Near-Lossless Compression of 3-D Optical Data (Articolo in rivista)

Type
Label
  • Near-Lossless Compression of 3-D Optical Data (Articolo in rivista) (literal)
Anno
  • 2001-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/36.964993 (literal)
Alternative label
  • Bruno Aiazzi; Luciano Alparone; Stefano Baronti (2001)
    Near-Lossless Compression of 3-D Optical Data
    in IEEE transactions on geoscience and remote sensing; IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bruno Aiazzi; Luciano Alparone; Stefano Baronti (literal)
Pagina inizio
  • 2547 (literal)
Pagina fine
  • 2557 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=964993 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 39 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 11 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 11 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • \"Nello Carrara\" Research Institute on Electromagnetic Waves (IROE), National Research Council (CNR), I-50127 Florence, Italy Department of Electronics and Telecommunications, University of Florence, I-50139 Florence, Italy \"Nello Carrara\" Research Institute on Electromagnetic Waves (IROE), National Research Council (CNR), I-50127 Florence, Italy (literal)
Titolo
  • Near-Lossless Compression of 3-D Optical Data (literal)
Abstract
  • In this work, near-lossless compression yielding strictly bounded reconstruction error is proposed for high-quality compression of remote sensing images. A classified causal DPCM scheme is presented for optical data, either multi/hyperspectral three-dimensional (3-D) or panchromatic two-dimensional (2-D) observations. It is based on a classified linear-regression prediction, followed by context-based arithmetic coding of the outcome prediction errors and provides excellent performances, both for reversible and for irreversible (near-lossless) compression. Coding times are affordable thanks to fast convergence of training. Decoding is always real time. If the reconstruction errors fall within the boundaries of the noise distributions, the decoded images will be virtually lossless even though encoding was not strictly reversible. (literal)
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