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
this https URL.

IQUAFLOW 是一个新的图像质量框架，提供了一套工具来评估图像质量，可以通过使用 AI 模型的性能作为代理来测量质量，Wrapped in Mlflow。