干涉高光谱图像高效压缩技术研究
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摘要
干涉高光谱技术是目前航天遥感领域内具有实用性价值的成像光谱技术,能够获得丰富的被测目标的空间和光谱信息,被广泛应用于环境监测、地质、气象和军事等领域。作为一种三维图像,其海量的数据导致存储与有限带宽信道上的传输非常困难,因此必须针对其数据特点设计高效的压缩编码方案。
针对干涉高光谱图像航天应用需求,论文从两个角度对干涉高光谱图像编码技术作了深入研究:一是从无损压缩编码的角度,对帧间预测和矢量量化进行改进与结合,研究高效的适合于干涉高光谱图像特点的无损压缩方法;二是从有损压缩编码的角度,分别对三维小波变换和熵编码方法进行改进,研究有效保持恢复图像光谱和空间信息的有损压缩方法。本文的主要工作及取得的研究成果如下:
1.提出一种针对联合调制干涉高光谱数据——LASIS (Large Aperture Static Imaging Spectrometer)数据的快速双向预测无损压缩方法。该方法由双向预测和熵编码两个模块有机结合形成。采用双向预测方法消除LASIS数据中的空间冗余信息和光谱冗余信息。并对双向预测运算优化,提高预测速度。采用运算速度快的区域编码对预测残差编码,提高编码速度。快速双向预测无损压缩方法在压缩比和运行速度方面均优于目前国际上流行的无损压缩方法,适合于实际航天星载压缩应用。
2.提出一种基于点到线模型的扩展矢量量化算法,并给出该算法在图像无损压缩中的实际压缩方案。该算法在LBG (Linde-Buzo-Gray algorithm)算法码书的基础上,利用点到线的垂线关系,对矢量进行量化表示,达到图像压缩的目的。在此基础上,我们进一步将扩展LBG算法应用于干涉高光谱图像压缩中,提出一种结合双向预测的矢量量化无损压缩算法,该方法可以显著提高干涉高光谱图像无损压缩的性能。
3.提出一种基于方向角预测的三维小波变换的有损压缩方法。这种三维小波变换的特点在于它将方向预测结合到三维提升小波中。每一维中提升小波的预测可在相关性最强的方向上进行而不需要总是局限在变换的方向上。实验证明,对于干涉高光谱图像这种方向性很强的立体图像序列,基于方向角预测的三维小波变换比原始三维提升小波变换有明显改进。使用基于方向角预测的三维小波变换的压缩方法比使用原始三维提升小波变换的方法在8倍压缩时的效果可提高1dB左右。
4.提出采用不等权重的率失真优化截取SPIHT (Set Partitioning in HierarchicalTrees)算法对干涉高光谱图像进行有损压缩。根据干涉高光谱图像在空间域和傅里叶域的特点,推导出空间域中随着光程差的增大,干涉图像对傅里叶域中光谱曲线的形状影响越大。基于上述研究结论,该算法在光程差方向上,逐渐提升率失真函数的斜率,增大对图像的保护程度,这样不仅弥补了SPIHT算法在码率分配上的不足,而且有效的保护了傅里叶域中的光谱信息。实验结果表明,该算法实现性能比传统算法在空间域和傅里叶域中都有所改进。
Interferential hyperspectral imaging technologies are considered as the practical imaging spectrometers in the current remote sensing. An interferential spectrometer, which can obtain plenty of both spatial and spectral information of a target, is widely used in environmental monitoring, geology, meteorology, military and other fields. As a three-dimensional image, the mass of data lead to the problems on transmission on the limited channel bandwidth and the storage on satellite. Therefore, it is necessary to design efficient compression methods according to the features of interferential hyperspectral image.
Based on the aerospace applications, the interferential hyperspectral image compression technologies are fully studied in this dissertation from two aspects: one is in lossless compression aspect to study the improvement and combination of prediction and Vector Quantization (VQ) to design high performance on compression for interferential hyperspectral image; the other is in lossy compression aspect to study three-dimensional wavelet transform and coding approaches which effectively maintain the spectral and spatial information of the reconstructed images.
The main contributions are as follows:
1. Fast dual-direction prediction lossless compression is proposed for united modulation interferential hyperspectral spectrometer data-Large Aperture Static Imaging Spectrometer (LASIS) data. This method consistes of two modules, dual-direction prediction and entropy coding. Dual-direction prediction can eliminate both spatial redundancy and spectral redundancy in LASIS data. Meanwhile an optimization is proposed for dual-direction prediction to enhance the speed of operation. Range coder is adopted to speed coding processing. Fast dual-direction prediction lossless compression method suitable for practical application of space-board compression outperforms many current international popular lossless compression methods both on compression ratio and speed.
2. A point to line model for the expansion of VQ algorithm is proposed. Meanwhile a lossless compression scheme using this expansion of VQ is carried out on interferential hyperspectral image. Based on Linde-Buzo-Gray (LBG) algorithm, the expansion of VQ makes use of perpendicular connecting the point to the line to represent vector for the quantization. Combined with dual-direction prediction method, the lossless compression method of dual-direction prediction expansion VQ can remarkably improve the compression performance of interferential hyperspectral image.
3. A three-dimensional (3D) orientation prediction filter in lifting structure is proposed for LASIS data. The novelty of this 3D-wavelet is that it combines directional prediction into 3D-wavelet. In this way, the prediction selects the most correlative pixel rather than pixels limited on the prediction direction. The experimental results show that this 3D orientation prediction wavelet improves the performance of wavelet obviously, particularly on the LASIS image with quite severe direction.
4. Weighted Rate-Distortion Optimization for Set Partitioning in Hierarchical Trees (SPIHT) is proposed for interferential hyperspectral image lossy compression. According to the traits of images in sptial domain and Fourier domain, it is testified that pixels on the large optical path difference are important for the shape of spectrum. So a rate-distortion optimizer for the SPIHT is designed to give an adaptive lifting of the rate-distortion slopes, which not only makes up the deficiency of bit allocation in SPIHT, but also protects the spectral information efficiently. The experimental results show that the proposed algorithm achieves improved performance over the conventional algorithms both in sptial domain and Fourier domain.
针对干涉高光谱图像航天应用需求,论文从两个角度对干涉高光谱图像编码技术作了深入研究:一是从无损压缩编码的角度,对帧间预测和矢量量化进行改进与结合,研究高效的适合于干涉高光谱图像特点的无损压缩方法;二是从有损压缩编码的角度,分别对三维小波变换和熵编码方法进行改进,研究有效保持恢复图像光谱和空间信息的有损压缩方法。本文的主要工作及取得的研究成果如下:
1.提出一种针对联合调制干涉高光谱数据——LASIS (Large Aperture Static Imaging Spectrometer)数据的快速双向预测无损压缩方法。该方法由双向预测和熵编码两个模块有机结合形成。采用双向预测方法消除LASIS数据中的空间冗余信息和光谱冗余信息。并对双向预测运算优化,提高预测速度。采用运算速度快的区域编码对预测残差编码,提高编码速度。快速双向预测无损压缩方法在压缩比和运行速度方面均优于目前国际上流行的无损压缩方法,适合于实际航天星载压缩应用。
2.提出一种基于点到线模型的扩展矢量量化算法,并给出该算法在图像无损压缩中的实际压缩方案。该算法在LBG (Linde-Buzo-Gray algorithm)算法码书的基础上,利用点到线的垂线关系,对矢量进行量化表示,达到图像压缩的目的。在此基础上,我们进一步将扩展LBG算法应用于干涉高光谱图像压缩中,提出一种结合双向预测的矢量量化无损压缩算法,该方法可以显著提高干涉高光谱图像无损压缩的性能。
3.提出一种基于方向角预测的三维小波变换的有损压缩方法。这种三维小波变换的特点在于它将方向预测结合到三维提升小波中。每一维中提升小波的预测可在相关性最强的方向上进行而不需要总是局限在变换的方向上。实验证明,对于干涉高光谱图像这种方向性很强的立体图像序列,基于方向角预测的三维小波变换比原始三维提升小波变换有明显改进。使用基于方向角预测的三维小波变换的压缩方法比使用原始三维提升小波变换的方法在8倍压缩时的效果可提高1dB左右。
4.提出采用不等权重的率失真优化截取SPIHT (Set Partitioning in HierarchicalTrees)算法对干涉高光谱图像进行有损压缩。根据干涉高光谱图像在空间域和傅里叶域的特点,推导出空间域中随着光程差的增大,干涉图像对傅里叶域中光谱曲线的形状影响越大。基于上述研究结论,该算法在光程差方向上,逐渐提升率失真函数的斜率,增大对图像的保护程度,这样不仅弥补了SPIHT算法在码率分配上的不足,而且有效的保护了傅里叶域中的光谱信息。实验结果表明,该算法实现性能比传统算法在空间域和傅里叶域中都有所改进。
Interferential hyperspectral imaging technologies are considered as the practical imaging spectrometers in the current remote sensing. An interferential spectrometer, which can obtain plenty of both spatial and spectral information of a target, is widely used in environmental monitoring, geology, meteorology, military and other fields. As a three-dimensional image, the mass of data lead to the problems on transmission on the limited channel bandwidth and the storage on satellite. Therefore, it is necessary to design efficient compression methods according to the features of interferential hyperspectral image.
Based on the aerospace applications, the interferential hyperspectral image compression technologies are fully studied in this dissertation from two aspects: one is in lossless compression aspect to study the improvement and combination of prediction and Vector Quantization (VQ) to design high performance on compression for interferential hyperspectral image; the other is in lossy compression aspect to study three-dimensional wavelet transform and coding approaches which effectively maintain the spectral and spatial information of the reconstructed images.
The main contributions are as follows:
1. Fast dual-direction prediction lossless compression is proposed for united modulation interferential hyperspectral spectrometer data-Large Aperture Static Imaging Spectrometer (LASIS) data. This method consistes of two modules, dual-direction prediction and entropy coding. Dual-direction prediction can eliminate both spatial redundancy and spectral redundancy in LASIS data. Meanwhile an optimization is proposed for dual-direction prediction to enhance the speed of operation. Range coder is adopted to speed coding processing. Fast dual-direction prediction lossless compression method suitable for practical application of space-board compression outperforms many current international popular lossless compression methods both on compression ratio and speed.
2. A point to line model for the expansion of VQ algorithm is proposed. Meanwhile a lossless compression scheme using this expansion of VQ is carried out on interferential hyperspectral image. Based on Linde-Buzo-Gray (LBG) algorithm, the expansion of VQ makes use of perpendicular connecting the point to the line to represent vector for the quantization. Combined with dual-direction prediction method, the lossless compression method of dual-direction prediction expansion VQ can remarkably improve the compression performance of interferential hyperspectral image.
3. A three-dimensional (3D) orientation prediction filter in lifting structure is proposed for LASIS data. The novelty of this 3D-wavelet is that it combines directional prediction into 3D-wavelet. In this way, the prediction selects the most correlative pixel rather than pixels limited on the prediction direction. The experimental results show that this 3D orientation prediction wavelet improves the performance of wavelet obviously, particularly on the LASIS image with quite severe direction.
4. Weighted Rate-Distortion Optimization for Set Partitioning in Hierarchical Trees (SPIHT) is proposed for interferential hyperspectral image lossy compression. According to the traits of images in sptial domain and Fourier domain, it is testified that pixels on the large optical path difference are important for the shape of spectrum. So a rate-distortion optimizer for the SPIHT is designed to give an adaptive lifting of the rate-distortion slopes, which not only makes up the deficiency of bit allocation in SPIHT, but also protects the spectral information efficiently. The experimental results show that the proposed algorithm achieves improved performance over the conventional algorithms both in sptial domain and Fourier domain.
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