Measurement-domain intra prediction framework for compressively sensed images

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

doi:10.1109/ISCAS.2017.8050262

Measurement-domain intra prediction framework for compressively sensed images

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

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 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 language	English
Title of host publication	IEEE International Symposium on Circuits and Systems
Subtitle of host publication	From Dreams to Innovation, ISCAS 2017 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467368520
DOIs	https://doi.org/10.1109/ISCAS.2017.8050262
Publication status	Published - 2017 Sep 25
Event	50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States Duration: 2017 May 28 → 2017 May 31

Other

Other	50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Country/Territory	United States
City	Baltimore
Period	17/5/28 → 17/5/31

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/ISCAS.2017.8050262

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

Measurement-domain intra prediction framework for compressively sensed images. / Zhou, Jianbin; Zhou, Dajiang; Guo, Li; Yoshimura, Takeshi; Goto, Satoshi.

IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 8050262.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017, Baltimore, United States, 17/5/28. https://doi.org/10.1109/ISCAS.2017.8050262

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AB - 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.

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Measurement-domain intra prediction framework for compressively sensed images

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