摘要
由于同相正交数据(In-phase and quadrature data,IQ data)原始数据的相关性很低,直接压缩原始数据是比较困难的。鉴于部分IQ数据的统计分布和合成孔径雷达(Synthetic Aperture Radar)数据的统计分布相似,参考SAR数据的压缩方法—分块自适应量化,针对部分IQ数据概率密度函数偏离高斯分布时分块自适应量化压缩算法性能下降的问题,提出了一种自适应标量-矢量量化压缩算法,对满足高斯分布特性的数据块采用标量量化,对不满足高斯分布特性的数据块采用矢量量化。实测数据的仿真,说明上述方法能够应用于IQ数据的压缩,并且各项性能指标均优于BAQ算法。
It is difficult to directly compress IQ data for the low relativity. In view of some IQ data statistical distribution and Synthetic Aperture Radar data are similar, this paper refers to synthetic aperture radar data compression algorithm, the Block adaptive quantization （BAQ） compression algorithm. In order to improve the performance of BAQ when some IQ data deviate from Gaussian distribution, therefore, an adaptive scalar - vector quantization com- pression algorithm is put forward in this paper. The data satisfying Gaussian distribution was compressed by BAQ, otherwise compressed by VQ. Experimental results indicate that the performance of the proposed algorithm is superior to BAQ algorithm.
出处
《计算机仿真》
北大核心
2017年第12期264-268,共5页
Computer Simulation
关键词
数据压缩
分块自适应量化
高斯分布
矢量量化
Data compression
Block adaptive quantization（BAQ）
Gaussian distribution
Vector quantization
