IQUAFLOW: A new framework to measure image quality
This provides a tool for researchers and practitioners to optimize image quality for AI tasks, such as in satellite imagery, but it is incremental as it builds on existing concepts like Mlflow and custom metrics.
The authors introduced IQUAFLOW, a framework for measuring image quality by integrating custom metrics and using AI model performance as a proxy, enabling studies on performance degradation from modifications like lossy compression, with satellite images as an example use case.
IQUAFLOW is a new image quality framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated. Furthermore, iquaflow allows to measure 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 consists in finding the smallest images that provide yet 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 cookie-cutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub https://github.com/satellogic/iquaflow.