## 4 Experimental Results
From the results, we find that using (1, 0) as the peak point in a complex image is more advantageous than using (0, \(-\)1); however, in a smooth image, using (0, \(-\)1) as the peak point will obtain good benefits in embedding capacity. Therefore, different peak points can be set according to whether the carrier is a complex image or a smooth image, and actual application requirements to achieve a balance between embedding capacity and image quality. We observe the results in Table 6 and find that when \(n=3\), \(pp_{1}=1\), and \(pp_{2}=-1\), the results are the best, so we set \(n\) as 3 and peak point to (1, \(-\)1) used as the experimental parameter in Table 7. Table 7 shows the performance comparison of our triple-RDH method when setting \(t_{g}\)_and_\(t_{w}\) from 1 to 5 for both the complex image \"Baboon\" and the smooth image \"F16\". When the recursive parameters are between \(t_{g}=1,\ t_{w}=1\) and \(t_{g}=2,t_{w}=2\), the embedding capacity EC has increased by 0.46 times; between \(t_{g}=2,\ t_{w}=2\) and \(t_{g}=3,t_{w}=3\), the EC has increased by 0.15 times; between \(t_{g}=3,t_{w}=3\) and \(t_{g}=4,\ t_{w}=4\), the EC has increased by 0.06 times; between \(t_{g}=4,\ t_{w}=4\) and \(t_{g}=5,t_{w}=5\), the EC has increased by 0.02 times. The higher the \(t_{g}\)_and_\(t_{w}\) values, the lower the increased EC will be, so the benefit is not high. Accordingly, we recommend the parameters as \(t_{g}=4\) and \(t_{w}=4\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Image**} & \multicolumn{6}{c}{**Baboon**} & \multicolumn{6}{c}{**F16**} \\ \hline \multicolumn{2}{c}{**Prediction Method**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} \\ \hline \multicolumn{2}{c}{**Metrics**} & **EC** & **PSNR** & **EC** & **PSNR** & **EC** & **PSNR** & **EC** & **PSNR** & **EC** & **PSNR** & **EC** & **PSNR** \\ \hline \multirow{3}{*}{\(n=1\)} & (0, \(-\)1) & 38,239 & 46.34 & 53,738 & 42.94 & 49,517 & 44.49 & 151,726 & 44.29 & 171,459 & 42.84 & 158,831 & 43.49 \\  & (**1, \(-\)1**) & **43,496** & **45.93** & **56,084** & **42.78** & **52,291** & **44.17** & **182,186** & **44.2** & 204,695 & 42.99 & 190,126 & 43.51 \\ \cline{2-13}  & (1, 0) & 39,552 & 46.51 & 55,228 & 42.95 & 50,813 & 44.58 & 162,201 & 44.85 & 179,161 & 42.88 & 175,669 & 43.76 \\ \hline \multirow{3}{*}{\(n=2\)} & (0, \(-\)1) & 75,795 & 38.97 & 91,105 & 36.5 & 87,790 & 37.6 & 223,235 & 37.55 & 245,731 & 36.74 & 229,137 & 37.11 \\  & (**1, \(-\)1**) & 89,083 & 38.47 & 103,796 & 36.37 & 97,735 & 37.28 & 260,492 & 37.61 & 273,462 & 36.96 & 260,942 & 37.24 \\ \cline{2-13}  & (1, 0) & 82,864 & 39.08 & 97,122 & 36.43 & 94,684 & 37.63 & 224,517 & 37.9 & 231,181 & 36.69 & 229,808 & 37.24 \\ \hline \multirow{3}{*}{\(n=3\)} & (0, \(-\)1) & 132,560 & 32.17 & 147,408 & 30.51 & 140,467 & 31.19 & 299,410 & 31.25 & 317,411 & 30.79 & 307,975 & 31.01 \\  & (**1, \(-\)1**) & 159,957 & 31.92 & 172,453 & 30.6 & 164,474 & 31.09 & 317,223 & 31.43 & 324,055 & 31.11 & 316,166 & 31.23 \\ \cline{1-1}  & (1, 0) & 144,993 & 32.6 & 152,361 & 30.52 & 155,691 & 31.4 & 264,640 & 31.43 & 265,591 & 30.7 & 263,638 & 31.02 \\ \hline \multirow{3}{*}{\(n=4\)} & (0, \(-\)1) & 206,421 & 25.32 & 218,495 & 24.51 & 212,185 & 24.79 & 365,758 & 25.5 & 378,971 & 24.85 & 370,502 & 24.91 \\ \cline{1-1}  & (**1, \(-\)1**) & 243,744 & 25.27 & 248,011 & 24.79 & 241,591 & 24.88 & 356,482 & 25.28 & 354,835 & 25.18 & 353,490 & 25.19 \\ \cline{1-1}  & (1, 0) & 215,380 & 25.71 & 206,349 & 24.49 & 216,826 & 24.96 & 285,726 & 25.04 & 285,603 & 24.66 & 284,403 & 24.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the performance of our triple-RDH method on images “Baboon” and “F16” with different \(n\) and different peak points.