Distributed Image Compression

Distributed Image Compression is a computational framework within image coding that enables the compression of visual data captured by multiple correlated sources without requiring them to communicate with one another during encoding, relying instead on joint decoding to exploit inter-source redundancy.

53

Publications

1.5K

Citations

103

Authors

53

Institutions

Publications per year

2017–2026

8

Variants

Distributed Source Coding For Images

Authors

103

  1. PL
    Pier Luigi Dragotti

    Imperial College London, United Kingdom

    5 publications · 95 citations

    5

    h-index

  2. BG
    Bernd Girod

    Stanford University, United States

    5 publications · 179 citations

    5

    h-index

  3. KR
    Kannan Ramchandran

    University of California, Berkeley, United States

    5 publications · 154 citations

    5

    h-index

  4. NG
    N. Gehrig

    Imperial College London, United Kingdom

    5 publications · 95 citations

    5

    h-index

  5. DV
    David Varodayan

    Stanford University, United States

    3 publications · 80 citations

    3

    h-index

1–5 of 103

Venues

  1. IEEE Transactions on Image Processing

    5 publications · 131 citations

    5

    h-index

  2. 3

    h-index

  3. Wireless Communications and Mobile Computing

    WCMC · 1 publications · 31 citations

    1

    h-index

  4. Computer Communications

    1 publications · 82 citations

    1

    h-index

  5. EURASIP Journal on Image and Video Processing

    1 publications · 27 citations

    1

    h-index

1–5