Sparse Representation Compression

Sparse Representation Compression is a signal compression paradigm within biomedical signal processing that exploits the principle of sparsity by representing signals as linear combinations of a few atoms from an overcomplete dictionary, thereby enabling efficient data compression through the retention of only significant non-zero coefficients.

627

Publications

30.3K

Citations

1.9K

Authors

675

Institutions

Publications per year

2017–2026

185

Authors

1.9K

  1. MB
    Michael B. Wakin

    Colorado School of Mines, United States

    7 publications · 1K citations

    7

    h-index

  2. RG
    Richard G. Baraniuk

    Rice University, United States

    7 publications · 885 citations

    7

    h-index

  3. JN
    Jelani Nelson

    Harvard University Press, United States

    6 publications · 428 citations

    6

    h-index

  4. BH
    Babak Hassibi

    California Institute of Technology, United States

    4 publications · 450 citations

    4

    h-index

  5. PF
    Pascal Frossard

    École Polytechnique Fédérale de Lausanne, Switzerland

    4 publications · 82 citations

    4

    h-index

1–5 of 1.9K

Venues

  1. IEEE Transactions on Image Processing

    17 publications · 775 citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 20 publications · 689 citations

    14

    h-index

  3. IEEE Transactions on Signal Processing

    12 publications · 736 citations

    12

    h-index

  4. Neurocomputing

    14 publications · 608 citations

    11

    h-index

  5. IEEE Transactions on Information Theory

    11 publications · 2.5K citations

    11

    h-index

1–5