• DocumentCode
    61058
  • Title

    A Fully Embedded Two-Stage Coder for Hyperspectral Near-Lossless Compression

  • Author

    Beerten, Jente ; Blanes, Ian ; Serra-Sagrista, Joan

  • Author_Institution
    Dept. of Inf. & Commun. Eng., Univ. Autonoma de Barcelona, Barcelona, Spain
  • Volume
    12
  • Issue
    8
  • fYear
    2015
  • fDate
    Aug. 2015
  • Firstpage
    1775
  • Lastpage
    1779
  • Abstract
    This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the l2-norm initially and in the $l_infty$-norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are the observation of the convergence of the entropy of the residuals in the original domain and in the spectral-spatial transformed domain and an embedded near-lossless layer. These contributions enable a progressive transmission while optimizing both signal-to-noise ratio (SNR) and peak absolute error (PAE) performance. The embeddedness is accomplished by bit-plane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multicomponent JPEG2000 for the l-norm and pairing its performance for the l2-norm, and also outperforming M-CALIC in the near-lossless case, i.e., for PAE ≥ 5.
  • Keywords
    arithmetic codes; convergence; data compression; distortion; geophysical image processing; hyperspectral imaging; image coding; minimisation; M-CALIC; PAE performance optimization; SNR optimization; bitplane encoding plus arithmetic encoding; convergence; distortion minimization; embedded two-stage coder; embeddedness; hyperspectral imaging; hyperspectral near lossless compression; l∞-norm subsequently; l2-norm initially; multicomponent JPEG2000; peak absolute error; signal-to-noise ratio; spectral-spatial transformed domain; Bit rate; Encoding; Entropy; Hyperspectral imaging; Image coding; Proposals; Transforms; Hyperspectral data; lossy compression; near-lossless compression; source coding;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1545-598X
  • Type

    jour

  • DOI
    10.1109/LGRS.2015.2425548
  • Filename
    7105877