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

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.