部分饱和SAR原始数据压缩

Compression on Fractional Saturation SAR RAW Data

分块自适应量化(BAQ)算法在实际应用中碰到的问题是SAR原始数据含有较多饱和成分的量化。文中分析了BAQ对这类数据产生较大误差的原因。根据数据的特点,提出了一种对传统BAQ进行改良的压缩算法——部分饱和BAQ(FSBAQ).实验表明,这种方法在不改变压缩比的情况下,对存在一定饱和的数据都能提高4.5dB以上的量化信噪比.文中给出了一条选择量化形式的曲线。并给出用传统BAQ算法及该文给出的算法对一块SAR原始数据进行量化和结果对比。

A quantizaiton method is presented to aim at the fractional saturation Synthetic Aperture Radar (SAR) raw data. Norm distribution is the basis of Block Adaptive Quantization (BAQ) algorithm. But in the application of BAQ, some trouble may encounter, one of these is fractional saturation of raw data. A compression algorithm based on the traditional BAQ algorithm is presented, which can select different ways to quantify the raw data. This algorithm reduces the error and increases the Signal to Quantization Noise Ratio (SQNR) without influence the compression ratio. Finally, a block of real SAR raw data are processed by the two algorithms and the result is compared with each other.