# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method
As shown in Table 7, the method in this study only requires a runtime of 0.14 s, demonstrating a relatively low computational complexity. In practical applications, this method can quickly accomplish relevant tasks. In the comparison of the overall performance in Section 3.4, the MVFC method ranks first. This is because the MVFC method combines the HVS characteristics, that is, the multi-channel structural information, by LoG filtering, and then combines with contrast information and chromaticity information, and the deviation and characteristic difference are used to characterize. The main limitation of this research is to deal with the different distortions in Section 3.5. At present, the IQA method mentioned in this paper does not have a method that can obtain a higher evaluation performance in all types of distortion evaluation. The main reason is that the preprocessing method, extraction method, and pooling method of image features are all different. From Tables 5 and 6, we can conclude that each IQA method has no means to solve all the distortion type evaluation, which is the main deficiency of current IQA research. Hence, all researchers should pay more attention to solve all the distortion types' evaluation in future IQA research. ## 4 Conclusions

In accordance with the high precision and high efficiency requirements of modern FR-IQA methods, this paper proposes a full-reference method (MVFC) based on the fusion of multi-channel visual information from color images. Image structural information is extracted using the multi-channel LoG algorithm and it is pooled with chroma and contrast features using the standard deviation method. The parameters of MVFC are obtained through the control variable method, ultimately resulting in the computational method of this study. During the experimental phase, four classic FR-IQA databases were selected, totaling 109 reference images and 6345 distorted images for testing. The results were compared with those from ten classic FR-IQA methods and five learning-based methods. The experimental results indicate that in terms of the performance evaluation standard of PLCC, the minimum value of this method across the four databases is 0.8822, with the maximum reaching 0.9754. Its stability and precision are superior to most comparison methods. In single-distortion type tests, the highest PLCC value of this method can reach up to 0.9912, with the lowest at 0.5466. Its generalization and prediction performance also outperform most FR-IQA methods. By comparing the computational complexity with seven other methods, the higher computational efficiency of this method is verified. In conclusion, the proposed FR-IQA method in this study demonstrates a stable algorithmic performance. In the future, all IQA methods need to be improved to yield a better performance for the IQA problem in real settings, including the MVFC method. Conceptualization, B.J. and C.S. ; methodology, S.B. ; software, S.B. ; validation, B.J., C.S. and L.W. ; formal analysis, S.B. ; investigation, S.B. ; resources, S.B. ; data curation, S.B. ; writing--original draft preparation, S.B. ; writing--review and editing, B.J. ; visualization, S.B. ; super \begin{table}
\begin{tabular}{c c c c} \hline \hline
**IQA Index** & **Time Cost/s** & **IQA Index** & **Time Cost/s** \\ \hline SSIM & 0.126 & VSI & 0.536 \\ GSM & 0.271 & RVSIM & 0.939 \\ CVSS & 0.436 & MAD & 1.256 \\ CAGS & 0.483 & Proposed & 0.148 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the efficiency of different IQA methods. vision, C.S. ; project administration, B.J. ; funding acquisition, B.J., C.S. and L.W. All authors have read and agreed to the published version of the manuscript.