## 4 Experimental Validation
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{3}{c}{**Structural Content**} & \multicolumn{3}{c}{**Peak Signal Noise Ratio**} \\ \cline{2-7}  & **OMKHES-MS** & **WOA-EBS** & **GWO-EBS** & **OMKHES-MS** & **WOA-EBS** & **GWO-EBS** \\ \hline Barbara & 0.9986 & 0.9984 & 0.9984 & 59.4556 & 59.0647 & 53.4721 \\ \hline Boat & 0.9991 & 0.9990 & 0.9989 & 58.1759 & 57.3818 & 49.5272 \\ \hline Foreman & 0.9991 & 0.9990 & 0.9990 & 63.5057 & 62.4948 & 59.3999 \\ \hline House & 0.9986 & 0.9984 & 0.9984 & 72.6470 & 70.7592 & 58.4823 \\ \hline Isabe & 0.9986 & 0.9984 & 0.9985 & 58.2083 & 57.3049 & 51.4495 \\ \hline Peppers & 0.9987 & 0.9984 & 0.9985 & 62.3885 & 61.0412 & 58.4367 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SC and PSNR analysis of OMKHES-MS technique with different images. Figure 7: Comparative RMSE analysis of OMKHES-MS technique with existing models. attained lower SC of 0.9984 and 0.9984 respectively. Similarly, with Peppers image, the OMKHIES-MS model has reached improved SC of 0.9987 whereas the WOA-EBS and GWO-EBS models have resulted in reduced SC of 0.9984 and 0.9985 respectively. An extensive PSNR investigation of the OMKHIES-MS model with recent approaches under distinct test images is made in Figure 9. The experimental values highlighted that the OMKHIES-MS model has led to enhanced performance with increased PSNR values under every image. For instance, with Barbara image, the OMKHIES-MS model has depicted superior PSNR of 59.4556 dB whereas the WOA-EBS and GWO-EBS models have exhibited inferior PSNR of 59.0647 dB and 53.4721 dB respectively. Likewise, with Peppers image, the OMKHIES-MS model has exhibited increased PSNR of 62.3885 dB whereas the WOA-EBS and GWO-EBS models have implied decreased PSNR of 61.0412 dB and 58.4367 dB respectively. A comprehensive AD and MD assessment of the OMKHIES-MS model is compared with recent methods in Table 3. The experimental values notified that the OMKHIES-MS model has gained effectual outcome with maximum values of AD and MD. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{3}{c}{**Average Difference**} & \multicolumn{3}{c}{**Maximum Difference**} \\ \cline{2-7}  & **OMKHIES-MS** & **WOA-EBS** & **GWO-EBS** & **OMKHES-MS** & **WOA-EBS** & **GWO-EBS** \\ \hline Barbara & 0.0053 & 0.0066 & 0.0067 & 45.4132 & 47.2942 & 46.6040 \\ \hline Boat & 0.0184 & 0.0194 & 0.0195 & 44.7870 & 46.8104 & 45.9838 \\ \hline Foreman & 0.0282 & 0.0296 & 0.0296 & 53.5160 & 54.9844 & 54.8858 \\ \hline House & 0.0009 & 0.0020 & 0.0022 & 53.1631 & 54.6097 & 54.3963 \\ \hline Isabe & 0.0008 & 0.0018 & 0.0017 & 43.8986 & 46.3907 & 45.2574 \\ \hline Peppers & 0.0005 & 0.0007 & 0.0008 & 47.0321 & 48.3379 & 49.8736 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AD and MD analysis of OMKHIES-MS technique with different images. Figure 8: Comparative SC analysis of OMKHIES-MS technique with existing models. Figure 10 demonstrates the AD assessment of the OMKHES-MS model with existing models on dissimilar images. The results designated that the OMKHES-MS model has presented capable results with negligible values of AD over the other WOA-EBS and GWO-EBS models. For instance, with [PERSON] image, the OMKHES-MS model has gotten lesser AD of 0.0053 whereas the WOA-EBS and GWO-EBS models have presented greater AD of 0.0066 and 0.0067 respectively. Besides, with [PERSON]'s image, the OMKHES-MS model has extended decreased AD of 0.0005 whereas the WOA-EBS and GWO-EBS models have delivered enlarged AD of 0.0007 and 0.008 respectively. Figure 10: Comparative AD analysis of OMKHES-MS technique with existing models. Figure 9: Comparative PSNR analysis of OMKHES-MS technique with existing models. Figure 11 represents the MD inspection of the OMKHES-MS model with existing models on diverse images. The experimental values reported that the OMKHES-MS model has accomplished better performance with lower values of MD over the other WOA-EBS and GWO-EBS models.