使用深度神经网络的视频压缩

2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2022-12-23 DOI:10.1109/CCIP57447.2022.10058645

Dayananda P, Siddharth Subramanian, Vijayalakshmi Suresh, Rishab Shivalli, Shrinkhla Sinha

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引用次数: 0

摘要

由于在线视频内容的增加，需要先进的视频压缩技术。一种强大的压缩方法可以帮助在有限的带宽下有效地传输视频数据。我们观察到，越来越多的互联网用于视频会议、在线游戏和教育，导致欧洲和其他地区Netflix、YouTube和其他流媒体服务的视频质量下降，尤其是在2019冠状病毒病疫情期间。在标准的视频压缩算法中，它们被表示为残差帧之后的一系列参考帧，这些方法在应用上受到限制。深度学习的引入和目前的进展有可能克服这些问题。本研究提供了一个基于深度学习的视频压缩模型，该模型满足或超过当前的H.264标准。

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Video Compression using Deep Neural Networks

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards.

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2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP)

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