Perceptual Compression for Video Storage and Processing Systems
Perceptual Compression for Video Storage and Processing Systems
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DOI：
10.1145/3357223.3362725
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Amrita Mazumdar;Brandon Haynes;M. Balazinska;L. Ceze;Alvin Cheung;M. Oskin
中科院分区：
文献类型：
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作者：
Amrita Mazumdar;Brandon Haynes;M. Balazinska;L. Ceze;Alvin Cheung;M. Oskin
压缩视频占互联网流量的70%，视频上传增长率远远超过计算和存储改进趋势。过去在利用感知线索如显着性方面的工作，即，观看者集中其感知注意力的区域，在保持感知质量的同时减小了压缩视频大小，但是需要对视频编解码器进行显著改变，并且忽略了该感知信息的数据管理。在本文中，我们提出了Vignette，一种压缩技术和存储管理器，用于云端基于感知的视频压缩。Vignette补充了现成的压缩软件和硬件编解码器实现。Vignette的压缩技术使用神经网络来预测转码过程中使用的显着性信息，其存储管理器将感知信息集成到视频存储系统中。我们的研究结果证明了将人类视觉系统的信息嵌入云视频存储系统架构的好处。
Compressed videos constitute 70% of Internet traffic, and video upload growth rates far outpace compute and storage improvement trends. Past work in leveraging perceptual cues like saliency, i.e., regions where viewers focus their perceptual attention, reduces compressed video size while maintaining perceptual quality, but requires significant changes to video codecs and ignores the data management of this perceptual information. In this paper, we propose Vignette, a compression technique and storage manager for perception-based video compression in the cloud. Vignette complements off-the-shelf compression software and hardware codec implementations. Vignette's compression technique uses a neural network to predict saliency information used during transcoding, and its storage manager integrates perceptual information into the video storage system. Our results demonstrate the benefit of embedding information about the human visual system into the architecture of cloud video storage systems.