Interweaving Permutation Meets Block Compressed Sensing  

作　　者：ZHANG Bo LIU Yulin JING Xiaojun ZHUANG Jie WANG Kai 

机构地区：[1]Communication NCO Academy, Army Engineering University, Chongqing 400035, China [2]Beijing University of Posts and Telecommunications, Beijing 100876, China [3]University of Electronic Science and Technology of China, Chengdu 611731, China

出　　处：《Chinese Journal of Electronics》2018年第5期1056-1062,共7页电子学报（英文版）

基　　金：supported by the Program for New Century Excellent Talents in University of China(No.NCET-11-0873);the Key Project of Chongqing Natural Science Foundation(No.CSTC2011BA2016);the Program for Fundamental and Advanced Research of Chongqing(No.cstc2013jcyj A40045)

摘　　要：Traditional Block compressed sensing(BCS) schemes encode nature images via a fixed sampling rate without taking the sparsity level differences among the blocks into consideration. In order to improve the sampling efficiency, a permutation-based BCS scheme with separate reconstruction is considered in this paper. The error performance bound of BCS scheme is carefully analyzed, and it is revealed that the smaller the maximum block sparsity level of the 2D signal is, the better reconstruction performance the algorithm has. According to the theoretical analysis result, an interweaving-permutationbased BCS strategy is investigated. In the proposed approach, the maximum block sparsity level of the 2D signal can be reduced significantly by interweaving permutation. As a result, better reconstruction performance can be achieved. Simulation results show that the proposed approach improves the Peak signal-to-noise ratio(PSNR)of reconstructed-images significantly.Traditional Block compressed sensing（BCS） schemes encode nature images via a fixed sampling rate without taking the sparsity level differences among the blocks into consideration. In order to improve the sampling efficiency, a permutation-based BCS scheme with separate reconstruction is considered in this paper. The error performance bound of BCS scheme is carefully analyzed, and it is revealed that the smaller the maximum block sparsity level of the 2D signal is, the better reconstruction performance the algorithm has. According to the theoretical analysis result, an interweaving-permutationbased BCS strategy is investigated. In the proposed approach, the maximum block sparsity level of the 2D signal can be reduced significantly by interweaving permutation. As a result, better reconstruction performance can be achieved. Simulation results show that the proposed approach improves the Peak signal-to-noise ratio（PSNR）of reconstructed-images significantly.

关 键 词：Block compressed sensing Interweaving permutation Image compression Image coding 

分 类 号：TN911.7[电子电信—通信与信息系统]