Entropy-constrained Quantization

Entropy-Constrained Quantization is a rate-distortion optimization framework in lossy data compression that characterizes a quantizer designed to minimize distortion subject to a constraint on the output entropy, thereby explicitly balancing fidelity and compression efficiency.

117

Publications

5.9K

Citations

153

Authors

93

Institutions

Publications per year

2017–2026

5

Authors

153

  1. MW
    Michael W. Marcellin

    University of Arizona, United States

    12 publications · 472 citations

    11

    h-index

  2. PA
    Philip A. Chou

    Microsoft (United States), United States

    9 publications · 759 citations

    9

    h-index

  3. TL
    Tamás Linder

    Queen's University, Canada

    8 publications · 318 citations

    8

    h-index

  4. NF
    N. Farvardin

    University of Maryland, College Park, United States

    6 publications · 513 citations

    6

    h-index

  5. RH
    Richard Heusdens

    Delft University of Technology, Netherlands

    5 publications · 135 citations

    5

    h-index

1–5 of 153

Venues

  1. IEEE Transactions on Information Theory

    33 publications · 2.7K citations

    25

    h-index

  2. IEEE Transactions on Image Processing

    12 publications · 470 citations

    11

    h-index

  3. 4

    h-index

  4. 4

    h-index

  5. IEEE Transactions on Signal Processing

    4 publications · 156 citations

    4

    h-index

1–5