Statistical Image Features

Statistical Image Features is a type of image feature representation that characterizes visual patterns through quantitative measures derived from statistical distributions of pixel intensities or transformations thereof, distinguishing it from structural or semantic feature types by its reliance on aggregate numerical properties rather than spatial configurations or high-level meanings.

1.2K

Publications

63.9K

Citations

3.6K

Authors

1.3K

Institutions

Publications per year

2017–2026

409

Authors

3.6K

  1. AC
    Alan C. Bovik

    The University of Texas at Austin, United States

    13 publications · 10K citations

    13

    h-index

  2. CB
    Christoph Busch

    Darmstadt University of Applied Sciences, Germany

    10 publications · 498 citations

    10

    h-index

  3. CR
    Christoph Redies

    Friedrich Schiller University Jena, Germany

    9 publications · 420 citations

    9

    h-index

  4. AR
    Amjad Rehman

    Prince Sultan University, Saudi Arabia

    8 publications · 212 citations

    8

    h-index

  5. YQ
    Yun Q. Shi

    New Jersey Institute of Technology, United States

    7 publications · 447 citations

    7

    h-index

1–5 of 3.6K

Venues

  1. IEEE Transactions on Image Processing

    29 publications · 6.2K citations

    23

    h-index

  2. 21

    h-index

  3. IEEE Access

    20 publications · 608 citations

    15

    h-index

  4. 13

    h-index

  5. Procedia Computer Science

    14 publications · 652 citations

    13

    h-index

1–5