Abstract
In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ CM×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.
| Original language | English |
|---|---|
| Title of host publication | 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9789881476821 |
| DOIs | |
| Publication status | Published - 2017 Jan 17 |
| Event | 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of Duration: 2016 Dec 13 → 2016 Dec 16 |
Other
| Other | 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju |
| Period | 16/12/13 → 16/12/16 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Information Systems
- Signal Processing
Cite this
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Sparse source recovery with graph in network coding. / Yu, Sung Bok; Choe, Yoonsik.
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820844.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Sparse source recovery with graph in network coding
AU - Yu, Sung Bok
AU - Choe, Yoonsik
PY - 2017/1/17
Y1 - 2017/1/17
N2 - In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ CM×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.
AB - In network coding, the successive original video frame data can be transmitted at once. However, if insufficient number of innovative packets are transmitted due to the packet loss or delay, network coding system is to be underdetermined. Thus, since network coding matrix (random coefficient matrix) is not invertible, original data cannot be recovered by matrix inversion. To solve this problem, in this paper, a new compressive sensing method with graph Laplacian regularizer is proposed, which exploits correlation between successive original video frame data. Experimental results demonstrate the effectiveness of proposed algorithm, implemented by the alternative direction multiplier method (ADMM) and show that PSNR values from reconstructed images are above 33dB with coding matrix Φ CM×N of which measurement M = 0.75N and 22dB with measurement M = 0.66N, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85013777455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013777455&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2016.7820844
DO - 10.1109/APSIPA.2016.7820844
M3 - Conference contribution
AN - SCOPUS:85013777455
BT - 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -