# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 4 Experimental Results
### Video Quality  
To obtain a reasonably comprehensive evaluation of the impact of data embedding on the quality of H.264/AVC video, the video sequences are encoded with two QPs of 16 and 28, and five different payloads are selected according to the embedding capacity of each video. The 10 th frame of the six cover videos with a QP of 16 and the corresponding marked frames generated by our schemes are shown in Figure 6, where the payload is the maximum value we use for each video in our experiments. It can be seen that the visual distortions in the marked frames are almost unnoticeable. Hence, the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [43] and entropy are used to further demonstrate the visual quality of marked video. The results of the schemes using the \((0,0)\) coefficient pairs are shown in Table 4, and the corresponding percentages for each ranking are shown in Table 5. From Tables 4 and 5, it can be seen that when QP is 16, as far as PSNR is concerned, the quality of the marked video generated by our scheme is the best in 66.7% of the cases, while in the remaining cases, our results are all ranked in the middle, which are superior to [34] and inferior to [33]; in terms of SSIM, our scheme achieves the best video quality in 93.3% of the cases. When QP is 28, 70.0% of the PSNR values \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Embedding Capacity (bits)**} \\ \cline{2-7}
**Video** & \multicolumn{3}{c}{**QP = 16**} & \multicolumn{3}{c}{**QP = 28**} \\ \cline{2-7}  & **Ours\({}^{-}\)** & [37] & **[29]** & **Ours\({}^{-}\)** & [37] & **[29]** \\ \hline \multirow{2}{*}{foreman container} & 674,237 & 864,838 (+28.3\%) & 480,248 (\(-28.8\%\)) & 16,892 & 22,252 (+31.7\%) & 11,761 (\(-30.4\%\)) \\  & 388,377 & 499,770 (+28.7\%) & 273,428 (\(-29.6\%\)) & 11,747 & 15,398 (+31.1\%) & 8118 (\(-30.9\%\)) \\ \cline{1-1} \cline{2-7} bus & 1,253,046 & 1519,264 (+21.2\%) & 879,967 (\(-29.8\%\)) & 156,593 & 205,092 (+31.0\%) & 110,696 (\(-29.3\%\)) \\ \cline{1-1} \cline{2-7} crew & 616,595 & 795,013 (+28.9\%) & 440,713 (\(-28.5\%\)) & 24,478 & 32,174 (+31.4\%) & 16,845 (\(-31.2\%\)) \\ \cline{1-1} \cline{2-7} hall\_monitor & 701,549 & 920,756 (+31.2\%) & 493,344 (\(-29.7\%\)) & 14,704 & 19,408 (+32.0\%) & 10,171 (\(-30.8\%\)) \\ \cline{1-1} \cline{2-7} mobile & 1,371,842 & 1,616,303 (+17.8\%) & 989,803 (\(-27.8\%\)) & 311,538 & 404,755 (+29.9\%) & 220,584 (\(-29.2\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The embedding capacity of the schemes without using the \((0,0)\) coefficient pairs, where the percentage figures in parentheses indicate the relative difference between other schemes and Ours\({}^{-}\). Figure 6: The 10 th frame of original videos (**a**) foreman, (**b**)container, (**c**) bus, (**d**) crew, (**e**) hall_monitor, and (**f**) mobile, and the corresponding 10 th frame of marked videos, where (**g**) marked foreman, (**h**) marked container, and (**i**) marked bus are generated by our scheme using the \((0,0)\) coefficient pairs; (**j**) marked crew, (**k**) marked hall_monitor, and (**l**) marked mobile are generated by our scheme without using the \((0,0)\) coefficient pairs, respectively. highest, and for SSIM, our scheme obtains the best results in about 73.3% of the cases, both results are higher than the comparison methods. The main reason for the small difference between the PSNR and SSIM values of our results and the comparison methods is that the proportion of the \((0,0)\) coefficient pairs are very high, and thus large part of data will be embedded into these coefficient pairs. However, our embedding mode of the \((0,0)\) coefficient pairs is the same as that of [34], and the difference in embedding efficiency between our scheme and [33] is not very large.