Machine-Learning-Based Method for Content-Adaptive Video Encoding

Авторы: Zvezdakov Sergey, Kondranin Denis, Vatolin Dmitriy
Сборник: 2021 Picture Coding Symposium (PCS)
Год издания: 2021
Место издания: IEEE Bristol, UK
Первая страница: 1
Последняя страница: 5
DOI: 10.1109/pcs50896.2021.9477507
Аннотация: Video codecs have several dozen parameters that subtly affect the encoding rate, quality and size of the compressed video. Codec developers, as a rule, provide standard presets that on average yield acceptable performance for all videos, but for a given video, certain parameters may yield more efficient encoding. In this paper, we propose a new approach to predicting video codec presets to increase compression efficiency. Our effort involved collecting a new representative video-sequence dataset from Vimeo.com. An experimental evaluation showed relative bitrate decreases of 17.8% and 7.9%, respectively for the x264 and x265 codecs with standard options, all while maintaining quality and speed. Comparison with other methods revealed significantly faster automatic preset selection with a comparable improvement in results. Finally, our proposed content-adaptive method predicts presets that archive better performance than codec-developer presets from MSU Codec Comparison 2020 [1].
Добавил в систему: Звездаков Сергей Васильевич

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