# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 4 Experimental Results
### Embedding Capacity  
In order to calculate the embedding capacity of the proposed scheme, we conducted some experiments with test images and datasets. Also, state-of-the-art schemes were compared. The embedding capacity is the total number of bits in the embedded image. Therefore the embedding rate is used to measure the embedding ability of the original image. The embedding rate represents the number of bits of information that can be embedded into each pixel, and it is measured in bits per pixel (bpp). A larger embedding rate means that the solution is more capable of embedding. In order to obtain the highest embedding rates in the experiments, all scenarios used the best effect parameter settings. Figure 16 compares the proposed EPBTL scheme and four SOTA schemes including [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] et al. (2020) on the four testing images including Lena, Man, Jetplane and Baboon in terms of embedding rate. As can be seen in the four test images, the proposed scheme shows varying degrees of improvement in embedding rate over the other four SOTA schemes. The images 'Jetplane' and 'Lena' both have an embedding rate of over 3.0 bpp, which is a significant change. The image 'Jetplane' has a smaller increase compared to the other three images. This is because the prediction errors for the pixels of the image 'Jetplane' fall less in the self-recordable subgroup (\(P_{\textit{ns}}\)). It leads to an increase in the data that needs to be recorded by the auxiliary information. To verify the generality of the scheme, we compare this schemes on the three datasets including UCID ([PERSON] et al., 2019), BOSSbase ([PERSON] et al., 2019), and BOWS-2 ([PERSON] et al., 2020) in terms of average embedding rate. As can be seen in Figure 17, in the datasets BOSSbase and BOWS-2, the proposed scheme also has an embedding rate of more than 3.0 bpp. The embedding rate also improved on the dataset UCID. This indicates that the proposed scheme has a higher embedding rate than the other schemes in the experiment with random images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Lena** & **Man** & **Jetplane** & **Baboon** \\ \hline PSNR(dB) & 9.1231 & 7.5832 & 8.0899 & 5.9632 \\ SSIM & 0.0312 & 0.0661 & 0.0349 & 0.0310 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR and SSIM between the original image and embedded encrypted image  
Figure 16: The comparison of the embedding rates of the different schemes on the test images includes [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] et al. (2020) and Proposed method. Tables 5-8 show the embedding rate of the IPBTL-RDHEI scheme and in the proposed EPBTL scheme when \(\alpha=2\sim 7\), and \(\beta=2\) and 3. We only need to pay attention to the optimal value of each image in all situations. The reason for choosing \(\beta=2\),3, \(\alpha=2\sim 7\) is that the experiments can achieve the better embedding rate at this configuration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \((\alpha,\beta)\) & \((1,3)\) & \((2,3)\) & \((3,3)\) & \((4,3)\) & \((5,3)\) & \((6,3)\) & \((7,3)\) \\ \hline Lena & / & / & 1.7045 & **2.7872** & 2.6754 & 1.9445 & 0.9974 \\ Man & / & / & 0.6545 & 2.0964 & **2.5517** & 1.9945 & 0.9854 \\ Jetplane & / & 0.9855 & 2.6945 & **3.0589** & 2.6945 & 1.9367 & 0.9845 \\ [PERSON] & / & / & / & / & 0.8745 & **1.2502** & 0.8854 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The embedding rate of the IPBTL-RDHEI scheme when \(\beta=3\) and \(\alpha=2\sim 7\). Figure 17: The comparison of the average embedding rates of the different schemes on the datasets includes [PERSON] et al. [23], [PERSON] et al. [25], [PERSON] et al. [26], [PERSON] et al. [27] and Proposed method.