摘要
在分析分块自适应量化(BAQ)压缩数据的分布特性、量化信噪比、动态范围及其对矢量量化数据压缩性能和运算量影响的基础上,对分块自适应矢量量化(BAVQ)压缩算法进行了改进,提出了采用高比特数BAQ压缩的方案。利用改进的BAVQ算法压缩高分辨率SAR原始数据的结果表明,在相同压缩比的条件下,该文提出的改进算法可获得更大的量化信噪比。解压缩数据生成的图像可以清晰地保留图像中的细节信息。
In this paper, the factors that affect the Block Adaptive Vector Quantization （BAVQ） compression performance are analyzed, which are the Signal to compression Noise Ratio （SNR） of Block Adaptive Quantization （BAQ）, the encoding algorithm computation complexity, the statistics and dynamic range of BAQ compressed data. Based on these factors, the paper proposes an improved BAVQ algorithm to compress Synthetic Aperture Radar （SAR） raw data. In the improved algorithm, the traditional block adaptive quantizer is replaced by more bits one. Using this improved BAVQ algorithm to compress high resolution SAR raw data, larger SNR values than the previous compression algorithms under the same compression ratio are obtained. The decompressed images reserve most of details on the images resulted from raw data.
出处
《电子与信息学报》
EI
CSCD
北大核心
2005年第8期1233-1236,共4页
Journal of Electronics & Information Technology
关键词
原始数据压缩
分块自适应矢量量化
高倍数压缩
Raw data compression, Block adaptive vector quantization, High compression ratio
