# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 7 Results and Discussion
### Experiments and Relate Analysis of Image Quality Evaluation
#### 7.2.3 Database Independence  
Since ROI-BRISQUE algorithm is evaluated only on the LIVE IQA database2, we cannot make sure that the performance of ROI-BRISQUE maintains its superiority in other IQA database. To show this, we apply the ROI-BRISQUE to the alternate TID2013 database [52]. The TID2013 database contains 25 reference images and 3000 distorted images (25 reference images \(\times\) 24 types of distortions \(\times\) 5 levels of distortions). Further, although it has 24 distortion categories, we test the ROI-BRISQUE only on these categories which has been trained for JPEG, JP2k (JPEG2000 compression), BLUR (Gaussian Blur), and WN (Additive white noise). TID2013 database does not contain FF (Fast Fading) distortion. The results of testing on TID2013 database are shown in Table 7. Compared with PSNR, SSIM, BLIINDS-II, DIIVINE and BRISQUE, the correlation with subjective perception of ROI-BRISQUE remained consistently competitive.  
In addition, to study whether the algorithm is database dependent, we also test ROI-BRISQUE on a portion of the CSIQ image database [53]. The database consists of 30 original images, each distorted using one of six types of distortions, each at four to five different levels of distortion. The database contains 5000 subjective ratings from 35 different observers, and the ratings are reported in the form of DMOS (the value is in the range [0, 1], where 1 denotes the lowest quality). The distortions used in CSIQ are: JPEG compression, JPEG2000 compression, global contrast decrements, additive pink Gaussian noise, and Gaussian blurring. In total, there are 866 distorted images. We test ROI-BRISQUE on the CSIQ database over 4 distortion categories in common with the LIVE database: JPEG, JPEG2000, Gaussian noise, and Gaussian blurring. The experimental results are reported in Table 8. It is obvious that the performance of ROI-BRISQUE does not depend on the database. The correlations are still consistently high.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **PSNR** & **SSIM** & **BLIINDS-II** & **DIIVINE** & **BRISQUE** & **ROI-BRISQUE** \\ \hline
**PSNR** & 0 & \(-1\) & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\
**SSIM** & 1 & 0 & 1 & \(-1\) & \(-1\) & \(-1\) \\
**BLIINDS-II** & 1 & \(-1\) & \(0\) & \(-1\) & \(-1\) & \(-1\) \\
**DIIVINE** & 1 & 1 & 1 & \(0\) & \(-1\) & \(-1\) \\
**BRISQUE** & 1 & 1 & 1 & 1 & \(0\) & \(-1\) \\
**ROI-BRISQUE** & 1 & 1 & 1 & 1 & \(0\) & \(-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of one-sided t-test performed between SROCC values of various IQA algorithms. The value of “1” indicates that the row algorithm is statically superior to the column algorithm; “\(-1\)” indicates that the row is inferior to the column; “\(0\)” indicates that the row and the column methods are equivalent. Italics indicate No-reference algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **JP2K** & **JPEG** & **WN** & **BLUR** & **All** \\ \hline
**PSNR** & 0.8250 & 0.8760 & 0.9180 & 0.9342 & 0.8700 \\
**SSIM** & 0.9630 & 0.9354 & 0.8168 & 0.9600 & 0.9016 \\
**BLIINDS-II** & _0.9157_ & _0.8901_ & _0.6600_ & _0.8500_ & _0.8442_ \\
**DIIVINE** & _0.9240_ & _0.8660_ & _0.8510_ & _0.8620_ & _0.8890_ \\
**BRISQUE** & _0.8320_ & _0.9240_ & _0.8290_ & _0.8810_ & _0.8960_ \\
**ROI-BRISQUE** & _0.8534_ & _0.9276_ & _0.8488_ & _0.8965_ & _0.8993_ \\ \hline \hline \end{tabular}
\end{table}
Table 7: SROCC results obtained by training on the LIVE IQA database and testing on the TID2013 database. Italicized algorithms are NR IQA algorithms, others are FR IQA algorithms.