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

Abstract

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.