Abstract：Tetrolet transform, as one of the mutiscale geometric analysis method, can be used to represent natural images sparsely. However, SAR images consist of textures, leading to that the high frequency coefficients of Tetrolet still have large amplitude, and thus affects the sparse representation performance of SAR images seriously. In this paper, a new transform named Tetrolet Packet is proposed. At first, the high frequency coefficients are reordered, and then the high frequency sub-bands are decomposed using multi-Tetrolet transform according to a entropy based cost function. So a optimal Tetrolet tree structure can be found, and the energy of coefficients are concentrated with less direction informations in order to get better performance of SAR image compression. In the experiment, the SAR images are reconstructed with the same number of coefficients of Tetrolet transform and Tetrolet Packet respectively, and their reconstruction performances are compared. The results show that the proposed method Tetrolet Packet outperforms Tetrolet in the sense of sparse representation ability for SAR images, both in visual quality and PSNR. Furthermore, transform coefficients of both methods have similar zero-tree structures, and the compression performance of the proposed method is investigated by employing Modified-SPIHT.
2012, Vol. 34 
