# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method
From the above data, it can be seen that the currently more challenging distortion type evaluations are CTC (contrast change) and block (different intensity local block distortions). The MVFC method outperforms CVSS in the evaluation of these two distortion types and performs significantly better than VSI and CVSS in block evaluation. Therefore, the generalization performance of MVFC method is good, both on the TID2013 database and across all databases. To further validate MVFC method's performance in evaluating individual distortion types, a comparison was conducted with five recently published journal articles, namely DISIS [43], SG-ESSIM [44], and VSPSS [45]. LGV and SWLGV [46] were tested on the TID2013 database, with the SROCC value chosen as the performance evaluation criterion. The results are shown in Table 5, with the top two marked in bold. In Table 6, the top three ranked computational methods based on the total marked count are VSPSS (20 times), the MVFC method (17 times), and SG-ESSIM (14 times). As seen from the data, the MVFC method exhibits a high prediction performance. Upon further analysis of the data, MVFC method's average SROCC value across all 24 distortion types is 0.8531, which is very close to the computational methods VSPSS and SWLGV and is significantly higher than the remaining three computational methods. These results also indicate that the MVFC method has good generalizability and strong competitiveness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **Type** & **DISTS** & **SG-ESSIM** & **VSPSS** & **LGV** & **SWLGV** & **Proposed** \\ \hline  & AGN & 0.8450 & **0.9360** & **0.9471** & 0.9210 & **0.9360** & **0.9500** \\  & ANC & 0.7860 & 0.8550 & 0.8722 & **0.9110** & **0.9040** & **0.8859** \\  & SCN & 0.8590 & **0.9350** & **0.9385** & 0.8870 & 0.9300 & **0.9438** \\  & MN & **0.8140** & 0.7150 & 0.7712 & **0.8210** & **0.8420** & 0.7480 \\  & HFN & 0.8680 & **0.9200** & **0.9218** & 0.9160 & 0.8740 & **0.9211** \\  & IN & 0.6740 & **0.8330** & **0.8758** & 0.7590 & 0.7950 & **0.8082** \\  & QN & 0.8100 & **0.9110** & 0.8765 & 0.8660 & **0.9560** & **0.9063** \\  & GB & 0.9260 & **0.9690** & **0.9631** & 0.9520 & **0.9610** & 0.9074 \\  & DEN & 0.8990 & **0.9630** & 0.9502 & **0.9840** & **0.9760** & 0.9592 \\  & JPEG & 0.8970 & 0.9500 & **0.9650** & **0.9700** & 0.9520 & **0.9532** \\  & JP2K & 0.9310 & 0.9490 & **0.9725** & 0.9450 & **0.9680** & **0.9625** \\  & JGTE & **0.9060** & 0.8230 & **0.9234** & 0.8950 & **0.9000** & 0.8804 \\ TID2013 & J2 TE & 0.8650 & **0.8990** & **0.9246** & **0.9180** & 0.8740 & 0.8960 \\  & NEPN & **0.8330** & 0.8010 & **0.8076** & 0.7190 & 0.7950 & **0.8140** \\  & Block & 0.3020 & **0.6230** & 0.1716 & **0.6030** & 0.6010 & **0.6427** \\  & MS & **0.7520** & 0.7060 & **0.7715** & 0.6770 & **0.7560** & 0.6566 \\  & CTC & 0.4640 & 0.4520 & **0.4763** & **0.6590** & **0.6670** & 0.3666 \\  & CCS & **0.7890** & 0.0100 & **0.8116** & 0.7500 & **0.7580** & 0.7350 \\  & MGN & 0.7900 & **0.9000** & **0.9135** & 0.8190 & 0.8410 & **0.8903** \\  & CN & 0.9070 & **0.9160** & **0.9231** & 0.8380 & 0.8590 & **0.9356** \\  & LCNI & 0.9320 & **0.9520** & **0.9583** & 0.8730 & 0.9170 & **0.9754** \\  & ICQD & 0.8320 & **0.9280** & **0.8856** & 0.8450 & 0.8640 & **0.9261** \\  & CHA & **0.8790** & 0.8350 & **0.8923** & 0.7930 & 0.7880 & **0.8396** \\  & SSR & 0.9440 & **0.9640** & **0.9632** & 0.8000 & 0.8100 & **0.9695** \\ \hline  & Average & 0.8127 & 0.8227 & **0.8532** & 0.8384 & **0.8552** & **0.8531** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SROCC values of IQA methods for each type of distortion. ### Comparison of Method Computational Efficiency

To compare the computational complexity of the method proposed in this study with that of the other seven methods, a pair of color images with a resolution of 512 \(\times\) 512 from the TID2008 database were selected as test images. These were run on a computer with a 2.3 GHz Intel Core i5 memory, using MATLAB R2016a as the simulation software.