# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 2 Literature Review  
In reviewing the literature on watermark embedding, a distinction was made between classic methods and methods using ANNs. Classic methods can include methods, such as those based on the least significant bit [23; 24; 25] or methods based on frequency domain manipulation (discrete wavelet transform, discrete Fourier transform, discrete sine, and cosine transform) [10; 26; 27; 28; 29; 30; 31]. There are many modifications of these methods, such as a combination of the least significant bit method and those based on manipulation of the frequency domain, which includes publications concerning hybrid domains [23; 32; 33; 34]. When discussing the embedding of a watermark in an image, a distinction should be made between embedding a watermark in a static image [23; 34; 35; 36; 37] and in a video [14; 15; 16; 17]. The methods used for static images find their application and development in methods for video purposes. An elementary example of an information hiding method is the modification of the least significant bit, which is effective in its simplicity but sensitive to any kind of operation performed on a dynamic image, such as a video image. More resistant are methods based on frequency domain manipulation, where the watermark is embedded in such a frequency band of the image as to produce a watermarked image with the least degradation in quality compared to the original. Images are modified as a whole or in certain areas of interest, which may be parts of the image where it is easier to hide information or, due to the operation of the method for which no overall modification of the image is necessary to hide the information. Embedding a watermark in a video is a more complex issue than that of a static image. The main problem is to keep the watermark highly resistant while maintaining good video quality. The difficulty itself stems from the fact that the video image, when encoded into a particular format (depending on the DVB-T receiver, Internet TV), is usually compressed to reduce its size several times. This compression is usually multifaceted [38; 39] and is carried out not only in the binary symbol domain but is also achieved by downsampling of channels in a given color model, introducing changes in the frequency domain and introducing a motion vector, where such a cascade of source image translations ultimately hinders the possibility of watermark reconstruction. Multifaceted variations in the source image make it difficult to manually select the appropriate method for watermarking a transparent video image. This has led researchers towards using adaptive methods, such as DNN, for this purpose. Incorporating DNN into the issues of stenography and video watermarking allow a high probability of recovering the watermark and keeping the quality of the watermarked image in high quality [38; 39; 40] by being able to select optimal image composition and decomposition methods as a result of the training process [41; 42; 43; 44; 45; 46; 47]. Depending on the approach adopted, the watermark can be recovered from a single video frame or from a specific number of frames. For video watermarking, both classic and ANN-based methods are used [48; 49; 50; 51; 14; 15; 16; 17]. Among these are methods dedicated to specific video codecs, including the H.265/HEVC (High-Efficiency Video Coding) codec for which the method presented in this article has been developed [16; 17; 50; 51]. A noteworthy issue is the description of the coefficients describing the degree of accuracy of the watermarked video to the original [52]. The PSNR (peak signal-to-noise ratio) and MSE (mean-squared-error) are popular coefficients and used in this publication. There are also other coefficients that address the determination of the degree of accuracy between images, such as: NMSE (normalized-mean-squared-error), RMSE (root-mean-square-error), or SSIM (structural-similarity-index-measure).