## 4 Experimental Results and Analysis
Furthermore, since RMSE ranges from 0 to positive infinity, both methods fall within a similar, small range, indicating comparable performance. In summary, the evaluation metrics obtained through regression using the MLP neural network surpass those obtained using the RF. The bolded numbers denote the evaluation metrics with the best performance. The study concurrently compared results from the same model using different loss functions. Experiments were conducted using an MLP neural network model with various loss functions, and the findings are summarized in Table 6, showing that MSE outperforms MAE in terms of results. To illustrate the consistency between the proposed metrics in this paper and human subjective ratings, scatter plots were generated, as shown in Figure 12:1 These plots display the evaluation results of nine commonly used metrics relative to the MOS in the TID2013 \begin{table}
\begin{tabular}{c c c} \hline \hline  & **MAE** & **MSE** \\ \hline \multirow{4}{*}{TID2013} & KROCC & 0.6974 & 0.8452 \\  & PLCC & 0.8637 & 0.9680 \\  & RMSE & 0.6784 & 0.4907 \\  & SROCC & 0.8745 & 0.9483 \\ \hline \multirow{4}{*}{TID2018} & KROCC & 0.7619 & 0.8197 \\  & PLCC & 0.9031 & 0.9659 \\ \cline{1-1}  & RMSE & 0.8769 & 0.5333 \\ \cline{1-1}  & SROCC & 0.7697 & 0.9118 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: MLP regression models with different loss functions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **GMSD** & **GSM** & **IFC** & **MAD** & **MSSIM** & **PSNR** & **SSIM** & **VIF** & **VSI** & **FFSF** \\ \hline \multirow{4}{*}{TID2013} & KROCC & 0.6339 & 0.6255 & 0.6785 & 0.7327 & 0.5977 & 0.7161 & 0.5588 & 0.6665 & 0.7183 & 0.7720 \\  & PLCC & 0.8590 & 0.8464 & 0.8791 & 0.9071 & 0.8319 & 0.9080 & 0.7895 & 0.8769 & 0.9000 & 0.9397 \\  & RMSE & 0.6346 & 0.6603 & 0.5905 & 0.5219 & 0.6880 & 0.5193 & 0.7608 & 0.5959 & 0.5404 & 0.4256 \\  & SROCC & 0.8044 & 0.7946 & 0.8697 & 0.9052 & 0.7779 & 0.8925 & 0.7417 & 0.8510 & 0.8965 & 0.9316 \\ \hline \multirow{4}{*}{TID2008} & KROCC & 0.7092 & 0.6596 & 0.7009 & 0.7294 & 0.6636 & 0.9120 & 0.5758 & 0.6991 & 0.7123 & 0.8017 \\  & PLCC & 0.8788 & 0.8422 & 0.8810 & 0.8899 & 0.8579 & 0.7395 & 0.7732 & 0.8762 & 0.8762 & 0.9485 \\ \cline{1-1}  & RMSE & 0.6404 & 0.7235 & 0.6349 & 0.6120 & 0.6895 & 0.9078 & 0.8511 & 0.6468 & 0.6466 & 0.4277 \\ \cline{1-1}  & SROCC & 0.8907 & 0.8504 & 0.8903 & 0.9051 & 0.8559 & 0.5628 & 0.7749 & 0.8840 & 0.8979 & 0.9444 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A performance comparison of 10 metrics on two databases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RF** & **SVR** & **MLP** \\ \hline \multirow{4}{*}{TID2013} & KROCC & 0.7777 & 0.7426 & 0.8452 \\  & PLCC & 0.9426 & 0.9180 & 0.9680 \\  & RMSE & 0.4095 & 0.6907 & 0.4907 \\  & SROCC & 0.9315 & 0.9083 & 0.9483 \\ \hline \multirow{4}{*}{TID2018} & KROCC & 0.7921 & 0.7497 & 0.8197 \\  & PLCC & 0.9429 & 0.9159 & 0.9659 \\ \cline{1-1}  & RMSE & 0.4422 & 0.6017 & 0.5333 \\ \cline{1-1}  & SROCC & 0.8382 & 0.8738 & 0.9118 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A performance comparison of two regression methods. database. It is evident that the results obtained from individual evaluation methods exhibit considerable dispersion in the scatter distribution of image MOSs, lacking a clear trend. Further analysis through curve fitting accentuates the inconsistency between subjective and objective assessments. This outcome unequivocally indicates significant disparities between the objective quality assessment results obtained and the subjective ratings. To demonstrate the alignment between the metrics proposed in this study and human subjective ratings, scatter plots were generated, as depicted in Figure 17. These plots illustrate the evaluation outcomes of nine commonly used metrics relative to the Mean Opinion Score (MOS) in the TID2013 database. The GMSD metric shows poor monotonicity and a weak trend in the fitted curve, indicating varied GMSD scores for the same MOS.