# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 4 Results
### Watermarking Research Results
Figure 10: BER of the hidden message (watermark in the form of binary string). Figure 9: PSNR of the hidden image (watermark in the form of image) relative to the recovered image. The obtained average result on the test group consisting of 15 tested persons was \(Q=62\%\). The obtained average result should be interpreted as a high similarity of the image with the embedded watermark to the original image, taking into account the results described below. During the research, it turned out that in the test group, there were also people who were able to find subtle subjective differences after zooming in on the image, but were unable to indicate which image was original and which was modified, which meets the assumptions of the method, because the watermark is invisible to the human eye (the test person, who noticed the differences after zooming in on the image fragments, were not able to indicate which image from the pair was modified). This result proves the effectiveness of the method in practical application and is supported by the objective results obtained mathematically in the form of the values of the MSE and PSNR coefficients. The change in compression coefficient expressed as CRF has a significant impact on the survival of the watermark under HEVC coding conditions. The changes in the image caused by the lower compression are not very invasive, which manifests itself in zero BER values. Based on the results in Table 9, it is noticeable that there is a tendency to correctly recover the watermark from all frames of the video test footage for a range of CRF values of \(\langle 0;22\rangle\). It is also possible to completely recover the watermark from individual video frames for CRF values not exceeding 24, however, it is more probable that for decoded watermark, BER would not be 0. The previous publication [22] presented only the most efficient way of extracting values from the subsquares in the form of the median used in the ASPA operation. Watermark extraction results using the mean and the most frequent element (value) are also included in the results discussed. By placing the results of the quality of the decoded watermark in the form of a BER using these methods, the following conclusions can be drawn, as presented in the next paragraph. The increase in compression, the invasiveness which causes increasingly significant changes, is well illustrated by the BER for information extraction using the most frequent element. In a \(32\times 32\) subsquare according to ASPA, the same values are embedded for each pixel. As the pixel values in the subsquare change due to compression, the number of pixels representing the embedded value is reduced. It is for this reason that, despite the appearance of non-zero BER values for the most frequently occurring element, recovery of the embedded value by the mean and median is possible. The median compared to the mean is a better way of extracting information, as it uses the mean results in larger BER values. This is due to the principle of the median, whose operation, unlike that of the mean, is not affected by changes in the extreme values in the set. Table 10 shows the results of a recovered watermark in the context of a correctly recovered number of characters that correspond to the results obtained in Table 9.  
The coded watermark by ASPA was a 16-character message with the following text,\"WATERMARK_WAT22\". Algorithm performance results when changing encoded image resolutions are shown in Table 11.  
Changing the resolution of the encoded image significantly affects the quality of the recovered watermark. When the image resolution is changed in the form of a multiple reduction or increase of the encoded image, the watermark is kept according to the results in Table 11. However, if the image resolution reduces the subsquares for ASPA interpretation to \(16\times 16\), the watermark will degrade.