Low bit rate SAR image coding based on adaptive multiscale Bandelets and cooperative decision

  • Authors:

  • Shuyuan Yang;Yanxiong Lu;Min Wang;Licheng Jiao

  • Affiliations:

  • Department of Electrical Engineering, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China;Department of Electrical Engineering, National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China

  • Venue:
  • Signal Processing
  • Year:
  • 2009

Quantified Score

Hi-index 0.08

Visualization

Abstract

As an adaptive approximation tool, bandelets exhibit enormous potential in image compression. In this paper, we propose a low complexity adaptive multiscale bandelets transform (AMBT) for synthetic aperture radar (SAR) image compression. Different from optical images, SAR images carry information in low frequency bands as well as high frequency bands. So in our proposed approach multilevel wavelet packet decomposition is performed on images firstly, and then dyadic partition is employed to get some squares on which to perform a bandeletization subsequently. The determination of the optimal direction of geometric flow is one of the most important issues in the implementation of bandelets. Here a cooperative decision of the flow direction is adopted to obtain accurate flows. Because the proposed approach can avoid the bottom to top pruning algorithm of wavelet quadtree and the exhaustive searching of geometric flows in the second generation bandelets, it is of low complexity and thus can be implemented rapidly. Moreover, the proposed AMBT exhibits good performance at low bit rate because of the efficient representation of bandelet in capturing geometrics in image. Finally some experiments were carried out on some SAR images and the results show that our proposed scheme outperforms wavelet and the second generation bandelet methods in both PSNR and time consumption.