TIQA-PSI: Toolbox for perceptual Image Quality Assessment of Pan-Sharpened Images

被引:0
|
作者
Stepien, Igor [1 ]
Oszust, Mariusz [2 ]
机构
[1] Rzeszow Univ Technol, Doctoral Sch Engn & Tech Sci, Al Powstancow Warszawy 12, PL-35959 Rzeszow, Poland
[2] Rzeszow Univ Technol, Dept Comp & Control Engn, Wincentego Pola 2, PL-35959 Rzeszow, Poland
关键词
Image Quality Assessment; Hyperspectral Imaging; Pan-sharpening; Toolbox; Perceptual image quality; MATLAB; FUSION;
D O I
10.1016/j.softx.2023.101494
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A Pan-Sharpening (PS) technique integrates spatial details of a high-resolution panchromatic (PAN) image with spectral information of a low-resolution multi-spectral (MS) image, creating a single high-resolution color image. Since different PS methods produce images of a different quality, they should be compared using Image Quality Assessment (IQA) approaches that mimics human visual perception. In this paper, a MATLAB-based Toolbox for perceptual Image Quality Assessment of PanSharpened Images (TIQA-PSI) is presented. TIQA-PSI aims to stimulate the development and facilitate the evaluation of new PS or PS-IQA approaches as it contains a large number of state-of-the-art IQA methods and images generated by representative PS techniques with corresponding subjective scores. They can be used to assess the output of any new PS algorithm or to serve as baselines for the comparison with new PS-IQA methods. Furthermore, apart from popular PS-IQA methods, the toolbox offers an opportunity to employ perceptual IQA approaches that can be effectively trained on scores obtained in tests with human subjects for superior image evaluation. The code of TIQA-PSI is available on GitHub.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页数:7
相关论文
共 50 条
  • [1] Perceptual Quality Assessment of Pan-Sharpened Images
    Agudelo-Medina, Oscar A.
    Dario Benitez-Restrepo, Hernan
    Vivone, Gemine
    Bovik, Alan
    REMOTE SENSING, 2019, 11 (07)
  • [2] Image Quality Assessment of Pleiades-1A Triplet Bundle and Pan-sharpened Images
    Jacobsen, Karsten
    Topan, Huseyin
    Cam, Ali
    Ozendi, Mustafa
    Oruc, Murat
    PHOTOGRAMMETRIE FERNERKUNDUNG GEOINFORMATION, 2016, (03): : 141 - 152
  • [3] An object approach to the assessment of the spatial quality of pan-sharpened remote sensing images
    Rodriguez-Esparragon, Dionisio
    Marcello-Ruiz, Javier
    Eugenio-Gonzalez, Francisco
    Garcia-Pedrero, Angel
    Gonzalo-Martin, Consuelo
    2015 4TH INTERNATIONAL WORK CONFERENCE ON BIOINSPIRED INTELLIGENCE (IWOBI), 2015, : 49 - 53
  • [4] Expanded Q4 Quality Assessment for Pan-Sharpened MultiSpectral Image
    Wang Zhongwu
    Zhao Zhongming
    GEOINFORMATICS 2008 AND JOINT CONFERENCE ON GIS AND BUILT ENVIRONMENT: ADVANCED SPATIAL DATA MODELS AND ANALYSES, PARTS 1 AND 2, 2009, 7146
  • [5] Pan-Sharpened Image Optical Encryption
    Mehra, Isha
    Nishchal, Naveen K.
    ADVANCES IN OPTICAL SCIENCE AND ENGINEERING, 2015, 166 : 441 - 444
  • [6] Three-branch neural network for No-Reference Quality assessment of Pan-Sharpened Images
    Stępień, Igor
    Oszust, Mariusz
    Engineering Applications of Artificial Intelligence, 2025, 139
  • [7] A Global Quality Measurement of Pan-Sharpened Multispectral Imagery
    Alparone, Luciano
    Baronti, Stefano
    Garzelli, Andrea
    Nencini, Filippo
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004, 1 (04) : 313 - 317
  • [8] Assessment of pan-sharpened very high-resolution WorldView-2 images
    Ghosh, Aniruddha
    Joshi, P. K.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (23) : 8336 - 8359
  • [9] Combination of multifeature extraction and learning-based pooling for quality assessment of pan-sharpened remote sensing image
    Zhang, Feiyan
    DuanMu, Chunjiang
    JOURNAL OF APPLIED REMOTE SENSING, 2020, 14 (02):
  • [10] The Potential of Pan-Sharpened EnMAP Data for the Assessment of Wheat LAI
    Siegmann, Bastian
    Jarmer, Thomas
    Beyer, Florian
    Ehlers, Manfred
    REMOTE SENSING, 2015, 7 (10) : 12737 - 12762