# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Performance on Individual Distortion Types  
To fully test the proposed algorithm, we also compared the performance of the competing BIQA algorithms on individual distortion types in LIVE and CSIQ databases. The same train-test procedures as in the previous experiments were conducted. The median SROCC values across the 1000 train-test trials are listed in Table 4. It is clear that most of the competing algorithms could achieve good evaluation accuracy on individual distortion types in LIVE database. However, only BIQA-SC and BIQA-SC-II could obtain relatively high prediction accuracy on each distortion type in CSIQ database. Compared with BRISQUE [15], which only utilizes NSS features extracted from a single NFM, BIQA-SC-I and BIQA-SC-II had significantly improved prediction accuracy on six types of distortions in CSIQ database. This implies that statistics extracted from multiple NFMs and DFMs are more sensitive to the distortion changes than statistics obtained from a single NFM. The main reason is that measuring the relationship between coefficients in receptive fields of different sizes could get more detailed information indicating the changes of the surrounding world. Moreover, BIQA-SC achieved the best performance on individual distortion types in CSIQ database. This implies that using color features extracted from HSV color space can improve the evaluation accuracy of contrast distorted images. Figure 8: Scatter plots and fitted lines of the DMOS values versus the predicted scores by the competing algorithms on LIVE database [28]. Figure 7: Box plots of SROCC and PLCC distributions of BLIINDS-II [13] (BL), DIIVINE [12] (DI), BRISQUE [15] (BR), GMLOG [19] (GM), BIQA-SC-I (SC-I), BIQA-SC-II (SC-II), and BIQA-SC (SC) across 1000 train-test trials on LIVE database [28]: (**a**) box plot of SROCC distributions; and (**b**) box plot of PLCC distributions. To further test the generalization ability of the proposed algorithms, we also conducted experiments on the 24 distortion types in TID2013 database. The bar plots in Figure 9 correspond to the median SROCC values of 1000 train-test trials on TID2013 database. It is clear that BIQA-SC achieved promising results on most commonly encountered distortion types. For several special distortion types, such as local block-wise distortion (LBD), contrast change (CC), change of color saturation (CCS), and comfort noise (CN), BIQA-SC still achieved satisfying results and outperformed state-of-the-art algorithms. This indicates that the proposed method is capable of evaluating the image quality for various distortion types. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**LIVE**} & \multicolumn{6}{c}{**CSIQ**} \\ \cline{2-13}  & **JP2K** & **JPEG** & **WN** & **GB** & **FF** & **WN** & **JPEG** & **JP2K** & **PGN** & **GB** & **GCD** \\ \hline DIIVINE [12] & 0.911 & 0.911 & 0.977 & 0.955 & 0.911 & 0.909 & 0.883 & 0.883 & **0.941** & 0.889 & 0.886 \\ BLIINDS-II [13] & 0.916 & 0.886 & 0.959 & 0.910 & 0.834 & 0.776 & 0.778 & 0.839 & 0.828 & 0.882 & 0.685 \\ BRISQUE [15] & 0.913 & 0.908 & 0.973 & 0.960 & 0.869 & 0.861 & 0.850 & 0.834 & 0.868 & 0.863 & 0.447 \\ ILNIQE [17] & 0.872 & 0.863 & 0.980 & 0.916 & 0.833 & 0.851 & 0.875 & 0.894 & 0.877 & 0.830 & 0.518 \\ GMLOG [19] & 0.917 & **0.930** & 0.981 & 0.934 & 0.918 & 0.704 & 0.864 & 0.878 & 0.881 & 0.872 & 0.833 \\ BIQA-SC-1 & 0.928 & **0.918** & 0.982 & 0.967 & 0.908 & **0.934** & **0.891** & 0.849 & 0.926 & 0.907 & 0.886 \\ BIQA-SC-II & **0.939** & 0.911 & **0.983** & **0.966** & **0.915** & 0.931 & 0.888 & **0.908** & 0.939 & **0.916** & **0.919** \\ BIQA-SC & **0.938** & 0.913 & **0.985** & **0.973** & **0.915** & **0.943** & **0.911** & **0.924** & **0.967** & **0.940** & **0.950** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Median SROCC comparison of the competing algorithms on individual distortion types in LIVE [28] and CSIQ [29] databases.