# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 4 Results
### HEVC Compression Research Results  
The results of the DNN coder mimicking HEVC coding are presented as a comparison of the image obtained at the output of the DNN coder to the image obtained at the output of the HEVC codec (Table 7) for selected CRF values from the full compression range \(\langle 0;51\rangle\). A preview of the frames from the test video used for the research presentation is shown in Figure 4. The flow of the coder training process is shown in Figures 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Input Tensor Size** & **Number of Weights** & **Size on Disk** &
\begin{tabular}{c} **Processing Time for** \\ **a Single Tensor [ms]** \\ \end{tabular} & **GPU Processor** \\ \hline Encoder & \((2,192,128,1)\) & 5776723 & 69.10 MB & 62.234 & GeForce 1080 Ti GTX \\ Decoder & \((192,128,1)\) & 5771494 & 69.00 MB & 61.424 & 11 GB \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of the encoder and decoder.  
Figure 3: Preview of the edge effect: Left for a poorly trained network, right for a better trained network.  
The test set is made up of the individual initial frames of the test video, which are dimmed at the start and gradually brightened while movement occurs in the image [54]. This choice of a test set ensures that the proposed method is tested under more demanding conditions, as the compression coefficient for more dynamic motion scenes may increase, while for more static scenes, it may be reduced when using CRF. Unlike the QP (quantization parameter), which sets a fixed compression value for the entire video, the CRF allows the compression to be changed to a higher or lower value. In practical application, it is popularized to determine the degree of video compression using the CRF, so the results presented in this article will be using this parameter.  
Figure 4: A preview of the frames from the test video used for the research presentation is subsequently (from the top left): 1, 5, 10, 20, 25, and 30.  
Figure 5: PSNR of the coder image (trained on CRF = 24) to the image after HEVC compression.  
Figure 6: PSNR of the coder image (trained on CRF = 24) to the original image.  
An additional factor influencing the selection of the test video is the presence of darkened and completely dark frames in the video, which is important as potential graphic artifacts can easily be seen in dark background. Because it is easier to hide a watermark (another image in the image) in a brightened image, there is a tendency to brighten images. In order to test the encoder's performance in this aspect as well, it was decided to also use darkened video frames in the test sample.  
Analyzing the results in the table above, it is noticeable that the DNN coder tends to reproduce the darkened images more accurately. In the range of values \(\langle 0;16\rangle\) for CRF, the quality of the PNSR reproduction is above 50 dB, which is due to the fact that in this range of values, the compression is not expansive, which is further confirmed in the column with the results of the comparison with the original uncompressed image.  
One of the factors affecting the accuracy of the DNN coder's representation of an image relative to an image encoded by H.265 is not only to consider the similarity of the coder's output image to the HEVC codec's output image but also to consider the similarity to the original image. For the CRF value with which the coder was trained, the PSNR value oscillates closer to the order of 50 dB than 40 dB, as is the case for compression coefficient values above 30.  
The graphs from the training process up to epoch 200 show an upward trend in the PSNR coefficient. In subsequent epochs, there is a reduction in oscillations resulting from a reduction in the learning factor, as described in the section on coder training.