# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### MVFC Method  
Based on the quantification results of multi-channel LoG filtering, contrast, and chromaticity, \(SIM_{\text{LoG}}\), \(SIM_{\text{LC}}\), and \(SIM_{\text{MN}}\) feature similarity images can be obtained. By pooling the obtained feature similarities in space, an IQA method can be obtained. The index of the proposed method can be calculated using Equation (9):  
\[S=w_{1}\cdot SD(SIM_{LoG})+w_{2}\cdot SD\left(\sum\
olimits_{\Omega}SIM_{MN }\right)+w_{3}\cdot SD\left(\sum\
olimits_{\Omega}SIM_{LC}\right) \tag{9}\]  
where \(w_{1}\), \(w_{2}\), and \(w_{3}\) are weights representing importance, and \(w_{1}+w_{2}+w_{3}=1\). Standard deviation (SD) can effectively highlight the feature differences of the image [28]. The \(SD\) calculation formula is \(SD(k)=\sqrt{\frac{1}{H}\sum\limits_{k=1}^{H}{(k-\overline{k})}^{2}}\), \(\overline{k}=\frac{1}{H}\sum\limits_{k=1}^{H}{k}\), where \(k\) represents the feature similarity value of the image. In order to express the reliability of this method intuitively, two groups of original distorted image pairs (including subjective scores of images) with the same specifications are selected from the TID2008 database, and the objective scores of the above images are calculated through the proposed method, and then the obtained objective scores are compared with the subjective scores. The higher the subjective score, the smaller the difference between the reference and distorted images and the higher the quality for the distorted image. The lower the objective score, the smaller the difference between the reference and distorted images, and the higher the quality, and vice versa. As can be seen from Figure 2, the subjective scores of Figure 2b,c decrease in turn, indicating that Figure 2c is more distorted than Figure 2b, while the objective scores calculated by the proposed method decrease in turn, which is in line with the results of human judgment. The subjective and objective scoring results in Figure 2e,f are the same, so they are not repeated here. It can be observed that the proposed method can accurately express the degree of distortion. The method content described above can be more intuitively expressed in Figure 3, and the calculating process of MVFC is shown in Algorithm 1. ```
Input: The reference image \(\mathbf{R}\) and \(1\) distorted image \(\mathbf{D}\). Output: An quality score \(\mathbf{S}\). % transform into an opponent color space (steps 1-2) \(\mathbf{H}\) stands for \(\mathbf{R}\) or \(\mathbf{D}\). ```
1: R\({}_{\text{H}}\)G\({}_{\text{H}}\)B\({}_{\text{H}}\) transfer to L\({}_{\text{H}}\)M\({}_{\text{H}}\)N\({}_{\text{H}}\) ```
2: Downsample the image % Contrast feature map similarity calculation. (steps 3-9) 3: window \(\leftarrow\) fspecial('gaussian',2,1.5). 4: window \(\leftarrow\) window/sum(sum(window)). 5: mu\({}_{\text{H}}\)\(\leftarrow\) filter2(window, L\({}_{\text{H}}\),'same'). 6: mu\({}_{\text{L}}\)_sq\({}_{\text{H}}\)\(\leftarrow\) mu\({}_{\text{H}}\)\(\times\) mu\({}_{\text{H}}\). 7: sigma\({}_{\text{H}}\)\(\leftarrow\) sqrt(abs(filter2(window,\(\lambda\) L\({}_{\text{H}}\)\(\times\) L\({}_{\text{H}}\),'same') \(-\) mu\({}_{\text{L}}\)sq\({}_{\text{H}}\))). 8: C2 \(\leftarrow\) 50. % Define C2 to avoid instabilities when denominator is close to 0 9: SIM\({}_{\text{LC}}\) (2\(\times\) sigma\({}_{\text{R}}\)\(\times\)sigma\({}_{\text{D}}\) + C2)/(sigma\({}_{\text{R}}\)\({}^{2}\) + sigma\({}_{\text{D}}\)\({}^{2}\) + C2). % Similarity calculation in chroma channels (M\({}^{\text{+}}\), N\({}^{\text{+}}\)) (steps 20-22). (steps 10-13) 10: T1 \(\leftarrow\) 0.00006. 11: mSIM \(\leftarrow\) abs ((2 \(\times\) (M\({}_{\text{R}}\)) \(\times\) (M\({}_{\text{D}}\)) + T1)/((M\({}_{\text{R}}\))\({}^{2}\) + (M\({}_{\text{D}}\))\({}^{2}\) + T1)). 12: nSIM \(\leftarrow\) abs ((2 \(\times\) (N\({}_{\text{D}}\)) \(\times\) (N\({}_{\text{D}}\)) + T1)/((N\({}_{\text{R}}\))\({}^{2}\) + (N\({}_{\text{D}}\))\({}^{2}\) + T1)). 13: SIM\({}_{\text{MN}}\)\(\leftarrow\) mSIM\({}_{\text{L}}\)rSIM.