IQM-Vis: A user-centric python toolbox for visualising and evaluating image quality metrics
Image Quality Metrics (IQMs) assess differences between images where human judgements are too expensive or infeasible due to the number of evaluations needed. Although a multitude of IQMs have been proposed in the literature, none are considered flawless. Therefore, it is crucial to understand their limitations by evaluating their suitability for different application domains. This type of evaluation is both qualitative and quantitative, lending itself best to interactive graphical tools. To this end, we created IQM-Vis, the first open source toolbox dedicated to analysing IQMs, visualising image distortions and conducting human image perception experiments, all through a simple Python interface.
Authors
- Nathan F. Lepora (ORCID: https://orcid.org/0000-0001-5327-1523)
- Raúl Santos‐Rodríguez (ORCID: https://orcid.org/0000-0001-9576-3905)
- Ricardo Kleinlein (ORCID: https://orcid.org/0000-0002-7313-7601)
- Jorge Vila-Tomás (ORCID: https://orcid.org/0000-0002-8246-0195)
- Pablo Hernández-Cámara (ORCID: https://orcid.org/0000-0003-2957-844X)
- Valero Laparra (ORCID: https://orcid.org/0000-0001-7531-9890)
- Alexa Hepburn (ORCID: https://orcid.org/0000-0001-6564-6310)
- Matt Clifford (ORCID: https://orcid.org/0009-0008-5745-0092)
- Paula Daudén
Institutions
- Universitat de València (ES)
- University of Bristol (GB)
- Center for Pain and the Brain (US)
Publication Details
- Journal
- SoftwareX
- Published
- 2025-06-21
- DOI
- https://doi.org/10.1016/j.softx.2025.102225
- Primary Topic
- Image and Video Quality Assessment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation