Reversible integer KLT for progressive-to-lossless compression of multiple component images

Hao, Pengwei; Shi, Qin

doi:10.1109/icip.2003.1247041

Cited by 57 publications

(38 citation statements)

References 7 publications

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“…On a related note, regardless of the fact that the KLT is the optimal transform in the coding gain sense, its practical application has been somewhat limited because of its complexity and of the fact that the transform is signal-adaptive; however, a few recent works have rediscovered the KLT and attempted to exploit its superior decorrelation capabilities. In [6], an integer implementation of the KLT followed by JPEG 2000 applied to each transformed component is presented. In [7] a hybrid 3D wavelet transform is used, employing JPEG 2000 a spatial decorrelator, with full 3D post-compression rate-distortion optimization; this technique significantly outperforms stateof-the-art schemes.…”

Section: Introductionmentioning

confidence: 99%

A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Penna

Tillo

Magli

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Abstract-Transform-based lossy compression has a huge potential for hyperspectral data reduction. In this paper we propose a lossy compression scheme for hyperspectral data based on a new low-complexity version of the Karhunen-Loève transform, in which complexity and performance can be balanced in a scalable way, allowing one to choose the best trade off that better matches a specific application. Moreover, we integrate this transform in the framework of Part 2 of the JPEG 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard.

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Section: Introductionmentioning

confidence: 99%

A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Penna

Tillo

Magli

et al. 2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…In [14], an integer implementation of the KTL followed by JPEG 2000 applied to each transformed component is presented. In [15], a hybrid 3-D wavelet transform is used, employing JPEG 2000 as spatial decorrelator.…”

mentioning

confidence: 99%

Transform Coding Techniques for Lossy Hyperspectral Data Compression

Penna

Tillo

Magli

et al. 2007

IEEE Trans. Geosci. Remote Sensing

272

153

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Abstract-Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loève transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications.

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“…However, PCA is a data-dependent transform, entailing the need to compute it for each individual image before its application. As a consequence, its computational complexity is substantial due to the covariance matrix calculation, the extraction of eigenvectors, the matrix factorization and the integer mapping (integer implementation is needed for lossless coding [6,7]). Hence, in scenarios where the data has a significant size in the spectral dimension, such as IASI images -with more than 8000 components-, PCA and its integer implementation are not feasible.…”

Section: Introductionmentioning

confidence: 99%

Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data

Amrani

Serra-Sagristà

Hernández-Cabronero

et al. 2016

2016 Data Compression Conference (DCC)

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Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelettransformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain. For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

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Reversible integer KLT for progressive-to-lossless compression of multiple component images

Cited by 57 publications

References 7 publications

A New Low Complexity KLT for Lossy Hyperspectral Data Compression

A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Transform Coding Techniques for Lossy Hyperspectral Data Compression

Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data

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