小波域的合成孔径雷达原始数据压缩
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摘要
作为一种高分辨率主动微波成像雷达,合成孔径雷达(SAR)原始数据量极大,难以符合存储或传输的要求,数据压缩是保证SAR系统指标同时减小数据量的有效方法。在分析SAR原始数据的统计特性和功率特性的基础上,本文从二进制小波变换、二进制小波包变换、多进制小波变换和多小波变换4个方面,较系统地研究了小波域的SAR原始数据压缩算法,以期获得更高效、实用的压缩方法。
木文研究了二进制小波变换的相关理论,针对分解系数的量化提出了二进制小波分解系数的熵约束分块自适应量化(WT-ECBAQ)和网格编码量化(WT-TCQ)。二进制小波变换采用非均匀分辨率对信号进行时间(空间)和频率分析,变换系数能量相对集中,因此在变换域内进行比特分配,可以实现良好的编码效果。WT-ECBAQ根据分块方差自适应地调节量化器参数,通过量化降低数据熵值,利用熵编码器进一步降低码率,达到所需压缩比,试验表明此算法压缩性能较好。WT-TCQ算法利用TCQ算法复杂性中等而性能与矢量量化相当的优点,可以获得更高的压缩信噪比,编码率较大时,压缩算法的性能优势更明显。
本文通过研究二进制小波包变换的基本理论,分析分解系数的特点,提出二进制小波包分解系数的分块自适应量化(WPT-BAQ)和二进制小波包分解系数的TCQ量化(WPT-TCQ)。二进制小波包变换调整分解结构,选择性地分解每层的高频分量,克服了二进制小波变换高频部分频率分辨率低的缺点,对不同子带分配不同码率能够更有效地利用编码资源,提高压缩性能。WPT-BAQ根据分块方差调节Lloyd-Max最佳量化器的量化参数,充分利用量化资源。WPT-TCQ利用分解系数间有限的相关性和TCQ自身的优势获得更好的压缩效果。压缩试验说明WPT-TCQ算法性能优于WPT-BAQ算法,且编码率高时这种优势更明显。
本丈在研究多进制小波变换的基础上,提出了多进制小波分解系数的BAQ量化算法。多进制小波变换更直接地细致划分信号频带,有利于捕获信号的细节,与二进制小波仅有一个尺度函数和一个小波函数不同,M进制小波具有一
As a high-resolution active microwave imaging radar, Synthetic Aperture Radar (SAR) system generates a big amount of data, which is hard to be transmitted or stored because of limited processing or downlink capacity, so there is an urgent need to reduce the data column. Data compression is an effective method to solve the problem and keep SAR system quota at the same time. After analyzing statistics and power characteristics of SAR raw data, the paper studies data compression of SAR raw data in wavelet domain, from the point of 2-band wavelet transform, 2-band wavelet packet transform, multiband wavelet transform and multiwavelet transform.The paper studies 2-band wavelet transform theory and advances two quantization algorithms according to characterics of the transform coefficients, WT-ECBAQ and WT-TCQ. As the most common method, 2-band wavelet transform analyzes time and frequency information of signals with non-uniform resolution, concentrates energy of transform coefficients, and allocates bit resource to gain good performance. WT-ECBAQ adjusts parameters of quantizer adaptively in line with variance of data block, uses entopy-constrained quantizer to decrease data entropy and gain requested compression ratio. Compression experiments have proved good compression performance. WT-TCQ uses TCQ to quantize wavelet coefficients and gains better compression performance. The advantages of this algorithm are more obvious at large rate.The paper makes comprehensive research into 2-band wavelet packet transform, analyzs the features of transform coefficients and puts forward WPT-BAQ and WPT-TCQ. 2-band wavelet packet transform modulates decomposition structure and selectively decomposes high-frequency component at every level. It conquers the shortcomings of 2-band wavelet transform which has low resolution at high
frequency, and uses code resource effectively by distributing bit ratio to different band. WPT-BAQ tunes quantization parameters of Lloyd-Max quantizer adaptively according to block variances and makes good use of quantization resource. WPT-TCQ obtains good compression performance using limited relativity and goodness of TCQ. Compression experiments show that the performance of WPT-TCQ is better than WPT-BAQ and the difference is much bigger at high rate.The paper quantizes M-band wavelet transform coefficients with BAQ based on the M-band wavelet transform theory. While 2-band wavelet has one scalar function and one wavelet function, M-band wavelet has one scalar function and M-l wavelet functions. So multiband wavelet transform can directly and finely divide signal frequency, which is useful to obtain signal details. The algorithm uses code resource reasonably to decrease quanzation distortion. Compression tests show good performance of this algorithm.Combining properties of SAR raw data and advantages of muhiwavelet, the paper quantizes muhiwavelet transform coefficients with BAQ and analyzes compression performance with experiments.As a new concept, muhiwavelet has vector scalar function and vector wavelet function, and it meets demands for symmetry, compacted support, vanishing moments and regularity needs at the same time.Because of good performance and complexity ratio, SAR raw data compression in wavelet domain is development direction for realtime data compression.
木文研究了二进制小波变换的相关理论,针对分解系数的量化提出了二进制小波分解系数的熵约束分块自适应量化(WT-ECBAQ)和网格编码量化(WT-TCQ)。二进制小波变换采用非均匀分辨率对信号进行时间(空间)和频率分析,变换系数能量相对集中,因此在变换域内进行比特分配,可以实现良好的编码效果。WT-ECBAQ根据分块方差自适应地调节量化器参数,通过量化降低数据熵值,利用熵编码器进一步降低码率,达到所需压缩比,试验表明此算法压缩性能较好。WT-TCQ算法利用TCQ算法复杂性中等而性能与矢量量化相当的优点,可以获得更高的压缩信噪比,编码率较大时,压缩算法的性能优势更明显。
本文通过研究二进制小波包变换的基本理论,分析分解系数的特点,提出二进制小波包分解系数的分块自适应量化(WPT-BAQ)和二进制小波包分解系数的TCQ量化(WPT-TCQ)。二进制小波包变换调整分解结构,选择性地分解每层的高频分量,克服了二进制小波变换高频部分频率分辨率低的缺点,对不同子带分配不同码率能够更有效地利用编码资源,提高压缩性能。WPT-BAQ根据分块方差调节Lloyd-Max最佳量化器的量化参数,充分利用量化资源。WPT-TCQ利用分解系数间有限的相关性和TCQ自身的优势获得更好的压缩效果。压缩试验说明WPT-TCQ算法性能优于WPT-BAQ算法,且编码率高时这种优势更明显。
本丈在研究多进制小波变换的基础上,提出了多进制小波分解系数的BAQ量化算法。多进制小波变换更直接地细致划分信号频带,有利于捕获信号的细节,与二进制小波仅有一个尺度函数和一个小波函数不同,M进制小波具有一
As a high-resolution active microwave imaging radar, Synthetic Aperture Radar (SAR) system generates a big amount of data, which is hard to be transmitted or stored because of limited processing or downlink capacity, so there is an urgent need to reduce the data column. Data compression is an effective method to solve the problem and keep SAR system quota at the same time. After analyzing statistics and power characteristics of SAR raw data, the paper studies data compression of SAR raw data in wavelet domain, from the point of 2-band wavelet transform, 2-band wavelet packet transform, multiband wavelet transform and multiwavelet transform.The paper studies 2-band wavelet transform theory and advances two quantization algorithms according to characterics of the transform coefficients, WT-ECBAQ and WT-TCQ. As the most common method, 2-band wavelet transform analyzes time and frequency information of signals with non-uniform resolution, concentrates energy of transform coefficients, and allocates bit resource to gain good performance. WT-ECBAQ adjusts parameters of quantizer adaptively in line with variance of data block, uses entopy-constrained quantizer to decrease data entropy and gain requested compression ratio. Compression experiments have proved good compression performance. WT-TCQ uses TCQ to quantize wavelet coefficients and gains better compression performance. The advantages of this algorithm are more obvious at large rate.The paper makes comprehensive research into 2-band wavelet packet transform, analyzs the features of transform coefficients and puts forward WPT-BAQ and WPT-TCQ. 2-band wavelet packet transform modulates decomposition structure and selectively decomposes high-frequency component at every level. It conquers the shortcomings of 2-band wavelet transform which has low resolution at high
frequency, and uses code resource effectively by distributing bit ratio to different band. WPT-BAQ tunes quantization parameters of Lloyd-Max quantizer adaptively according to block variances and makes good use of quantization resource. WPT-TCQ obtains good compression performance using limited relativity and goodness of TCQ. Compression experiments show that the performance of WPT-TCQ is better than WPT-BAQ and the difference is much bigger at high rate.The paper quantizes M-band wavelet transform coefficients with BAQ based on the M-band wavelet transform theory. While 2-band wavelet has one scalar function and one wavelet function, M-band wavelet has one scalar function and M-l wavelet functions. So multiband wavelet transform can directly and finely divide signal frequency, which is useful to obtain signal details. The algorithm uses code resource reasonably to decrease quanzation distortion. Compression tests show good performance of this algorithm.Combining properties of SAR raw data and advantages of muhiwavelet, the paper quantizes muhiwavelet transform coefficients with BAQ and analyzes compression performance with experiments.As a new concept, muhiwavelet has vector scalar function and vector wavelet function, and it meets demands for symmetry, compacted support, vanishing moments and regularity needs at the same time.Because of good performance and complexity ratio, SAR raw data compression in wavelet domain is development direction for realtime data compression.
引文
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[2] 张澄波,综合孔径雷达原理、系统分析与应用,第一版,北京,科学出版社,1989年
[3] 许织新,数据压缩,北京,国防工业出版社,1990年.
[4] 姜丹,信息论与编码,第一版,合肥,中国科学技术大学出版社,2001年
[5] 吴乐南,数据压缩,第一版,北京,电子工业出版社,2003年
[6] J. C. Culander, R. N. Mcdonough, Synthetic Aperture Radar Systems and Signal Processing, New York, John Willey & Sons, Inc., 1991: 288-294.
[7] Ronald Kwok, William T. K. Johnson, Block adaptive quantization of Magellan SAR data, IEEE Transaction on Geoscience and Remote Sensing, 1989(27): 375-383
[8] S. M. Parkes and H.L. Clifton, The compression of raw SAR and SAR image data, International Journal of Remote Sensing, 1999(20): 3563-3581
[9] U. Benz, K. Strodl, A. Moreira. A comparison of several algorithms for SAR raw data compression, IEEE Transaction on Geoscience and Remote Sensing, 1995(33): 1266-1276
[10] S. M. Parkes, Data compression and spacewire, Proceedings of the International Geoscience and Remote Sensing Symposium, 1999: 2267-2269
[11] B. L. Hunecutt, Spaceborne imaging Radar-C instrument, IEEE Transactions of Geoscience and Remote Sensing, 1989(27): 164-169
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[13] T. Algra, Compression of raw SAR data using entropy-constrained quantization, Proceedings of International Geoscience and Remote Sensing symposium, 2000: 2660-2662
[14] E.Magli and G. Olmo, Predictive coding of SAR phase history data, in Proceedings of the International Geoscience and Remote Sensing symposium, 2002: 1132-1134
[15] G. Kuduvali, M. Dutkiewicz, and I. Cumming, Synthetic aperture radar signal data compression using block adaptive quantization, Proceedings of the Goddard Science Information Management and Data Compression Workshop, 1994: 43-57
[16] R. M. Gray, Vector quantization, IEEE Acoustics, Speech, and Signal Processing Magazing, 1984(4): 4-29
[17] 孙圣和,陆哲明.矢量量化技术及应用.第一版,北京:科学出版社,2002年.
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