{"id":105094,"date":"2022-09-14T21:37:05","date_gmt":"2022-09-14T19:37:05","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/kompaniya-sollabora-predstavila-sistemu-mashinnogo-obucheniya-dlya-szhatiya-video"},"modified":"2022-09-14T21:37:05","modified_gmt":"2022-09-14T19:37:05","slug":"kompaniya-sollabora-predstavila-sistemu-mashinnogo-obucheniya-dlya-szhatiya-video","status":"publish","type":"post","link":"https:\/\/prohoster.info\/de\/blog\/novosti-interneta\/kompaniya-sollabora-predstavila-sistemu-mashinnogo-obucheniya-dlya-szhatiya-video","title":{"rendered":"Das Unternehmen Collabora hat ein Machine-Learning-System zur Videokompression vorgestellt.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Collabora hat eine Implementierung eines Machine-Learning-Systems ver\u00f6ffentlicht, das die Effizienz der Videokonferenzkompression steigert. Es erm\u00f6glicht eine zehnfache Reduzierung der erforderlichen Bandbreite beim Streaming von Videos mit Gesichtern, bei gleichbleibender Qualit\u00e4t auf H.264-Niveau. Die Implementierung wurde in Python unter Verwendung des PyTorch-Frameworks geschrieben und ist unter der GPLv3-Lizenz verf\u00fcgbar.      <\/p>\n<p>Die Methode erm\u00f6glicht die Rekonstruktion von Gesichtsd details, die bei starker Kompression verloren gehen. Das Machine-Learning-Modell generiert eine Animation eines sprechenden Kopfes basierend auf einem separat \u00fcbermittelten hochqualitativen Bild des Gesichts und dem empfangenen Video, wobei es die Ver\u00e4nderungen im Gesichtsausdruck und der Kopfposition verfolgt. Auf Senderseite wird das Video mit einer sehr niedrigen Bitrate \u00fcbertragen, w\u00e4hrend es auf Empf\u00e4ngerseite vom Machine-Learning-System verarbeitet wird. Zur weiteren Qualit\u00e4tssteigerung kann das generierte Video mit einem Super-Resolution-Modell bearbeitet werden.    <center>  <div class=\"youtube-placeholder\" data-id=\"jGQeuOacxSE\" onclick=\"loadVideo(this)\">\r\n        <img decoding=\"async\" src=\"https:\/\/img.youtube.com\/vi\/jGQeuOacxSE\/hqdefault.jpg\" alt=\"Video abspielen\" loading=\"lazy\" width=\"480\" height=\"360\" style=\"width:100%;height:auto;\">\r\n        <div class=\"play-button\"><\/div>\r\n    <\/div>  <\/center><br \/>\n<br \/>Quelle: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=57783\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f \u0421ollabora \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044e \u0441\u0438\u0441\u0442\u0435\u043c\u044b 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Diese erm\u00f6glicht es, die erforderliche Bandbreite beim \u00dcbertragen von Video mit Gesicht des Teilnehmers um das Zehnfache zu reduzieren, w\u00e4hrend die Qualit\u00e4t auf dem Niveau von H.264 bleibt. Die Implementierung ist in Python unter Verwendung des PyTorch-Frameworks geschrieben und unter der GPLv3-Lizenz ver\u00f6ffentlicht. 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