Measurement-domain intra prediction framework for compressively sensed images

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems
Subtitle of host publicationFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467368520
DOIs
Publication statusPublished - 2017 Sep 25
Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: 2017 May 282017 May 31

Other

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

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Zhou, J., Zhou, D., Guo, L., Yoshimura, T., & Goto, S. (2017). Measurement-domain intra prediction framework for compressively sensed images. In IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings [8050262] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2017.8050262