) & 0.8570 (0.000026) & 1.8174 (0.000522) \\ BIQI [14] & 0.9119 (0.004789) & 0.7144 (0.000756) & 2.0347 (0.000011) \\ BRISQUE [4] & 0.9710 (0.010362) & 0.9680 (0.004423) & 1.5331 (0.001605) \\ TIP [2] & 0.9011 (0.009542) & 0.8811 (0.007826) & 1.4768 (0.001412) \\ NIQE [17] & **0.9721** (0.017612) & 0.9561 (0.010149) & **0.1884** (0.005590) \\ BLIND-II [3] & 0.9177 (0.001637) & 0.9111 (0.000488) & 0.8750 (0.007369) \\ MCNN [19] & 0.9358 (-) & 0.9459 (-) & 6.4538 (-) \\ IQA-CWT [7] & 0.9169 (-) & - (-) & 7.8650 (-) \\ SFA [20] & 0.9166 (-) & 0.8305 (-) & 0.7055 (-) \\
**NSSIM** & 0.9464 & **0.9689** & 0.8669 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison with ten existing IQA algorithms on LIVE II dataset (145 Gaussian blur images). We take Spearman’s rank order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE) as evaluation indexes. In each column, the best performance value is marked in boldface. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **SROCC (\(p\)-Values)** & **PLCC (\(p\)-Values)** & **RMSE (\(p\)-Values)** \\ \hline PSNR & 0.7893 (0.000059) & 0.8938 (0.001219) & 0.5119 (0.005339) \\ SSIM [1] & 0.8080 (0.000672) & 0.7821 (0.000021) & 0.8348 (0.000059) \\ BIQI [14] & 0.8600 (0.015124) & 0.6603 (0.000482) & 1.0738 (0.000041) \\ BRISQUE [4] & 0.8239 (0.005389) & 0.9009 (0.009336) & 0.4960 (0.010058) \\ TIP [2] & 0.8847 (0.012782) & 0.8847 (0.007216) & 1.1021 (0.001250) \\ NIQE [17] & 0.8638 (0.010644) & 0.8994 (0.008812) & 0.4990 (0.001229) \\ BLIND-II [3] & 0.8715 (0.000956) & 0.8246 (0.000372) & 0.6458 (0.006883) \\ NR-CSR [18] & **0.9239** (-) & 0.8775 (-) & 0.5478 (-) \\
**NSSIM** & 0.8886 & **0.9239** & **0.4367** \\ \hline \end{tabular}
\end{table}