A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as a Proxy
Gallés, Pau (Satellogic Inc)
Takáts, Katalin (Satellogic Inc)
Hernández Cabronero, Miguel (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Berga, David (EURECAT - Multimedia Technologies Unit)
Pega, Luciano (Satellogic Inc)
Riordan-Chen, Laura (Satellogic Inc)
Garcia, Clara (Satellogic Inc)
Becker, Guillermo (Satellogic Inc)
Garriga, Adan (EURECAT - Multimedia Technologies Unit)
Bukva, Anica (EURECAT - Multimedia Technologies Unit)
Serra-Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Vilaseca, David (Satellogic Inc)
Marin, Javier (Satellogic Inc)

Date:	2024
Abstract:	iquaflow is a framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated and a set of unsupervised methods is offered by default. Furthermore, iquaflow measures quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem involves finding images that, while being compressed to their smallest possible file size, still maintain sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with iquaflow is suitable for such case. All this development is wrapped in Mlflow: an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookiecutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub.
Grants:	Agencia Estatal de Investigación RTC2019-007434-7
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Artificial intelligence ; Data compression ; Image analysis ; Image processing ; Image resolution ; Image segmentation ; Object detection ; Quality control ; Remote sensing
Published in:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17 (2024) , p. 3285-3296, ISSN 2151-1535

DOI: 10.1109/JSTARS.2023.3342475

12 p, 3.1 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Engineering > Group on Interactive Coding of Images (GICI)
Articles > Research articles
Articles > Published articles