Titlebook: Hyperspectral Data Compression; Giovanni Motta,Francesco Rizzo,James A. Storer Book 2006 Springer-Verlag US 2006 3D.3D wavelet-based image

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Lossless Predictive Compression of Hyperspectral Images, of their implementation their outline is essentially the same. Each algorithm tries to approximate the ideal case of stationary data for which optimal predictors can be computed. The approximations can be viewed as attempting to partition the data space into sets within which the stationarity assum

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Locally Optimal Partitioned Vector Quantization of Hyperspectral Data,e encoding of source vectors drawn from a high dimensional source on .. LPVQ breaks down the input space into subspaces and, for each subspace, designs a minimal-distortion vector quantizer. The partition is adaptively determined while building the quantizers in order to minimize the total distortio

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Three-Dimensional Wavelet-Based Compression of Hyperspectral Images,sform automatically exploits inter-band dependence. Two versions of the algorithm were implemented. The integer filter implementation enables lossy-to-lossless compression, and the floating point filter implementation provides better performance for lossy representation. Wavelet packet structure and

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Spectral Ringing Artifacts in Hyperspectral Image Data Compression,ely-varying mean values in spatial planes of spatially low-pass subbands. Failing to account for this phenomenon can have detrimental effects on image compression, including reduced effectiveness in compressing spatially low-pass subband data, and biases in some reconstructed spectral bands..These e

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Lossless Predictive Compression of Hyperspectral Images,ption can be applied with some level of plausibility. The extent to which these algorithms function or fail depends upon the validity of their assumption. There is clearly much more work to be done before we can claim that the problem of predictive lossless compression has been solved.

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Lossless Compression of Ultraspectral Sounder Data,PIHT, 2D JPEG2000, 2D CALIC, 2D JPEG-LS etc.) can be significantly improved when combining the BAR preprocessing scheme. We also report the promising compression results for the ultraspectral sounder data using various approaches such as lossless PCA and Predictive Partitioned Vector Quantization (PPVQ).