Slepian-wolf Coding

Slepian-Wolf Coding is a theoretical framework in lossless data compression that enables distributed source coding of correlated information sources without inter-source communication, achieving joint entropy rates as if the sources were encoded together.

290

Publications

19.2K

Citations

435

Authors

219

Institutions

Publications per year

2017–2026

16

Authors

435

  1. ZX
    Zixiang Xiong

    Texas A&M University, United States

    26 publications · 2.5K citations

    20

    h-index

  2. AL
    A.D. Liveris

    Texas A&M University, United States

    15 publications · 2.1K citations

    13

    h-index

  3. SC
    Samuel Cheng

    University of Oklahoma, United States

    13 publications · 445 citations

    11

    h-index

  4. VS
    Vladimir Stanković

    University of Strathclyde, United Kingdom

    14 publications · 564 citations

    11

    h-index

  5. DH
    Dake He

    IBM Research - Thomas J. Watson Research Center, United States

    12 publications · 268 citations

    11

    h-index

1–5 of 435

Venues

  1. IEEE Transactions on Information Theory

    54 publications · 6.7K citations

    31

    h-index

  2. IEEE Transactions on Communications

    15 publications · 430 citations

    12

    h-index

  3. IEEE Communications Letters

    10 publications · 1.4K citations

    10

    h-index

  4. arXiv (Cornell University)

    Repository · 5 publications · 204 citations

    5

    h-index

  5. IEEE Transactions on Image Processing

    4 publications · 191 citations

    4

    h-index

1–5