# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 4 Results
### Comparison with Other Methods  
In order to check the performance of the proposed method, a comparison was made with other state-of-the-art (SOTA) methods, which are listed in the literature review. In order to compare the methods, the HEVC compression value was assumed to be 16 and the resolution of the test video to be 416 \(\times\) 240. The results of the comparison of watermark embedding methods under HEVC compression conditions are shown in Table 12.  
The methods presented in Table 12 represent different approaches to embedding a watermark in video under HEVC compression conditions. In the first method, the watermark is embedded as intra-prediction residual pixels of 4 \(\times\) 4 luminance transform blocks in the spatial domain [16]. In the second method compared, the prediction modes of selected 4 \(\times\) 4 intra-prediction blocks are changed [17]. In the next method, the watermark is embedded as the multi-coefficients of the selected 4 \(\times\) 4 luminance discrete sine transform blocks [51]. The previously proposed method differs from the one presented in this article in efficiency. The watermark capacity has been increased along with its resistance to HEVC compression while increasing the PSNR value. The architecture of the DNNs used, and the way they are trained has been changed, however, the way the watermark is embedded and restored has remained based on the operation of the ASPA. The average PSNR for the proposed method is dependent on the compression coefficient, however, it is greater than 44 dB in the HEVC compression range, allowing it to be recovered. The PSNR for the proposed method has a higher value than for the previously proposed method and is close to the method having the highest value of the compared SOTA methods. The calculation time for the proposed method is noticeably different from the other methods compared. The reason for this state is the non-optimized structure of the ANN for real-time processing. However, the lack of optimization of the ANN structure is not the main reason for the long calculation time, it is caused by the suboptimal division of the image into fragments corresponding to the resolution matching the ANN input. The main factor that extends the watermark embedding process is the support for the YUV420p color model and the associated image storage. An optimization involving the parallelization of the processes of obtaining and overwriting image fragments would significantly speed up computation. The time calculated for the implementation that does not include the parallelized calculations for watermark embedding is 81.13 s. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Average PSNR (dB)**} & \multirow{2}{*}{**Capacity (Bits/Frame Size)**} & **(Embedding Time/Extraction Time) (ms) for Test Frame with 416 \(\times\) 240 Resolution** & \multirow{2}{*}{**Hardware**} \\ \hline
**Method 1** & & & & \\
**[PERSON] et al. [16]** & 47.519 & 100 bits/416 \(\times\) 240 & 32.478/5.622 & 3.30 GHz CPU, 4 GB RAM \\
**Method 2** & & & & 4 GB RAM \\
**[PERSON] et al. [17]** & 46.415 & 100 bits/416 \(\times\) 240 & 36.855/5.048 & 3.30 GHz CPU, 4 GB RAM \\
**Method 3** & & & & 3.00 GHz CPU, 4 GB RAM \\
**[PERSON] et al. [51]** & 45.462 & 100 bits/416 \(\times\) 240 & 34.058/5.997 & 4 GB RAM \\
**Previously proposed** & & & & & \\
**method [22]** & 42.617 & 80 bits/128 \(\times\) 128 & 34,457.92/3759.72 & 11 GB, 32 GB RAM \\
**Proposed method** & 47.299 & 96 bits/128 \(\times\) 128 & 81,132.13 \({}^{1}\)/82,223.74\({}^{1}\) & & \\
**Proposed method** & 47.299 & 96 bits/128 \(\times\) 128 & 735.101 \({}^{2}\)/725.861 \({}^{2}\) & 11 GB, 32 GB RAM \\ \hline \hline \end{tabular}  
* Time calculated for an implementation that does not include parallelized calculations, \({}^{2}\) Time of calculations performed sequentially on beforehand prepared image fragments with a resolution corresponding to the size of the encoder and decoder inputs.  
\end{table} 
Table 12: Method comparison.