A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as A Proxy

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...

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Vydáno v:IEEE journal of selected topics in applied earth observations and remote sensing Ročník 17; s. 1 - 12
Hlavní autoři: Galles, P., Takats, K., Hernandez-Cabronero, M., Berga, D., Pega, L., Riordan-Chen, L., Garcia, C., Becker, G., Garriga, A., Bukva, A., Serra-Sagrista, J., Vilaseca, D., Marin, J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial intelligence
Cognitive tasks
Compression
Data compression
Deep learning
Downloading
Image analysis
Image coding
Image compression
Image processing
Image quality
Image reconstruction
Image resolution
Image segmentation
Machine learning
Object detection
Performance degradation
Quality assessment
Quality control
Remote sensing
Repositories
Satellite imagery
Satellites
Source code
Task analysis
Training
Transform coding
ISSN:1939-1404, 2151-1535
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Popis
Shrnutí: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.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2023.3342475