小波变换块自适应量化算法压缩SAR原始数据

Compression of SAR Raw Data with Wavelet Transform Block Adaptive Quantization

本文研究了如何使用基于小波变换的块自适应量化(WT-BAQ)算法压缩合成孔径雷达原始数据。结合一组实测SAR原始数据,进行了压缩和解压缩,并计算了数据域及图像域评价参数,给出了压缩算法最终所成的图像。并与块自适应量化(BAQ)、块自适应矢量量化(BAVQ)以及小波包变换块自适应量化(WPT-BAQ)方法进行了性能比较。实验表明,在相同比特率条件下,本文算法得到的数据域信噪比和图像域信噪比均比另外三种算法高。本文算法具有一定实用价值。最后文章给出了结论和将来进一步的工作。

This paper analyzed the algorithm of compressing synthetic aperture radar raw data using wavelet transform block adaptive quantization（WT-BAQ）. By using a real SAR raw data, compression and decompression are performed respectively. The quality parameters in data and image domain are achieved and the final SAR images are obtained. The comparison of quality parameters is made with block adaptive quantization （BAQ）, block adaptive vector quantization （BAVQ） and wavelet packets transform block adaptive quantization （WPT- BAQ）. The experiments manifest that with the same bit rate, SQNR and SDNR after using the algorithm proposed in this paper are higher than that of other three algorithms mentioned above. The WT-BAQ algorithm has practical value in some degree. In the end, conclusions are drawn and possible future works are given.