ZHAN Xin, ZHANG Rong. Complex SAR Image Compression Using Entropy-Constrained Dictionary Learning and Universal Trellis Coded Quantization[J]. Chinese Journal of Electronics, 2016, 25(4): 686-691. doi: 10.1049/cje.2016.07.015
Citation: ZHAN Xin, ZHANG Rong. Complex SAR Image Compression Using Entropy-Constrained Dictionary Learning and Universal Trellis Coded Quantization[J]. Chinese Journal of Electronics, 2016, 25(4): 686-691. doi: 10.1049/cje.2016.07.015

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

doi: 10.1049/cje.2016.07.015
Funds:  This work is supported by the National Basic Research Program of China (973 Program) (No.2010CB731904), and the National Natural Science Foundation of China (No.61172154).
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  • Corresponding author: ZHANG Rong (corresponding author) received the M.E. and Ph.D. degrees in signal and information processing from Hefei University of Technology and University of Science and Technology of China in 1995 and 1998, respectively. She is currently an associate professor at University of Science and Technology of China. Her research interests focus on image processing of remote sensing. (Email:zrong@ustc.edu.cn)
  • Received Date: 2014-05-15
  • Rev Recd Date: 2014-07-07
  • Publish Date: 2016-07-10
  • 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 0.66dB 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 0.87dB in SNR. Furthermore, the proposed method is also suitable for real-time applications.
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