Framework and VLSI architecture of measurement-domain intra prediction for compressively sensed visual contents

Jianbin Zhou, Dajiang Zhou, Li Guo, Takeshi Yoshimura, Satoshi Goto

研究成果: Article査読

1 被引用数 (Scopus)

抄録

This paper presents a measurement-domain intra prediction coding framework that is compatible with compressive sensing (CS)-based image sensors. In this framework, we propose a low-complexity intra prediction algorithm that can be directly applied to measurements captured by the image sensor. We proposed a structural random 0/1 measurement matrix, embedding the block boundary information that can be extracted from the measurements for intra prediction. Furthermore, a low-cost Very Large Scale Integration (VLSI) architecture isimplemented for the proposed framework, by substituting the matrix multiplication with shared adders and shifters. The experimental results show that our proposed framework can compress the measurements and increase coding efficiency, with 34.9% BD-rate reduction compared to the direct output of CS-based sensors. The VLSI architecture of the proposed framework is 9.1 Kin area, and achieves the 83% reduction in size of memory bandwidth and storage for the line buffer. This could significantly reduce both the energy consumption and bandwidth in communication of wireless camera systems, which are expected to be massively deployed in the Internet of Things (IoT) era.

本文言語English
ページ(範囲)2869-2877
ページ数9
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E100A
12
DOI
出版ステータスPublished - 2017 12 1

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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