TY - GEN
T1 - Universal and low-complexity quantizer design for compressive sensing image coding
AU - Li, Xiangwei
AU - Lan, Xuguang
AU - Yang, Meng
AU - Xue, Jianru
AU - Zheng, Nanning
PY - 2013
Y1 - 2013
N2 - Compressive sensing imaging (CSI) is a new framework for image coding, which enables acquiring and compressing a scene simultaneously. The CS encoder shifts the bulk of the system complexity to the decoder efficiently. Ideally, implementation of CSI provides lossless compression in image coding. In this paper, we consider the lossy compression of the CS measurements in CSI system. We design a universal quantizer for the CS measurements of any input image. The proposed method firstly establishes a universal probability model for the CS measurements in advance, without knowing any information of the input image. Then a fast quantizer is designed based on this established model. Simulation result demonstrates that the proposed method has nearly optimal rate-distortion (R∼D) performance, meanwhile, maintains a very low computational complexity at the CS encoder.
AB - Compressive sensing imaging (CSI) is a new framework for image coding, which enables acquiring and compressing a scene simultaneously. The CS encoder shifts the bulk of the system complexity to the decoder efficiently. Ideally, implementation of CSI provides lossless compression in image coding. In this paper, we consider the lossy compression of the CS measurements in CSI system. We design a universal quantizer for the CS measurements of any input image. The proposed method firstly establishes a universal probability model for the CS measurements in advance, without knowing any information of the input image. Then a fast quantizer is designed based on this established model. Simulation result demonstrates that the proposed method has nearly optimal rate-distortion (R∼D) performance, meanwhile, maintains a very low computational complexity at the CS encoder.
KW - Compressive sensing imaging
KW - Gaussian distribution
KW - image coding
KW - quantization
UR - https://www.scopus.com/pages/publications/84893696994
U2 - 10.1109/VCIP.2013.6706403
DO - 10.1109/VCIP.2013.6706403
M3 - 会议稿件
AN - SCOPUS:84893696994
SN - 9781479902903
T3 - IEEE VCIP 2013 - 2013 IEEE International Conference on Visual Communications and Image Processing
BT - IEEE VCIP 2013 - 2013 IEEE International Conference on Visual Communications and Image Processing
T2 - 2013 IEEE International Conference on Visual Communications and Image Processing, IEEE VCIP 2013
Y2 - 17 November 2013 through 20 November 2013
ER -