Multispectral KLT-wavelet data compression for Landsat thematic mapper images

Epstein, Benjamin R.; Hingorani, R.; Shapiro, J.M.; Czigler, M.

doi:10.1109/dcc.1992.227461

Cited by 48 publications

(15 citation statements)

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“…The estimates are that over a terabyte (10 12 bytes) of data will be generated every day by the EOS satellites, most of it multispectral image data. Largely due to this fact, a lot of attention has recently been focused on compression of multispectral images [1], [2], [3], [4], [5]. However, most of the compression methods that exploit spectral as well as spatial redundancy have been lossy compression algorithms, and for archival storage and for certain applications it is important to use lossless compression in order to preserve all of the data that is collected.…”

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confidence: 99%

Band ordering in lossless compression of multispectral images

Tate

1997

IEEE Trans. Comput.

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(c) 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Abstract:In this paper, we consider a model of lossless image compression in which each band of a multispectral image is coded using a prediction function involving values from a previously coded band of the compression, and examine how the ordering of the bands affects the achievable compression.We present an efficient algorithm for computing the optimal band ordering for a multispectral image. This algorithm has time complexity O(n 2 ) for an n-band image, while the naive algorithm takes time (n!). A slight variant of the optimal ordering problem that is motivated by some practical concerns is shown to be NP-hard, and hence, computationally infeasible, in all cases except for the most trivial possibility.In addition, we report on our experimental findings using the algorithms designed in this paper applied to real multispectral satellite data. The results show that the techniques described here hold great promise for application to real-world compression needs.Index Terms-Compression, lossless compression, image compression, multispectral images, satellite data, NPcompleteness. Article:1 INTRODUCTION MULTISPECTRAL satellite images require enormous amounts of space, and with NASA's project EOS (the Earth Observing System), data will be generated at an unprecedented rate. The estimates are that over a terabyte (10 12 bytes) of data will be generated every day by the EOS satellites, most of it multispectral image data. Largely due to this fact, a lot of attention has recently been focused on compression of multispectral images [5]. However, most of the compression methods that exploit spectral as well as spatial redundancy have been lossy compression algorithms, and for archival storage and for certain applications it is important to use lossless compression in order to preserve all of the data that is collected. One notable exception to this is the work of Roger and Cavenor [4] who extensively study various prediction and coding methods used for lossless compression of AVIRIS data. Table 1 lists some current, widely used multispectral sources, with their acronyms, full names, and basic properties-it is data from these sensors that we used in this study.In this paper, we study lossless compression of multispectral images. Spectral redundancy is extracted by coding each band of the multispectral image by making use of a second -prediction band.‖ In much the same way that standard single-image lossless compression is separated into the two separate components of prediction and coding, we divide the lossless compression of multispectral images into three components: band ordering, prediction, and coding. The new stage, band ordering, refers to selecting a permutation...

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Band ordering in lossless compression of multispectral images

Tate

1997

IEEE Trans. Comput.

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“…For example, Saghri and Tescher [23] and Lee [17] used KLT followed by a 2-D DCT, and Epstein [9] and Amato et al [2] used KLT and DWT. Some others used DCT followed by an interband predictive coding [1].…”

Section: Spatial and Spectral Correlationmentioning

confidence: 99%

Encoding of multispectral and hyperspectral image data using wavelet transform and gain shape vector quantization

Kumar

Makkapati

2005

Image and Vision Computing

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“…The block size is adapted using a quadtree and a bit allocation for each block. The DCT transform can also be replaced by a wavelet transform (WT) (Epstein et al 1992;Tretter & Bouman 1995).…”

Section: Introductionmentioning

confidence: 99%

Entropy and astronomical data analysis: Perspectives from multiresolution analysis

Starck

Murtagh

Querre

et al. 2001

A&A

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Abstract. The Maximum Entropy Method is well-known and widely used in image analysis in astronomy. In its standard form it presents certain drawbacks, such an underestimation of the photometry. Various refinements of MEM have been proposed over the years. We review in this paper the main entropy functionals which have been proposed and discuss each of them. We define, from a conceptual point of view, what a good definition of entropy should be in the framework of astronomical data processing. We show how a definition of multiscale entropy fulfills these requirements. We show how multiscale entropy can be used for many applications, such as signal or image filtering, multi-channel data filtering, deconvolution, background fluctuation analysis, and astronomical image content analysis.

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Multispectral KLT-wavelet data compression for Landsat thematic mapper images

Cited by 48 publications

References 12 publications

Band ordering in lossless compression of multispectral images

Band ordering in lossless compression of multispectral images

Encoding of multispectral and hyperspectral image data using wavelet transform and gain shape vector quantization

Entropy and astronomical data analysis: Perspectives from multiresolution analysis

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