# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Experimental Results and Analysis
#### 4.2.2 Performance on Individual Distortion Types  
This section evaluates the performance of NR IQA models on individual distortion types. For NR IQA models, 80% of the five distorted images are used to train the NR IQA model, and 20% of the distorted images with specific distortion types are tested. The SROCC comparison for the 12 NR IQA models in the four benchmark databases is listed in Table 4; the best two NR IQA models for each distortion group are shown in boldface. In Table 5, we can find that from the results of single distortion type, TLLFD method is better than most methods. For example, according to the experimental results of GB distortion type in CSIQ database, TLLFD method is better than all methods; however, in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**IQA**} & \multicolumn{3}{c}{**LIVE (79)**} & \multicolumn{3}{c}{**TID2008 (384)**} & \multicolumn{3}{c}{**CSIQ (600)**} & \multicolumn{3}{c}{**MICT (168)**} \\ \cline{2-9}  & **SROCC** & **PLCC** & **SROCC** & **PLCC** & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline PSNR & 0.885 & 0.883 & 0.879 & 0.860 & 0.929 & 0.854 & 0.659 & 0.679 \\ SSIM & 0.948 & 0.945 & 0.910 & 0.941 & 0.924 & 0.933 & **0.915** & **0.920** \\ FSIM & **0.963** & **0.960** & **0.953** & 0.929 & 0.924 & 0.912 & 0.906 & 0.800 \\ VSI & 0.952 & 0.948 & 0.940 & 0.923 & **0.942** & 0.928 & 0.866 & 0.736 \\ \hline BIQI & 0.852 & 0.866 & 0.802 & 0.852 & 0.820 & 0.874 & 0.574 & 0.599 \\ DIIVINE & 0.909 & 0.909 & 0.897 & 0.903 & 0.880 & 0.899 & 0.641 & 0.680 \\ BLINDS2 & 0.931 & 0.937 & 0.866 & 0.906 & 0.869 & 0.912 & 0.851 & 0.875 \\ CORNIA & 0.945 & 0.947 & 0.897 & 0.931 & 0.893 & 0.929 & 0.901 & 0.918 \\ BRISQUE & 0.944 & 0.948 & 0.905 & 0.926 & 0.914 & 0.940 & 0.883 & 0.902 \\ GMLOG & 0.950 & 0.954 & 0.937 & **0.945** & 0.923 & 0.951 & 0.885 & 0.888 \\ NFERM & 0.944 & 0.949 & 0.940 & **0.951** & 0.929 & **0.953** & 0.887 & 0.892 \\ \hline OG IQA & 0.951 & 0.955 & 0.937 & 0.941 & 0.924 & 0.946 & - & - \\ CNN & 0.956 & 0.953 & - & - & - & - & - & - \\ Dip IQA & - & - & - & - & 0.930 & 0.949 & - & - \\ Deep IQA & - & - & - & - & 0.871 & 0.891 & - & - \\ MEON & - & - & - & - & 0.932 & 0.944 & - & - \\ \hline TLLFD & **0.958** & **0.960** & **0.940** & 0.944 & **0.939** & **0.953** & **0.919** & **0.925** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: SROCC and PLCC comparison of 15 IQA models on four benchmark databases (the two best models indexes are shown in bold). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**IQA Model**} & \multicolumn{4}{c}{**Live (799)**} \\ \cline{2-5}  & **SROCC** & **KROCC** & **PLCC** & **RMSE** & **Dimensions** \\ \hline \(512\times 512\) & 0.949 & 0.811 & 0.951 & 8.358 & 22 \\ \hline \(512\times 512\) & 0.958 & 0.828 & 0.960 & 7.577 & 44 \\ \hline \(512\times 512\) & & & & & \\ \(256\times 256\) & 0.956 & 0.825 & 0.959 & 7.739 & 66 \\ \(128\times 128\) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Image scale influence on quality assessment. other databases, it is not the optimal value, but it is also the suboptimal value. Finally, from the overall weighted average, TLLFD method maintains the optimal value. It should be noted that similar results can be obtained for KROCC, PLCC, and RMSE indicators; only SROCC indicators are listed here. Moreover, the last row of Table 4 lists the weighted average SROCC values of all distortion types, where the weights are the number of images in each distortion group. The quality prediction accuracy of TLLFD is high under individual distortion types on LIVE, TID2008, CSIQ, MICT. For the JPEG2000, JPEG, and FF distortion, it performs slightly worse. For the WN, GB distortion, it outperforms all other NR IQA methods. To sum up, there are the three main reasons for the improvement of performance. First, different distortion types have different CLBP maps, it can effectively measure the influence of different distortion types on image structure change.