针对大孔径静态干涉成像光谱仪(LASIS)的成像特点, 提出了一种基于三维非对称等长树小波变换的无链表SPITH算法结合ROI的图像压缩方案。首先, 对干涉高光谱图像进行三维非对称等长树离散小波变换。其次, 采用ROI方法对主要的光谱系数进行保护。最后, 采用改进的三维无链表SPITH算法, 编码干涉高光谱图像的小波变换域。实验结果表明, 该方法在8∶1压缩比下, 获得大于40 dB的平均峰值信噪比, 同时有效地保护了光谱信息。

针对大孔径静态干涉成像光谱仪LASIS 的成像特点, 提出了一种基于三维小波变换的无链表SPITH 算法结合感兴趣谱段保护的图像压缩方案。首先, 对高光谱干涉图像序列进行三维非对称等长树离散小波变换。其次, 采用感兴趣谱段方法保护重要光谱信息。最后, 对无链表三维SPITH 算法进行改进, 以有效编码高光谱干涉图像的小波变换域。实验结果表明, 该方法在8:1 压缩比下, 获得大于40 dB 的峰值信噪比, 同时有效的保护了光谱信息。

针对大孔径静态干涉成像光谱仪的成像特点，提出了一种基于三维小波变换的3DSPIHT算法结合ROI的图像压缩方案.对干涉高光谱图像序列进行了三维非对称离散小波变换.采用ROI方法对主要的光谱系数进行提升，以保护光谱信息.最后，对3DSPIHT算法进行改进，以有效编码干涉高光谱图像的小波变换系数.实验结果表明，该方法在8:1压缩比下可获得大于40 dB的峰值信噪比，同时有效地保护了光谱信息.

将三维集合分裂嵌入块编码算法结合感兴趣区域的压缩方案用于大孔径静态干涉成像光谱仪图像压缩.首先，对高光谱干涉图像序列进行三维非对称离散小波变换.其次，定义零块树作为编码单位.采用感兴趣区域方法对不同的编码单位赋予不同的比特率，以保护光谱信息.最后，采用改进的三维集合分裂嵌入块编码算法分别对每个编码单位进行编码.实验结果表明，该方案在8:1压缩比下，获得大于40dB的峰值信噪比，同时有效地保护了光谱信息.该算法复杂度低，实时性好，满足大孔径静态干涉成像光谱仪系统图像压缩要求.

针对大孔径静态干涉成像光谱仪(LASIS)的成像特点,提出了一种支持感兴趣区域(ROI)的非对称三维分层树集划分(AT-3DSPIHT)压缩算法。首先,对高光谱干涉图像进行非对称三维离散小波变换。然后,根据光谱信息的分布特点,采用ROI方法对不同区域的变换系数赋予不同的编码精度,以保护光谱信息。最后,采用改进的三维分层树集划分(3DSPIHT)算法编码高光谱干涉图像的小波变换系数。实验结果表明,该方法在8:1压缩比下,获得大于40 dB的峰值信噪比,同时有效地保护了光谱信息。

As one of the next generation imaging spectrometers, interferential spectrometer has been paid much attention. With traditional spectrum compression methods, the hyperspectral images generated by interferential spectrometer can only be protected with better visual quality in spatial domain, but its optical applications in Fourier domain are often ignored. So the relation between the distortion in Fourier domain and the compression in spatial domain is analyzed in this letter. Based on this analysis, a novel coding scheme is proposed, which can compress data in spatial domain while reducing the distortion in Fourier domain. The bitstream of set partitioning in hierarchical trees (SPIHT) is truncated by adaptively lifting the rate-distortion slopes of zerotrees according to the priorities of optical path difference (OPD) based on rate-distortion optimization theory. Experimental results show that the proposed scheme can achieve better performance in Fourier domain while maintaining the image quality in spatial domain.

在高光谱图像压缩中,由于波段预测后各波段残差图像分配的码流长度关系到该波段的清晰度,同时该波段作为预测波段对后面波段的预测编码会产生影响,因此设计一种合理的分配各波段码流长度的算法很有必要.本文提出了一种波段预测去除谱间冗余和码流预分配的高光谱图像压缩算法.用DPCM预测求出各波段的预测残差图像的标准差,然后根据标准差的大小对该波段进行SPIHT编码所需要分配的码流长度进行预测,最后基于均方差最小的线性预测器对图像各波段进行预测,根据事先分配的码流长度对各波段预测残差图像进行SPIHT编码.该算法在波段间采用最佳线性预测,有效地去除了高光谱图像的谱间相关性,同时,设计的分配码流长度的算法能够根据各波段信息量大小,以及与相邻波段的相关性来分配码流长度.实验表明,重建图像的平均PSNR比3D-SPIHT算法的高0.9～2.5 dB,因此该算法对高光谱图像有损压缩非常适用.

A three-dimensional (3D) wavelet coder based on 3D significance tree splitting is proposed for hyperspectral image compression. 3D discrete wavelet transform (DWT) is applied to explore the spatial and spectral correlations. Then the 3D significance tree structure is constructed in 3D wavelet domain, and wavelet coefficients are encoded via 3D significance tree splitting. This proposed algorithm does not need to use ordered lists, moreover it has less complexity and requires lower fixed memory than 3D set partitioning in hierarchical trees (SPIHT) algorithm and 3D set partitioned embedded block (SPECK) algorithm. The numerical experiments on AVIRIS images show that the proposed algorithm outperforms 3D SPECK, and has a minor loss of performance compared with 3D SPIHT. This algorithm is suitable for simple hardware implementation and can be applied to progressive transmission.