Abstract

The research concerns the use of machine learning algorithms for multiscale coding of digital video sequences. Based on machine learning, the digital image coder is generalized to the coding of video sequences. To this end, we offer an algorithm that allows for videoframes interdependency by using linear regression. The generalized image coder uses multiscale representation of videoframes, neural network three-dimensional interpolation of multiscale videoframe interpretation levels and generative-adversarial neural net replacement of homogeneous portions of a videoframe by synthetic video data. The method of coding the entire video and method of coding videoframes are exemplified by block diagrams. Formalized description of how videoframe correlation is taken into account is given. Real video sequences are used to carry out numerical experiments. The experimental data allow us to make a conclusion about the promise of using the algorithm in video coding and processing.

中文翻译：

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多尺度视频编码的机器学习

摘要

该研究涉及使用机器学习算法对数字视频序列进行多尺度编码。基于机器学习的数字图像编码器被推广到视频序列的编码。为此，我们提供了一种算法，通过使用线性回归来允许视频帧相互依赖。广义图像编码器使用视频帧的多尺度表示、多尺度视频帧解释级别的神经网络三维插值以及合成视频数据对视频帧的同质部分的生成对抗性神经网络替换。通过框图举例说明对整个视频进行编码的方法和对视频帧进行编码的方法。给出了如何考虑视频帧相关性的形式化描述。使用真实视频序列进行数值实验。