Binary Source Rate-distortion Theory

Binary Source Rate-Distortion Theory is a sub-framework within rate-distortion theory that specifically characterizes the fundamental limits of lossy compression for binary random variables by determining the minimum achievable rate required to reconstruct the source within a specified distortion level under a given distortion measure.

42

Publications

3.4K

Citations

76

Authors

55

Institutions

Publications per year

2017–2026

3

Authors

76

  1. RU
    Rüdiger Urbanke

    École Polytechnique Fédérale de Lausanne, Switzerland

    4 publications · 363 citations

    4

    h-index

  2. EM
    Emin Martinian

    Massachusetts Institute of Technology, United States

    4 publications · 165 citations

    4

    h-index

  3. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    4 publications · 165 citations

    4

    h-index

  4. TB
    Thomas Berger

    Cornell University, United States

    3 publications · 403 citations

    3

    h-index

  5. SB
    Satish Babu Korada

    École Polytechnique Fédérale de Lausanne, Switzerland

    3 publications · 347 citations

    3

    h-index

1–5 of 76

Venues

  1. IEEE Transactions on Information Theory

    19 publications · 2.9K citations

    17

    h-index

  2. ArXiv.org

    Repository · 4 publications · 60 citations

    4

    h-index

  3. IEEE Transactions on Communications

    2 publications · 32 citations

    2

    h-index

  4. IEEE Communications Letters

    1 publications · 16 citations

    1

    h-index

  5. 2022 IEEE International Symposium on Information Theory (ISIT)

    Conference · 1 publications · 32 citations

    1

    h-index

1–5