Academic Journal
Προβολή σε EDS

Jpeg stereo image lossy recompression with mutual information enhancement.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Jpeg stereo image lossy recompression with mutual information enhancement.
Συγγραφείς: Zhou, Junwei1 (AUTHOR) junweizhou@msn.com, Zhang, Benyi1 (AUTHOR) zhangbenyi@whut.edu.cn, Wu, Shengping1 (AUTHOR) wushengping@whut.edu.cn, Zhou, Lei1 (AUTHOR) leizhou@whut.edu.cn, Yang, Yanchao1 (AUTHOR) yangyc@whut.edu.cn, Xiang, Jianwen1 (AUTHOR) jwxiang@whut.edu.cn
Πηγή: Multimedia Systems. Oct2025, Vol. 31 Issue 5, p1-13. 13p.
Θεματικοί όροι: *JPEG (Image coding standard), *Image compression, *Feature extraction, *Discrete cosine transforms, *Information theory, *Stereo image processing, *Image quality analysis
Περίληψη: Despite JPEG remaining the most prevalent image compression algorithm, the majority of these algorithms primarily concentrate on uncompressed images, thereby overlooking the substantial quantity of existing compressed JPEG images. The recent strides in JPEG recompression strive to further minimize these JPEG file sizes. Unfortunately, the prevalent techniques, especially lossy recompression, frequently fail to fully utilize the correlation of coefficients, resulting in less-than-optimal compression. Moreover, they do not tap into the similarities among JPEG stereo images to boost compression. This paper proposes a novel approach, Distributed Coding for JPEG Lossy Recompression (DCLRC), to enhance the efficiency of JPEG recompression. DCLRC employs an encoding network to eliminate redundant data within the Discrete Cosine Transform (DCT) domain. This is followed by preprocessing via DCT coefficient reconstruction and alignment networks allocated for dimensionality reduction and channel alignment. More significantly, DCLRC capitalizes on the visually similar images in the DCT domain by incorporating a mutual information enhancement module. This pioneering module combines feature extraction, multi-head cross-attention, and information fusion. The experimental results show that DCLRC outperforms existing JPEG lossy recompression techniques, including ULIC, Balle2018, BPG, VCC-Intra, HEVC-Intra, JPEG2000, EILC2022, DSIN, and NDIC2022. DCLRC has achieved an average Peak Signal-to-Noise Ratio (PSNR) improvement of 2.0 dB as well as an average Multi-Scale Structural SIMilarity (MS-SSIM) improvement of 2.3 dB on the KITTI dataset at low bitrates. Such results unequivocally verify the effectiveness of DCLRC in augmenting JPEG recompression performance. Our code is available at https://github.com/wsp11/DCLRC. [ABSTRACT FROM AUTHOR]
Βάση Δεδομένων: Academic Search Index
FullText Links:
  – Type: other
Text:
  Availability: 0
CustomLinks:
  – Url: https://dx.doi.org/doi:10.1007/s00530-025-01945-9
    Name: EDS - Springer Nature Journals (s7799221)
    Category: fullText
    Text: View record at Springer
Header DbId: asx
DbLabel: Academic Search Index
An: 187458798
RelevancyScore: 1377
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1377.34240722656
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Jpeg stereo image lossy recompression with mutual information enhancement.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Junwei%22">Zhou, Junwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> junweizhou@msn.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Benyi%22">Zhang, Benyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangbenyi@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Shengping%22">Wu, Shengping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wushengping@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Lei%22">Zhou, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> leizhou@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yanchao%22">Yang, Yanchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yangyc@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiang%2C+Jianwen%22">Xiang, Jianwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jwxiang@whut.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Multimedia+Systems%22">Multimedia Systems</searchLink>. Oct2025, Vol. 31 Issue 5, p1-13. 13p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22JPEG+%28Image+coding+standard%29%22">JPEG (Image coding standard)</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+compression%22">Image compression</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br />*<searchLink fieldCode="DE" term="%22Discrete+cosine+transforms%22">Discrete cosine transforms</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+theory%22">Information theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Stereo+image+processing%22">Stereo image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Despite JPEG remaining the most prevalent image compression algorithm, the majority of these algorithms primarily concentrate on uncompressed images, thereby overlooking the substantial quantity of existing compressed JPEG images. The recent strides in JPEG recompression strive to further minimize these JPEG file sizes. Unfortunately, the prevalent techniques, especially lossy recompression, frequently fail to fully utilize the correlation of coefficients, resulting in less-than-optimal compression. Moreover, they do not tap into the similarities among JPEG stereo images to boost compression. This paper proposes a novel approach, Distributed Coding for JPEG Lossy Recompression (DCLRC), to enhance the efficiency of JPEG recompression. DCLRC employs an encoding network to eliminate redundant data within the Discrete Cosine Transform (DCT) domain. This is followed by preprocessing via DCT coefficient reconstruction and alignment networks allocated for dimensionality reduction and channel alignment. More significantly, DCLRC capitalizes on the visually similar images in the DCT domain by incorporating a mutual information enhancement module. This pioneering module combines feature extraction, multi-head cross-attention, and information fusion. The experimental results show that DCLRC outperforms existing JPEG lossy recompression techniques, including ULIC, Balle2018, BPG, VCC-Intra, HEVC-Intra, JPEG2000, EILC2022, DSIN, and NDIC2022. DCLRC has achieved an average Peak Signal-to-Noise Ratio (PSNR) improvement of 2.0 dB as well as an average Multi-Scale Structural SIMilarity (MS-SSIM) improvement of 2.3 dB on the KITTI dataset at low bitrates. Such results unequivocally verify the effectiveness of DCLRC in augmenting JPEG recompression performance. Our code is available at https://github.com/wsp11/DCLRC. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=187458798
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00530-025-01945-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1
    Subjects:
      – SubjectFull: JPEG (Image coding standard)
        Type: general
      – SubjectFull: Image compression
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Discrete cosine transforms
        Type: general
      – SubjectFull: Information theory
        Type: general
      – SubjectFull: Stereo image processing
        Type: general
      – SubjectFull: Image quality analysis
        Type: general
    Titles:
      – TitleFull: Jpeg stereo image lossy recompression with mutual information enhancement.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhou, Junwei
      – PersonEntity:
          Name:
            NameFull: Zhang, Benyi
      – PersonEntity:
          Name:
            NameFull: Wu, Shengping
      – PersonEntity:
          Name:
            NameFull: Zhou, Lei
      – PersonEntity:
          Name:
            NameFull: Yang, Yanchao
      – PersonEntity:
          Name:
            NameFull: Xiang, Jianwen
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 09424962
          Numbering:
            – Type: volume
              Value: 31
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Multimedia Systems
              Type: main
ResultId 1