# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Ablation Experiment  
#### 4.3.1 Cross-Database Validation and Hypothesis Testing  
At the same time, in order to illustrate the generalization capability of TLLFD method and prevent interference from over-fitting experiments, cross database verification experiments are carried out. In order to make a fair comparison, in Table 5, all models are validated on the full LIVE (779) database and tested on TID2008, CSIQ, and MICT. In Table 6, the NR IQA model was trained for image from CSIQ (600) database and tested on three other databases. The two best NR IQA models indexes are shown in bold. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Database** & **NIQE** & **ILNIQE** & **BIQI** & **DIIVINE** & **BLINDS2** & **BRISQUE** & **GMLOG** & **NFERM** & **TLLFD** \\ \hline TID2008 & 0.795 & 0.870 & 0.813 & 0.867 & 0.864 & 0.894 & 0.911 & **0.914** & **0.915** \\ MICT & 0.811 & 0.711 & 0.663 & 0.798 & 0.810 & **0.857** & 0.835 & 0.851 & **0.889** \\ CSIQ & 0.869 & 0.880 & 0.785 & 0.877 & 0.902 & 0.890 & 0.899 & **0.907** & **0.928** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SROCC comparison on cross-database validation when NR IQA models are trained on LIVE (the two best models indexes are shown in bold). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Database** & **D-TY** & **NIQE** & **ILN-** & **BIQI** & **DIIV-** & **BLIN-** & **GM** & **OG** & **Dip** & **Deep** & **MEON** & **TLLFD** \\ \hline \multirow{4}{*}{LIVE} & JP2K & 0.924 & 0.900 & 0.824 & 0.906 & 0.931 & 0.926 & **0.937** & - & - & - & **0.950** \\  & JPEG & 0.942 & 0.944 & 0.884 & 0.897 & 0.950 & 0.963 & **0.964** & - & - & - & **0.962** \\  & WN & 0.972 & 0.979 & 0.965 & 0.982 & 0.946 & 0.983 & **0.987** & - & - & - & **0.987** \\  & GB & 0.940 & 0.924 & 0.856 & 0.934 & 0.915 & 0.920 & **0.961** & - & - & - & **0.958** \\  & FF & 0.862 & 0.844 & 0.743 & 0.854 & 0.875 & 0.901 & 0.899 & - & - & - & **0.907** \\ \hline \multirow{4}{*}{TID2008} & JP2K & 0.902 & **0.937** & 0.855 & 0.895 & 0.902 & **0.935** & 0.926 & - & - & - & **0.935** \\  & JPEG & 0.887 & 0.887 & 0.887 & 0.887 & 0.887 & 0.884 & **0.934** & - & - & - & **0.931** \\  & WN & 0.817 & 0.883 & 0.756 & 0.840 & 0.685 & 0.891 & **0.907** & - & - & - & **0.904** \\  & GB & 0.847 & 0.860 & **0.899** & 0.890 & 0.857 & 0.886 & 0.881 & - & - & - & **0.929** \\ \hline \multirow{4}{*}{CSIQ} & JP2K & 0.911 & 0.796 & 0.818 & 0.871 & 0.879 & 0.918 & 0.917 & **0.944** & 0.907 & 0.898 & **0.925** \\  & JPEG & 0.913 & 0.828 & 0.859 & 0.883 & 0.895 & 0.917 & 0.933 & 0.936 & 0.929 & **0.948** & **0.942** \\  & WN & 0.925 & 0.924 & 0.850 & 0.901 & 0.868 & 0.946 & 0.941 & 0.904 & 0.933 & **0.951** & **0.949** \\  & GB & 0.883 & 0.905 & 0.844 & 0.895 & 0.883 & 0.915 & 0.907 & **0.932** & 0.890 & 0.918 & **0.936** \\ \hline \multirow{4}{*}{MICT} & JP2K & 0.836 & 0.868 & 0.660 & 0.851 & **0.894** & 0.887 & - & - & - & - & **0.988** \\  & JPEG & 0.906 & 0.868 & 0.690 & 0.755 & 0.873 & **0.941** & - & - & - & - & **0.986** \\ \hline \multicolumn{2}{c}{Weighted average} & 0.902 & 0.894 & 0.838 & 0.891 & 0.886 & **0.923** & 0.852 & 0.929 & 0.915 & 0.929 & **0.944** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SROCC comparisons of 12 NR IQA models on individual distortion types (the two best NR IQA models indexes are shown in bold). Tables 6 and 7 show the cross database experiment results respectively. Table 6 is the result of taking the LIVE data as the training set and carrying out the experiment in other databases. Table 7 shows the results of experiments with CSIQ data as training set and other databases. From Table 6, we can find that the model trained by LIVE database has better results for other databases, and the TLLFD method is due to the comparison method in the other three databases. From Table 7, we can find that the model trained by CSIQ database has more general results for other databases, but the overall results are good.