View in EDS

Distributed lossy compression for hyperspectral images based on multilevel coset codes.

Saved in:
Bibliographic Details
Title: Distributed lossy compression for hyperspectral images based on multilevel coset codes.
Authors: Xu, Ke1, Liu, Bin2, Nian, Yongjian3, He, Mi3, Wan, Jianwei1
Source: International Journal of Wavelets, Multiresolution & Information Processing. Mar2017, Vol. 15 Issue 2, p-1. 20p.
Subjects: Hyperspectral imaging systems, Multilevel codes, Regression analysis, Mathematical models, Multilinear algebra
Abstract: This paper focuses on the problem of lossy compression for hyperspectral images and presents an efficient compression algorithm based on distributed source coding. The proposed algorithm employs a block-based quantizer followed by distributed lossless coding, which is implemented through the use of multilevel coset codes. First, a bitrate allocation algorithm is proposed to assign the rational bitrate for each block. Subsequently, the multilinear regression model is employed to construct the side information of each block, and the optimal quantization step size of each block is obtained under the assigned bitrate while minimizing the distortion. Finally, the quantized version of each block is encoded by distributed lossless compression. Experimental results show that the compression performance of the proposed algorithm is competitive with that of state-of-the-art transform-based compression algorithms. Moreover, the proposed algorithm provides both low encoder complexity and error resilience, making it suitable for onboard compression. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Wavelets, Multiresolution & Information Processing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 121162902
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Distributed lossy compression for hyperspectral images based on multilevel coset codes.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Ke%22">Xu, Ke</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Bin%22">Liu, Bin</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Nian%2C+Yongjian%22">Nian, Yongjian</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22He%2C+Mi%22">He, Mi</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wan%2C+Jianwei%22">Wan, Jianwei</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Wavelets%2C+Multiresolution+%26+Information+Processing%22">International Journal of Wavelets, Multiresolution & Information Processing</searchLink>. Mar2017, Vol. 15 Issue 2, p-1. 20p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Hyperspectral+imaging+systems%22">Hyperspectral imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Multilevel+codes%22">Multilevel codes</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Multilinear+algebra%22">Multilinear algebra</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper focuses on the problem of lossy compression for hyperspectral images and presents an efficient compression algorithm based on distributed source coding. The proposed algorithm employs a block-based quantizer followed by distributed lossless coding, which is implemented through the use of multilevel coset codes. First, a bitrate allocation algorithm is proposed to assign the rational bitrate for each block. Subsequently, the multilinear regression model is employed to construct the side information of each block, and the optimal quantization step size of each block is obtained under the assigned bitrate while minimizing the distortion. Finally, the quantized version of each block is encoded by distributed lossless compression. Experimental results show that the compression performance of the proposed algorithm is competitive with that of state-of-the-art transform-based compression algorithms. Moreover, the proposed algorithm provides both low encoder complexity and error resilience, making it suitable for onboard compression. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Wavelets, Multiresolution & Information Processing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=121162902
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1142/S0219691317500126
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: -1
    Subjects:
      – SubjectFull: Hyperspectral imaging systems
        Type: general
      – SubjectFull: Multilevel codes
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Multilinear algebra
        Type: general
    Titles:
      – TitleFull: Distributed lossy compression for hyperspectral images based on multilevel coset codes.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Xu, Ke
      – PersonEntity:
          Name:
            NameFull: Liu, Bin
      – PersonEntity:
          Name:
            NameFull: Nian, Yongjian
      – PersonEntity:
          Name:
            NameFull: He, Mi
      – PersonEntity:
          Name:
            NameFull: Wan, Jianwei
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2017
              Type: published
              Y: 2017
          Identifiers:
            – Type: issn-print
              Value: 02196913
          Numbering:
            – Type: volume
              Value: 15
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: International Journal of Wavelets, Multiresolution & Information Processing
              Type: main
ResultId 1