title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð´ÐµÐºÐ¾Ð² ÑÐµÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Application of neural network methods to increase the efficiency of real-time video codecs
Creators: name: ÐÐ»ÐµÐºÑÐ¾Ð²ÑÐºÐ¸, ÐÐ°ÑÑÐ¸Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð´ÐµÐº
ÑÐ¶Ð°ÑÐ¸Ðµ Ð²Ð¸Ð´ÐµÐ¾
video codec
video compression

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description: The masters thesis is devoted to research in the field of video compression and comparison of different approaches, including traditional video compression methods and modern methods using neural networks. The work examines existing video compression technologies, compares them, and identifies their shortcomings. A review of video compression methods and algorithms was conducted. The aim of the work is to create a comprehensive model for the deep compression of real-time video streams and to optimize video codec algorithms based on it to improve their efficiency. Within the dissertation, the traditional method of video compression, such as H.264, was analyzed and described. An approach was proposed that involves using neural network algorithms to improve the traditional method of video compression. The method analyzes each step of video compression separately and determines which steps can be performed using a neural network. The use of neural network algorithms for video compression was presented in the work. When comparing H.264, H.265, and our approach using neural networks, it was found that our method has many advantages over traditional video compression methods. The terminological features of the subject area were considered, and specialized software TensorFlow was used to solve the tasks.