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Lossless to lossy compression for hyperspectral imagery based on wavelet and integerKLT transforms with 3D binary EZW

机译:基于小波和integerKLT变换的3D二进制EZW对高光谱图像进行无损压缩

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In this paper, a lossless to lossy transform based image compression of hyperspectral images based on Integer Karhunen-Loeve Transform (IKLT) and Integer Discrete Wavelet Transform (IDWT) is proposed. Integer transforms are used to accomplish reversibility. The IKLT is used as a spectral decorrelator and the 2D-IDWT is used as a spatial decorrelator. The three-dimensional Binary Embedded Zerotree Wavelet (3D-BEZW) algorithm efficiently encodes hyperspectral volumetric image by implementing progressive bitplane coding. The signs and magnitudes of transform coefficients are encoded separately. Lossy and lossless compressions of signs are implemented by conventional EZW algorithm and arithmetic coding respectively. The efficient 3D-BEZW algorithm is applied to code magnitudes. Further compression can be achieved using arithmetic coding. The lossless and lossy compression performance is compared with other state of the art predictive and transform based image compression methods on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. Results show that the 3D-BEZW performance is comparable to predictive algorithms. However, its computational cost is comparable to transform- based algorithms.
机译:本文提出了基于整数Karhunen-Loeve变换(IKLT)和整数离散小波变换(IDWT)的高光谱图像无损到有损变换的图像压缩方法。整数转换用于实现可逆性。 IKLT用作频谱去相关器,而2D-IDWT用作空间去相关器。三维二进制嵌入式零树小波(3D-BEZW)算法通过实现渐进式位平面编码有效地对高光谱体积图像进行编码。变换系数的符号和大小分别进行编码。符号的有损和无损压缩分别通过常规的EZW算法和算术编码实现。高效的3D-BEZW算法应用于代码幅度。使用算术编码可以实现进一步的压缩。在机载可见/红外成像光谱仪(AVIRIS)图像上,将无损和有损压缩性能与其他现有的基于预测和变换的图像压缩方法进行了比较。结果表明3D-BEZW性能可与预测算法相媲美。但是,其计算成本可与基于变换的算法相比。

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