Pearl: A Fast Deep Learning Driven Compression Framework for UHD Video Delivery
Pearl: A Fast Deep Learning Driven Compression Framework for UHD Video Delivery
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DOI：
10.1109/icc42927.2021.9500754
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Siqi Huang;Jiang Xie
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文献类型：
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作者：
Siqi Huang;Jiang Xie
超高清（UHD）视频由于良好的视觉体验而在人们的日常使用中越来越受欢迎。然而，UHD视频的数据大小是HD视频的416倍。这将给现有的视频传输系统带来诸多挑战，如网络带宽资源短缺、网络传输时延变长等。超分辨率（SR）算法被广泛用于视频传输应用中以应对这些挑战。然而，将超分辨率模型应用于超高清视频需要比高清视频更多的GPU内存，这给现有的系统带来了巨大的挑战。本文提出了一个名为Pearl的深度压缩框架，它利用深度学习的能力来压缩超高清视频。新的基于通道的超分辨率模型的开发，以克服GPU内存不足的问题。在pearl中，不是应用传统的基于RGB的超分辨率模型，而是基于UHD视频的Y，U和V通道训练三个独立的超分辨率模型。这些超分辨率模型用于从低分辨率视频重建UHD视频。使用Pearl，超分辨率算法可以成功应用于UHD视频。因此，UHD视频的数据大小可以在网络传输期间显著减少。同时，Pearl还可以提高视频编解码的效率。据我们所知，Pearl是第一个深度学习驱动的UHD视频压缩框架。我们通过大量的实验来评估Pearl的性能。在所有考虑的情况下，Pearl可以在视频传输过程中压缩高达95%的视频数据大小，并实现比现有系统快2.4倍的速度1。
Ultra-high-definition (UHD) videos are enjoying increased popularity in people’s daily usage because of the good visual experience. However, the data size of UHD videos is 416 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. Super resolution (SR) algorithms are widely used in video delivery applications to tackle these challenges. However, applying the super resolution model on UHD videos requires much more GPU memory, as compared with HD videos, which brings a significant challenge to existing systems.In this paper, we propose a deep compression framework named Pearl, which utilizes the power of deep learning to compress UHD videos. New channel-based super resolution models are developed to overcome the GPU memory shortage problem. In pearl, instead of applying the traditional RGB-based super resolution model, three separate super resolution models are trained based on the Y, U, and V channels of UHD videos. These super resolution models are used to reconstruct a UHD video from a low-resolution video. With Pearl, super resolution algorithms can be successfully applied to UHD videos. As a result, the data size of UHD videos can be significantly reduced during network transmission. At the same time, the efficiency of video encoding and decoding can also be improved with Pearl. To the best of our knowledge, Pearl is the first deep learning driven compression framework on UHD videos. We evaluate the performance of Pearl with extensive experiments. In all considered scenarios, Pearl can compress up to 95% of video data size during the video transmission and achieve 2.4 times faster, as compared with existing systems1.