Texture Quality Assessment

Texture Quality Assessment is a computational methodology that systematically evaluates the perceptual integrity and structural fidelity of surface patterns in digital images by quantifying deviations from ideal textural characteristics such as homogeneity, regularity, and detail preservation.

134

Publications

4.7K

Citations

493

Authors

195

Institutions

Publications per year

2017–2026

55

Authors

493

  1. JP
    Jan P. Allebach

    Purdue University West Lafayette, United States

    4 publications · 278 citations

    4

    h-index

  2. GJ
    Gangyi Jiang

    Ningbo University, China

    3 publications · 72 citations

    3

    h-index

  3. MC
    Mylène C. Q. Farias

    Universidade de Brasília, Brazil

    3 publications · 136 citations

    3

    h-index

  4. PG
    Pedro Garcia Freitas

    Universidade de Brasília, Brazil

    3 publications · 136 citations

    3

    h-index

  5. TK
    Tomasz Kozior

    Kielce University of Technology, Poland

    3 publications · 113 citations

    3

    h-index

1–5 of 493

Venues

  1. 7

    h-index

  2. IEEE Transactions on Image Processing

    5 publications · 311 citations

    5

    h-index

  3. Journal of Food Science

    JFS · 5 publications · 215 citations

    5

    h-index

  4. LWT

    4 publications · 188 citations

    4

    h-index

  5. Food Science & Nutrition

    3 publications · 84 citations

    3

    h-index

1–5