Content-Adaptive Memory for Viewer-Aware Energy-Quality Scalable Mobile Video Systems
Content-Adaptive Memory for Viewer-Aware Energy-Quality Scalable Mobile Video Systems
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DOI：
10.1109/access.2019.2908997
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
J. Edstrom;Y. Gong;Ali Ahmad Haidous;Brittney Humphrey;M. McCourt;Yiwen Xu;Jinhui Wang;Na Gong
中科院分区：
文献类型：
--
作者：
J. Edstrom;Y. Gong;Ali Ahmad Haidous;Brittney Humphrey;M. McCourt;Yiwen Xu;Jinhui Wang;Na Gong
移动设备在流媒体视频方面正变得越来越受欢迎，这些视频占据了互联网上所有数据流量的大部分。内存是移动视频处理系统中的关键组件，越来越多地主导着功耗。今天，存储器设计者仍然专注于硬件级的功率优化技术，这通常伴随着巨大的实现成本(例如，硅面积开销或性能损失)。在本文中，我们从观众的角度提出了一种视频内容感知的电能质量折衷的记忆技术。根据视频宏块特性对用户体验的影响，提出了两种简单有效的硬件自适应模型--决策树模型和Logistic回归模型。我们还实现了一种新颖的观众感知比特截断技术，该技术将对观众体验的影响降至最低，同时将能量质量适应引入到视频存储中。
Mobile devices are becoming ever more popular for streaming videos, which account for the majority of all the data traffic on the Internet. Memory is a critical component in mobile video processing systems, increasingly dominating the power consumption. Today, memory designers are still focusing on hardware-level power optimization techniques, which usually come with significant implementation cost (e.g., silicon area overhead or performance penalty). In this paper, we propose a video content-aware memory technique for power-quality tradeoff from viewer’s perspectives. Based on the influence of video macroblock characteristics on the viewer’s experience, we develop two simple and effective models–decision tree and logistic regression to enable hardware adaptation. We have also implemented a novel viewer-aware bit-truncation technique which minimizes the impact on the viewer’s experience, while introducing energy-quality adaptation to the video storage.