Complex SAR Image Compression Using Entropy-Constrained Dictionary Learning and Universal Trellis Coded Quantization  

作　　者：ZHAN Xin ZHANG Rong 

机构地区：[1]Department of Electronic Engineering and Information Science,University of Science and Technology of China [2]Key Laboratory of Electromagnetic Space Information,Chinese Academy of Sciences

出　　处：《Chinese Journal of Electronics》2016年第4期686-691,共6页电子学报（英文版）

基　　金：supported by the National Basic Research Program of China (973 Program)(No.2010CB731904);the National Natural Science Foundation of China(No.61172154)

摘　　要：In this paper,an Entropy-constrained dictionary learning algorithm(ECDLA) is introduced for efficient compression of Synthetic aperture radar(SAR) complex images.ECDLA RI encodes the Real and imaginary parts of the images using ECDLA and sparse representation,and ECDLA AP encodes the Amplitude and phase parts respectively.When compared with the compression method based on the traditional Dictionary learning algorithm(DLA),ECDLA RI improves the Signal-to-noise ratio(SNR) up to 0.66 dB and reduces the Mean phase error(MPE) up to 0.0735 than DLA RI.With the same MPE,ECDLA AP outperforms DLA AP by up to 0.87 dB in SNR.Furthermore,the proposed method is also suitable for real-time applications.In this paper,an Entropy-constrained dictionary learning algorithm（ECDLA） is introduced for efficient compression of Synthetic aperture radar（SAR） complex images.ECDLA RI encodes the Real and imaginary parts of the images using ECDLA and sparse representation,and ECDLA AP encodes the Amplitude and phase parts respectively.When compared with the compression method based on the traditional Dictionary learning algorithm（DLA）,ECDLA RI improves the Signal-to-noise ratio（SNR） up to 0.66 dB and reduces the Mean phase error（MPE） up to 0.0735 than DLA RI.With the same MPE,ECDLA AP outperforms DLA AP by up to 0.87 dB in SNR.Furthermore,the proposed method is also suitable for real-time applications.

关 键 词：Synthetic aperture radar image compression Dictionary learning Sparse representation 

分 类 号：TN957.52[电子电信—信号与信息处理]