## 4 Experimental Results
It can also be seen that the PSNR value of our proposed method is higher than those of the TLE and SBDE methods. In the image of the \"Baboon\", the EC of the IPVO method is less than 20,000, so the PSNR cannot be compared. Let us observe the average maximum EC and image quality PSNR of the ten test images in Figure 9a-j and Table 5. It can be seen that the average maximum EC of the TLE method is 179,587 bits, the average maximum EC of the SBDE is 149,298 bits, and the average maximum EC of IPVO is 33,465 bits. The average maximum EC of the triple-RDH method we proposed is 261,466 bits. Even in any image in Figure 9, there is an obvious difference in the embedding capacity. We have a great advantage in the embedding capacity, and the PSNR value tends to be flat when the embedding capacity is higher. Compared with other methods, especially the PSNR of IPVO presents a steeper downward trend. In Figure 9a-j, when the EC is 20,000 bits, the PSNR values of TLE, SBDE, IPVO, and the proposed triple-RDH method are 42.39, 40.31, 54.34 B, and 42.89 dB on average, respectively. When the EC is 40,000 bits, the average PSNR values of TLE, SBDE, IPVO, and the proposed triple-RDH method are 39.24, 37.34, 52.71, and 39.89 dB, respectively. When the EC is 60,000 bits, the average PSNR values of TLE, SBDE, IPVO, and the proposed triple-RDH method are 37.35, 35.47, and 38.04 dB, respectively; However, the IPVO method is incapable of hiding anything at this time. The PSNR value of the proposed triple RDH method is slightly higher than those of the average PSNR values of the TLE and SBDE methods. From Table 5, our proposed triple-RDH method using predictor method 2 has a higher EC than Predictor methods 1 and 3. In the \"Lena\" image, the EC of our triple-RDH method is 4931 bits higher than that of the method using Predictor method 1, and also 7714 bits higher than that of the method using predictor method 3. Table 5 is also a comparison table of our proposed triple-RDH method and TLE on the different predictor methods in terms of EC and PSNR metrics. It can be found that the maximum EC of all predictor methods of our proposed method is higher than that of the TLE method. At the maximum EC, the PSNR value of our proposed triple RDH method with predictor Method 2 is only slightly lower than that of the TLE method by 1.31 dB; the PSNR value of our proposed triple RDH method using predictor Method 3 is slightly lower by 1.12 dB; the PSNR value of our proposed triple RDH method using predictor method 1 is slightly lower by 1.1 dB. Table 6 shows the results in terms of EC and PSNR using our triple-RDH method for different \(n\) and different peak points. It can be observed from the experiment results that the PSNR can reach 46.51 dB when \(n=1\). When \(n=4\), the EC can be as high as 378,971 bits, but the PSNR drops to 24.85 dB. Accordingly, we do not recommend setting \(n\) to be 4. The EC with \(n=3\) can reach 324,055 bits and the PSNR is maintained at 31.11 dB, achieving high capacity while maintaining good image quality PSNR, so we recommend setting \(n\) as 3. At different peak points, we find that the EC with (1, \(-1\)) is higher than those of (0, \(-1\)) and (1, 0) in most cases, so we recommend setting the peak point to (1, \(-1\)) as the best choice. The peak points (0, \(-1\)) and (1, 0) have some differences in the performance of the complex image and the smooth image. For a smooth image \"F16\", when using the second predictor method and \(n\) is set as 3, the difference between peak points (1, \(-1\)) and (0, \(-1\)) in terms of EC is 6644 bits, and the difference in EC between peak points (1, \(-1\)) and (1, 0) is 58,464 bits. As for the complex image \"Baboon\", the difference in terms of EC between the peak points at (1, \(-1\)) and at (0, \(-1\)) is 25,045 bits; the difference in EC between peak points at (1, \(-1\)) and at (1, 0) is 20,092 bits.