title: Efficient lossy compression for compressive sensing acquisition of images in compressive sensing imaging systems.
abstract: Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image acquisition, the general image compression solution may not work well. In this paper, we propose an efficient lossy compression solution for CS acquisition of images by considering the distinctive features of the CSI. First, we design an adaptive compressive sensing acquisition method for images according to the sampling rate, which could achieve better CS reconstruction quality for the acquired image. Second, we develop a universal quantization for the obtained CS measurements from CS acquisition without knowing any a priori information about the captured image. Finally, we apply these two methods in the CSI system for efficient lossy compression of CS acquisition. Simulation results demonstrate that the proposed solution improves the rate-distortion performance by 0.4~2 dB comparing with current state-of-the-art, while maintaining a low computational complexity.
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. xiangweili09@gmail.com.
authors.forename: Xiangwei
authors.initials: X
authors.lastname: Li
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. xglan@mail.xjtu.edu.cn.
authors.forename: Xuguang
authors.initials: X
authors.lastname: Lan
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. mengyang@mail.xjtu.edu.cn.
authors.forename: Meng
authors.initials: M
authors.lastname: Yang
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. jrxue@mail.xjtu.edu.cn.
authors.forename: Jianru
authors.initials: J
authors.lastname: Xue
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. nnzheng@mail.xjtu.edu.cn.
authors.forename: Nanning
authors.initials: N
authors.lastname: Zheng