# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method
% Multi-channel LoG feature map extraction and similarity calculation. (steps 14-19) 14: L\({}_{\text{H}}\)DoG \(\leftarrow\) imgDoG(L\({}_{\text{H}}\)). 15: M\({}_{\text{H}}\)DoG \(\leftarrow\) imgDoG(M\({}_{\text{H}}\)). 16: N\({}_{\text{H}}\)DoG \(\leftarrow\) imgDoG(N\({}_{\text{H}}\)). 17: DoG\({}_{\text{R}}\)\(\leftarrow\) sqrt(L\({}_{\text{R}}\)DoG\({}^{2}\) + M\({}_{\text{R}}\)DoG\({}^{2}\) + N\({}_{\text{R}}\)DoG\({}^{2}\)). 18: DoG\({}_{\text{D}}\)\(\leftarrow\) sqrt(L\({}_{\text{D}}\)DoG\({}^{2}\) + M\({}_{\text{D}}\)DoG\({}^{2}\) + N\({}_{\text{D}}\)DoG\({}^{2}\)). 19: SIM\({}_{\text{LOG}}\)\(\leftarrow\) abs ((2\(\times\)DoG\({}_{\text{R}}\)\(\times\) DoG\({}_{\text{D}}\) + T1)/((DoG\({}_{\text{R}}\))\({}^{2}\) + (DoG\({}_{\text{D}}\))\({}^{2}\) + T1)). % Calculate the quality score of D. (steps 20-21) 20: Output \(\leftarrow\) 0.5 \(\times\) std2(SIM\({}_{\text{LOG}}\)) + 0.05 \(\times\) std2(SIM\({}_{\text{MN}}\)) + 0.45 \(\times\) std2(SIM\({}_{\text{LC}}\)). 21: end
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**Algorithm 1** MVFC. Figure 2: (**a,d**) are original images, while (**b,c,e,f**) are distorted images corresponding to different distortion levels of a and d. Their subjective scores are 4.0571, 3.0000, 5.1935, and 4.0645, respectively. And their objective scores are 0.0931, 0.1720, 0.0518, and 0.1177, respectively. ## 3 Experimental Results and Analysis

### Evaluation Metrics and Protocol

The performance of an FR-IQA method is given by correlation coefficients in the literature, which are measured between predicted and ground-truth quality scores. Four commonly used correlation coefficients are the [PERSON] rank-order correlation coefficient (SROCC), [PERSON] rank-order correlation coefficient (KROCC), Pearson linear correlation coefficient (PLCC), and root-mean-squared error (RMSE). The first two can characterize the prediction trend of the IQA method, while PLCC reflects the prediction accuracy of the method, and RMSE reflects the deviation of the method prediction. To calculate the last two indicators, the logistic regression analysis method [17] is adopted, and its mapping function is:

\[f(s)=\beta_{1}\bigg{[}\frac{1}{2}-\frac{1}{1+\exp(\beta_{2}(s-\beta_{3}))} \bigg{]}+\beta_{4}s+\beta_{5} \tag{10}\]

where \(\beta_{1}\), \(\ldots\), \(\beta_{5}\) are fitting parameters, \(s\) represents the original IQA score, and \(f\)(s) is the IQA score after regression. ### Databases

Currently, the four commonly used public image databases in the literature are TID2008 [35], CSIQ [36], LIVE [37], and TID2013 [38]. These databases contain many distortion types, rich content information, and noticeable pattern textures. Each image in these databases includes a corresponding mean opinion score (MOS) or differential mean opinion score (DMOS), which is suitable for conducting method-performance testing experiments. Table 2 lists the basic information of the databases, including a total of 6345 distorted images and 52 distortion types. Figure 3: The analytical flow of the MVFC. ### Parameter Optimization of the Method

As mentioned earlier, the parameter variables of the MVFC are \(T_{1}\), \(T_{2}\), \(w_{1}\), \(w_{2}\), and \(w_{3}\). To achieve the optimal performance of the method, it is necessary to determine the ideal variable values. In this research, the method of controlling variables is chosen to calculate the variable values, and the final target value is determined by the corresponding evaluation standard value (PLCC). First, the values of \(T_{1}\), \(w_{1}\), \(w_{2}\), and \(w_{3}\) are kept unchanged, and the value of \(T_{2}\) is set in the interval [20; 70]. If \(T_{2}\) is too large, it will weaken the feature representation, and if it is too small, it will reduce the calculation stability. As shown in Figure 4, when the \(T_{2}\) value is in the interval [40; 50], the PLCC value in the four databases is the most ideal. Here, \(T_{2}\) = 50 is selected.