## 5 Conclusions  
The process of training DNNs for video watermark embedding is a computationally resource-intensive task. The main reason for this is the resolution at which the ANN operates during the training process. By increasing the resolution of the learning patterns, i.e., the resolution at which the network will operate natively, the hardware resource requirements increase significantly. Given this fact, it is reasonable to train ANNs (encoders and decoders) at lower resolutions and then, through an appropriate algorithm, transpose their operations to higher image resolutions. Such a solution facilitates the process of training and searching for the optimal structure of the neural network, but it also enables the encoding of arbitrary image resolutions. Additionally, such a solution allows for multiple encodings of the same watermark in a larger image (a single frame of video), thus facilitating the process of watermark recovery. Moreover, it is possible to create a scenario of a larger image that includes different watermarks which, when combined, form one larger watermark. Encoding the watermark in the YUV colour palette seems to be characterised by a more permanent watermark deposition in the chrominance, i.e., in the U and V channels, than in the luminance Y channel. When designing a video watermarking system, it is worth bearing in mind the range of values in which the YUV and RGB colour palettes operate and, in particular, the fact that the YUV colour palette can also have negative values, while RGB has only positive values. This is not only important for the design of the watermark embedding system, but it is also crucial when using ANNs, forcing the selection of an appropriate activation function that takes negative values into account. The proper selection of the value range in which the watermark is embedded has a noticeable effect on the image in which it is hidden. When attempting to hide a white image in a black one (in the RGB colour palette, this would be hiding the 255 value in the 0 value), the network tends to learn to brighten the image so as to hide the watermark and keep the original image as true as possible, which results in an unwanted, visible brightening of the image. One possible solution to the above problem is to change the value range in which the watermark is embedded so as to reduce the size of the difference between the numerical values of each individual pixel of the original image and the watermark. Another approach is to attempt to apply an appropriate thresholding of the watermark energy, depending on the energy of the original image. The method presented in this paper generates desirable results for high image quality (low video-compression ratio), that is CRF, in the range \(\langle 0;\,16\rangle\) and demonstrates a tolerance for changes in image brightness--less for a darkened image, and more for a brightened one. Changing the encoded video to a different resolution results in a degradation of the watermark--greater for reduced video resolution and less for increased video resolution. The issues of changing the resolution of a watermarked video and embedding a watermark for a higher video compression ratio require further research. The question of increasing the capacity of the watermark and reducing the structure of the ANNs to speed up the method also requires further exploration. In order to reduce the structure of the neural network, the number of filters can be reduced. To make the watermark more resistant to image-resolution changes, it is worth introducing an additional component improving the quality of the recovered watermark, such as a super-resolution neural network. Increasing the capacity of the watermark can be achieved by appropriately selecting the parameters of the adjustable subsquares properties algorithm.