On board strip-based wavelet image coding for future space remote sensing missions

Parisot, Christophe; Antonini, Marc; Barlaud, Michel; Lambert-Nebout, Catherine; Latry, Christophe; Moury, Gilles

doi:10.1109/igarss.2000.859670

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…This means that an intermediate buffer must go between the LBWT and the PCT (parentchildren tree)-based codec to accumulate subband coefficients to extract PCTs. Parisot [24,26] and Lafruit [29] avoided the LBWTs interleaving by adopting an ASAP (as soon as possible) computation for subband coefficients and keeping a strict separation of coefficients from different subbands. In [29], Lafruit also gave an excellent work on optimizing memory organization for PCT production among different level traversal modes.…”

Section: Comparison To Prior Workmentioning

confidence: 99%

“…No reloading of samples is needed. Our work essentially generalizes Parisot's scan-based approach [24,26], Lafruit's block-based approaches [21,22,29] and Xu's memory-constrained wavelet transform (MCWT) [25] from the DWT to the GHT. By analyzing the dataflow in different decomposition levels of the GHT, we also provide a precise closed-form temporal relationship between the input samples and the subband coefficients, by which the correctness of the proposed approach is strictly proved.…”

Section: Article In Pressmentioning

confidence: 99%

“…There is a vast amount of literature on low-memory implementation of DWT, which falls loosely into three categories by the sample feeding modes, namely, the linebased (or scan-based) approaches [8,[23][24][25][26][27][28], the blockbased approaches [10,21,22,[29][30][31] and the novel scan order approaches [32,33]. Since data acquisition devices usually utilize sequential sensors, the line-based feeding is the most common one and has been implemented in hardware [27,28,34].…”

Section: Comparison To Prior Workmentioning

confidence: 99%

See 2 more Smart Citations

Low memory implementation of generic hierarchical transforms for parent–children tree (PCT) production and its application in image compression

Zhang

Cheng

Lü

2009

Signal Processing: Image Communication

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Section: Comparison To Prior Workmentioning

confidence: 99%

Section: Article In Pressmentioning

confidence: 99%

Section: Comparison To Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Low memory implementation of generic hierarchical transforms for parent–children tree (PCT) production and its application in image compression

Zhang

Cheng

Lü

2009

Signal Processing: Image Communication

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“…With this scheme, lossless compression can be achieved if the LWT is an integer wavelet. [13]. It will be used on the PLEIADES-HR mission which is a very high resolution (0.7 m) earth observation system.…”

Section: Transform With Bit Plane Encodermentioning

confidence: 99%

CCSDS Data Compression Recommendation: Development and Status

2002

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The Consultative Committee for Space Data Systems (CCSDS) has been engaging in recommending data compression standards for space applications. The first effort focused on a lossless scheme that was adopted in 1997. Since then, space missions benefiting from this recommendation range from deep space probes to near Earth observatories. The cost savings result not only from reduced onboard storage and reduced bandwidth, but also in ground archive of mission data. In many instances, this recommendation also enables more science data to be collected for added scientific value. Since 1998, the compression sub-panel of CCSDS has been investigating lossy image compression schemes and is currently working towards a common solution for a single recommendation. The recommendation will fulfill the requirements for remote sensing conducted on space platforms.

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“…[5] have shown good performance in lossless compression, but they are not appropriate for space applications due to its high complexity [6] . The lossless compression recommendation adopted by the CCSDS (Consultative Committee for Space Data Systems) has good compression performance only for low-entropy images [3] .…”

Section: Introductionmentioning

confidence: 99%

A lossless compression algorithm of remote sensing image for space applications

Sui

Yang

Liu

et al. 2008

J. Electron.(China)

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A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Compression Ratio (CR) of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.

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On board strip-based wavelet image coding for future space remote sensing missions

Cited by 15 publications

References 9 publications

Low memory implementation of generic hierarchical transforms for parent–children tree (PCT) production and its application in image compression

Low memory implementation of generic hierarchical transforms for parent–children tree (PCT) production and its application in image compression

CCSDS Data Compression Recommendation: Development and Status

A lossless compression algorithm of remote sensing image for space applications

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