Distributed Source Coding

Distributed Source Coding is a method in source coding theory that enables the efficient compression of correlated information sources operating without communication between encoders, relying on joint decoding to exploit statistical dependencies, thereby differing from conventional coding schemes that require coordinated encoding.

300

Publications

12.7K

Citations

479

Authors

221

Institutions

Publications per year

2017–2026

23

Authors

479

  1. KR
    Kannan Ramchandran

    University of California, Berkeley, United States

    24 publications · 2.8K citations

    17

    h-index

  2. SS
    S. Sandeep Pradhan

    University of Michigan, United States

    20 publications · 2.7K citations

    15

    h-index

  3. NC
    Ngai‐Man Cheung

    Singapore University of Technology and Design, Singapore

    10 publications · 263 citations

    10

    h-index

  4. MT
    Marco Tagliasacchi

    Politecnico di Milano, Italy

    10 publications · 227 citations

    10

    h-index

  5. AO
    Antonio Ortega

    University of Southern California, United States

    10 publications · 263 citations

    10

    h-index

1–5 of 479

Venues

  1. IEEE Transactions on Information Theory

    35 publications · 3.2K citations

    28

    h-index

  2. IEEE Transactions on Communications

    12 publications · 307 citations

    10

    h-index

  3. IEEE Transactions on Signal Processing

    7 publications · 171 citations

    7

    h-index

  4. Signal Processing Image Communication

    6 publications · 149 citations

    6

    h-index

  5. IEEE Communications Letters

    6 publications · 213 citations

    6

    h-index

1–5