# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Database Criteria** & **SSIM** & **VIF** & **MAD** & **FSIMc** & **GMSD** & **VSI** & **GSM** & **RVSIM** & **CVSS** & **CAGS** & **Proposed** \\ \hline \multirow{4}{*}{TID2008} & SROCC & 0.7417 & 0.6769 & 0.7807 & **0.8510** & 0.8044 & **0.8965** & 0.7946 & 0.6757 & 0.8069 & 0.8316 & **0.8606** \\  & KROCC & 0.5588 & 0.5147 & 0.6035 & **0.6665** & 0.6339 & **0.7183** & 0.6255 & 0.5146 & 0.6331 & 0.6469 & **0.6810** \\  & PLCC & 0.7895 & 0.7720 & 0.8267 & **0.8769** & 0.8590 & **0.9000** & 0.8464 & 0.7825 & 0.8406 & 0.8445 & **0.8822** \\  & RMSE & 0.7608 & 0.7880 & 0.6975 & **0.5959** & 0.6346 & **0.5404** & 0.6603 & 0.7719 & 0.6715 & 0.6639 & 0.5838 \\ \cline{2-11}  & SROCC & 0.7749 & 0.7491 & 0.8340 & 0.8840 & 0.8907 & **0.8979** & 0.8504 & 0.7375 & **0.9001** & 0.8231 & **0.9048** \\  & KROCC & 0.5768 & 0.5860 & 0.6445 & 0.6991 & 0.7092 & **0.7123** & 0.6596 & 0.5628 & **0.7215** & 0.6289 & **0.7278** \\  & PLCC & 0.7732 & 0.8084 & 0.8308 & 0.8762 & **0.8788** & 0.8762 & 0.8422 & 0.7954 & **0.8961** & 0.8091 & **0.9000** \\  & RMSE & 0.8511 & 0.7899 & 0.7468 & 0.6468 & **0.6404** & 0.6466 & 0.7235 & 0.8133 & **0.5956** & 0.7886 & **0.5850** \\ \cline{2-11}  & SROCC & 0.8756 & 0.9195 & 0.9466 & 0.9310 & **0.9570** & 0.9423 & 0.9108 & 0.8979 & **0.9580** & 0.9198 & **0.9570** \\  & KROCC & 0.6907 & 0.7537 & 0.7970 & 0.7690 & **0.764** & 0.7857 & 0.7374 & 0.7234 & **0.8173** & 0.7487 & **0.8127** \\  & PLCC & 0.8613 & 0.9277 & 0.9502 & 0.9192 & **0.930** & 0.9279 & 0.9894 & 0.9236 & **0.9589** & 0.9014 & **0.9611** \\  & RMSE & 0.1334 & 0.0980 & 0.0818 & 0.1034 & **0.0786** & 0.0979 & 0.1164 & 0.1007 & **0.0745** & 0.1137 & **0.0725** \\ \cline{2-11}  & SROCC & 0.9479 & 0.9636 & 0.9669 & 0.9645 & 0.9603 & 0.9524 & 0.9561 & 0.9600 & **0.9672** & **0.9734** & **0.9798** \\  & KROCC & 0.7963 & 0.8282 & **0.8421** & 0.8363 & 0.8268 & 0.8058 & 0.8150 & 0.8203 & 0.8406 & **0.8658** & **0.8863** \\  & PLCC & 0.9449 & 0.9604 & **0.9675** & 0.9613 & 0.9595 & 0.9482 & 0.9512 & 0.9570 & **0.9651** & 0.9640 & **0.9754** \\  & RMSE & 8.9455 & 7.6137 & **6.9073** & 7.5296 & 7.6937 & 8.6816 & 8.4327 & 7.9274 & **7.1573** & 8.3251 & **6.9051** \\ \hline \multirow{4}{*}{Weighted Average} & SROCC & 0.7942 & 0.7646 & 0.8405 & **0.8847** & 0.8544 & **0.9100** & 0.8452 & 0.7638 & 0.8721 & 0.8588 & **0.9027** \\  & KROCC & 0.6108 & 0.6049 & 0.6702 & **0.7101** & 0.7022 & **0.7366** & 0.6732 & 0.6006 & 0.7074 & 0.6828 & **0.7414** \\ \cline{1-1}  & PLCC & 0.8140 & 0.8261 & 0.8619 & **0.8928** & 0.8892 & **0.9033** & 0.8650 & 0.8307 & 0.8869 & 0.8575 & **0.9112** \\ \hline \multirow{4}{*}{Direct Average} & SROCC & 0.8350 & 0.8273 & 0.8821 & 0.9076 & 0.9031 & **0.9223** & 0.8780 & 0.8178 & **0.9081** & 0.8870 & **0.9256** \\ \cline{1-1}  & KROCC & 0.6557 & 0.6707 & 0.7218 & 0.7427 & 0.7457 & **0.7555** & 0.7094 & 0.6553 & **0.7531** & 0.7226 & **0.7770** \\ \cline{1-1}  & PLCC & 0.8422 & 0.8671 & 0.8938 & 0.9084 & 0.9127 & **0.9131** & 0.8841 & 0.8646 & **0.9152** & 0.8798 & **0.9297** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of IQA methods on four databases. In Table 4, the top three ranked computational methods based on the total marked count are DeepFR (8 times), MVFC method (7 times), and DoG-ssim (6 times), and their overall performances rank second. As seen from the data, the MVFC method exhibits a high prediction performance. It can be concluded that the MVFC method performs with a higher generalization in the commonly used public image databases. To further demonstrate the performance advantages of the MVFC method, the fitting performance of different methods is compared using the discrete points provided in the TID2013 database. As shown in Figure 6, the regression curve of the MVFC method has a better correlation with the subjective observation values. The MVFC integrates HVS characteristics and simultaneously extracts multi-channel LoG filtering, contrast information, and chroma information.