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 | |
| 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 | United States |
| City | Baltimore |
| Period | 17/5/28 → 17/5/31 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
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
}
TY - GEN
T1 - Measurement-domain intra prediction framework for compressively sensed images
AU - Zhou, Jianbin
AU - Zhou, Dajiang
AU - Guo, Li
AU - Yoshimura, Takeshi
AU - Goto, Satoshi
PY - 2017/9/25
Y1 - 2017/9/25
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85032672497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032672497&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2017.8050262
DO - 10.1109/ISCAS.2017.8050262
M3 - Conference contribution
AN - SCOPUS:85032672497
BT - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
ER -