Measurement-domain intra prediction framework for compressively sensed images

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

研究成果: Conference contribution

6 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 the measurements captured by the image sensor. Moreover, we propose a structural random 0/1 measurement matrix, embedding the block boundary information that can be extracted from the measurements for intra prediction. Experiment results show that our proposed framework can compress the measurements and increase coding efficiency, with 30% BD-rate reduction compared to the direct output of CS based sensors. This can significantly save both the energy consumption and the bandwidth in communication of wireless camera systems to be massively deployed in the era of IoT.

本文言語English
ホスト出版物のタイトルIEEE International Symposium on Circuits and Systems
ホスト出版物のサブタイトルFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781467368520
DOI
出版ステータスPublished - 2017 9 25
イベント50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
継続期間: 2017 5 282017 5 31

Other

Other50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
CountryUnited States
CityBaltimore
Period17/5/2817/5/31

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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