## 4 Experimental Results and Analysis
This paper selects features from Gabor filters with four orientations and five scales. The selection method for the orientation parameter \(\theta\) is as follows:  
\[\theta_{k}=\frac{k\pi}{n},\;k=\{0,\;1,\;2,\;\ldots,\;n-1\}, \tag{36}\]  
Here, \(n\) represents the total number of filter orientations. In this paper, we select four orientations, so the angles are, respectively, \(\theta_{0}=0^{\circ}\), \(\theta_{1}=45^{\circ}\), \(\theta_{2}=90^{\circ}\), \(\theta_{3}=135^{\circ}\). Figure 14: A scatter plot of the gradient similarity against the MOS for the images. Figure 15: Histogram of the SSIM values for the selected scanned images compared to the original images: (a) image A, (b) image B. The parameter \(f\) of the Gabor filter represents the frequency of the filter, and it is calculated as follows:  
\[\sigma\cdot f=\frac{1}{\pi}\sqrt{\frac{\ln 2}{2}}\times\frac{2^{B}+1}{2^{B}-1}, \tag{37}\]  
\(B\) represents the bandwidth of the filter, typically set as \(B=1\), then \(\sigma\cdot f=0.56\). For a filter bank, its frequency can be expressed as follows:  
\[f_{k}=a^{-k}f_{max},k=(0,\;1,\;\ldots,\;m-1), \tag{38}\]  
\(m\) represents the total number of frequencies for the filter, where \(a=\sqrt{2}\). In this study, five scales are chosen, and thus \(m=5\). As shown in Figure 16, the real and imaginary parts of the Gabor filter are illustrated. In the process of image learning, 80% of the images from each database were randomly selected for training, and the remaining 20% were used for testing. To ensure fair comparisons, the training-testing process was repeated 1000 times, and the median of the results after 1000 iterations was taken as the final outcome to eliminate performance biases. The indicators for measuring the strengths and weaknesses of the quality evaluation metrics are shown below:  
1. The SROCC metric evaluates the monotonic relationship between the image quality assessment metrics and the subjective evaluation, indicating whether the objective assessment maintains a consistent trend with changes in the subjective evaluation. The SROCC value ranges from \(-1\) to \(1\), with values closer to \(1\) or \(-1\) indicating stronger monotonicity. 2. The PLCC metric assesses the linear correlation between the image quality assessment metrics and the subjective evaluation, with values ranging from \(-1\) to \(1\). Positive values denote a positive correlation, negative values signify a negative correlation, and \(0\) indicates no correlation. The closer the absolute value is to one, the more accurate the metric's evaluation. 3. The KROCC metric measures the rank correlation between two variables, with values ranging from \(-1\) to \(1\). A value close to \(1\) indicates a strong correlation, while a value of \(0\) implies independence. 4. The RMSE quantifies the deviation between the image quality assessment metrics and the subjective evaluation, with smaller values indicating higher consistency. An RMSE value approaching \(0\) signifies the high accuracy of the metrics. This paper compares nine commonly used FR-IQA metrics, including GMSD, GSM, IFC, MAD, MSSIM, PSNR, SSIM, VIF, and VSI. Table 4 presents the results of testing these nine common metrics along with the proposed metric on two standard databases. The bolded numbers denote the evaluation metrics with the best performance. Based on the  
Figure 12:0 Visualization of the Gabor filter’s kernel. descriptions of the SROCC, PLCC, KROCC, and RMSE, combined with the data in the table, it is evident that the performance of the proposed FFSF metric is superior. The selection of various regression methods leads to diverse evaluation outcomes. To identify a suitable regression approach for feature fusion, this section conducts a comparison between random forest (RF) and the MLP neural network. As shown in Table 5, except for the RMSE metric, the results for the remaining three metrics are all superior in the MLP compared to the RF. The results of the SVR are comparatively less favorable.