TY - GEN
T1 - Compressive-Sensed Image Coding via Multi-layer Closed-Loop Prediction
AU - Chen, Zan
AU - Hou, Xingsong
AU - Shao, Ling
AU - Huang, Yuan
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/10
Y1 - 2019/5/10
N2 - These years have seen the advance of compressive sensing (CS), but the CS-based image coding scheme still has a poor rate-distortion (R-D) performance compared with the traditional image coding techniques. In this paper, we propose an image coding scheme based on the CS paradigm via multi-layer closed-loop prediction. In the scheme, we divide CS measurements into multi-layers and predict a particular layer's measurements with all its preceding layers' measurements, which can reduce the redundancies between CS measurements efficiently. The produced measurement residuals are then quantized into binary codes, which are tremendously reduced compared to quantizing the CS measurements directly. Furthermore, We provide a non-local low-rank CS reconstruction algorithm corresponding to our multi-layer closed-loop prediction scheme. Experimental results verify that the proposed scheme can significantly outperform JPEG2000, and the reconstruction quality of our scheme is no worse or even better than that of HEVC-Intra.
AB - These years have seen the advance of compressive sensing (CS), but the CS-based image coding scheme still has a poor rate-distortion (R-D) performance compared with the traditional image coding techniques. In this paper, we propose an image coding scheme based on the CS paradigm via multi-layer closed-loop prediction. In the scheme, we divide CS measurements into multi-layers and predict a particular layer's measurements with all its preceding layers' measurements, which can reduce the redundancies between CS measurements efficiently. The produced measurement residuals are then quantized into binary codes, which are tremendously reduced compared to quantizing the CS measurements directly. Furthermore, We provide a non-local low-rank CS reconstruction algorithm corresponding to our multi-layer closed-loop prediction scheme. Experimental results verify that the proposed scheme can significantly outperform JPEG2000, and the reconstruction quality of our scheme is no worse or even better than that of HEVC-Intra.
KW - Compressive sense
KW - Image coding
KW - Multi Layer Prediction
UR - https://www.scopus.com/pages/publications/85066297941
U2 - 10.1109/DCC.2019.00074
DO - 10.1109/DCC.2019.00074
M3 - 会议稿件
AN - SCOPUS:85066297941
T3 - Data Compression Conference Proceedings
SP - 562
BT - Proceedings - DCC 2019
A2 - Marcellin, Michael W.
A2 - Serra-Sagrista, Joan
A2 - Bilgin, Ali
A2 - Storer, James A.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Data Compression Conference, DCC 2019
Y2 - 26 March 2019 through 29 March 2019
ER -