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Lossless hyperspectral image compression using wavelet transform based spectral decorrelation

机译:使用基于小波变换的光谱去相关性进行无损高光谱图像压缩

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Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of computational complexity and CR. Tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Two-dimensional images corresponding to each band is compressed using JPEG-LS algorithm. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for onboard lossless hyperspectral image compression.
机译:整数系数离散小波变换(DWT)滤波器被实现并作为频谱去相关器进行了研究。由于频谱去相关步骤的性能直接影响压缩比(CR),因此就计算复杂度和CR而言,采用最方便的频谱去相关器非常重要。使用AVIRIS图像数据集进行测试,并在无损高光谱压缩框架内显示与各个子带分解级别相对应的CR。使用JPEG-LS算法压缩与每个波段对应的二维图像。结果表明,具有五级频谱子带分解的Cohen-Daubechies-Feauveau(CDF)9/7整数系数小波变换将是板载无损高光谱图像压缩的有效频谱去相关器。

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