Content-Adaptive Memory for Viewer-Aware Energy-Quality Scalable Mobile Video Systems

Jonathon Edstrom, Yifu Gong, Ali Ahmad Haidous, Brittney Humphrey, Mark E. McCourt, Yiwen Xu, Jinhui Wang, Na Gong

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number8681040
Pages (from-to)47479-47493
Number of pages15
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • Energy-quality adaptation
  • Video content
  • Video memory
  • Viewer's experience
  • Viewer-aware bit truncation

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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