# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method
Using the same method, \(T_{1}\) is set in the interval [0.00003, 0.00008], and when the \(T_{1}\) value is in [0.00004, 0.00006], the PLCC value of the MVFC in each database is the most ideal, and \(T_{1}\) = 0.00006 is chosen. The selection of the numerical values of \(w_{1}\), \(w_{2}\), and \(w_{3}\) is consistent with the above method. Since the values of \(T_{1}\) and \(T_{2}\) have been given and \(w_{1}+w_{2}+w_{3}\) = 1, it is only necessary to determine the values of two variables. As shown in Figure 5, keeping the value of \(w_{3}\) unchanged, when the value of \(w_{1}\) is in the interval [0.44, 0.54], the PLCC value of the MVFC in each database is the most ideal, and \(w_{1}\) = 0.5 is chosen. Similarly, keeping the value of w1 unchanged, when the value of \(w_{3}\) is in the interval [0.41, 0.46], the PLCC value of the MVFC is the most ideal, and \(w_{3}\) = 0.45 is selected. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Source Images** & **Distorted Images** & **Distortion Types** & **Observers** \\ \hline TID2013 & 25 & 3000 & 24 & 971 \\ TID2008 & 25 & 1700 & 17 & 838 \\ CSIQ & 30 & 866 & 6 & 35 \\ LIVE & 29 & 779 & 5 & 161 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IQA benchmark database. Figure 4: PLCC of different \(T_{1}\) and \(T_{2}\) values on four benchmark databases. Figure 5: PLCC of different \(w_{1}\) and \(w_{2}\) values on four benchmark databases. ### Overall Performance Comparison

MVFC method extracts three feature information of the image, namely multi-channel LoG filtering, contrast, and chroma. To demonstrate the performance advantages of the MVFC method, 10 classic algorithm methods are selected for comparison, including SSIM [9], CVSS [12], GSM [13], VIF [14], FSIMc [16], and VSI [17], as well as high-precision methods--RVSIM [39], MAD [40]--high-efficiency methods--GMSD [41]--and recently published methods--CAGS [42]. As shown in Table 3, the top three evaluation results are highlighted in bold. In addition, the evaluation results in the four databases are weighted averaged and directly averaged, where the weight of each database is determined by the number of distorted images it contains. As seen from Table 3, MVFC method has good performance in all four databases, and its overall performance ranks first. Especially in the LIVE database, the performance evaluation standard value has a significant advantage compared with the second and third places. Compared with FSIMc and VSI, which also use LoG filtering to extract image structure information, MVFC method has more obvious advantages in the LIVE and CSIQ databases. In the weighted average and direct average values of the first three indicators, the performance of MVFC method ranks first. In addition, in the TID2008 database, the SROCC value of MVFC method is 0.9048, ranking first among the 11 calculation methods. In summary, the MVFC method in this paper has good competitiveness and prediction accuracy. The methods with the top three places in Table 3 are the MVFC method in this paper (22 times), CVSS (14 times), and VSI (12 times). Moreover, it can be seen from Table 3 that the SROCC and PLCC values of the MVFC method are both greater than 0.8606, indicating that the method not only has a good predictive performance, but also has a better generalization ability. Then, we will also compare the MVFC method with five learning-based or AI-based IQA methods, namely DoG-ssim, dipIQ, Deepsim, DeepFR, and DIQA. Among them, with the SROCC and PLCC values chosen as the performance evaluation criteria. The results are shown in Table 4, with the top three marked in bold.