When developing machine learning models, image quality assessment (IQA) measures are a crucial component for evaluation. However, commonly used IQA measures have been primarily developed and optimized for natural images. In many specialized settings, such as medical images, this poses an often-overlooked problem regarding suitability. In previous studies, the IQA measure HaarPSI showed promising behavior for natural and medical images. HaarPSI is based on Haar wavelet representations and the framework allows optimization of two parameters. So far, these parameters have been aligned for natural images. Here, we optimize these parameters for two annotated medical data sets, a photoacoustic and a chest X-Ray data set. We observe that they are more sensitive to the parameter choices than the employed natural images, and on the other hand both medical data sets lead to similar parameter values when optimized. We denote the optimized setting, which improves the performance for the medical images notably, by HaarPSI$_{MED}$. The results suggest that adapting common IQA measures within their frameworks for medical images can provide a valuable, generalizable addition to the employment of more specific task-based measures.

翻译：在开发机器学习模型时，图像质量评估（IQA）指标是评估过程中的关键组成部分。然而，常用的IQA指标主要是针对自然图像开发和优化的。在许多专业场景中（例如医学图像），这带来了一个常被忽视的适用性问题。在先前的研究中，IQA指标HaarPSI在自然图像和医学图像上均表现出良好的性能。HaarPSI基于Haar小波表示，其框架允许优化两个参数。迄今为止，这些参数一直针对自然图像进行调整。本文中，我们针对两个带标注的医学数据集（一个光声数据集和一个胸部X射线数据集）优化了这些参数。我们观察到，与所使用的自然图像相比，医学图像对参数选择更为敏感；另一方面，当进行优化时，两个医学数据集都导向了相似的参数值。我们将优化后的设置记为HaarPSI$_{MED}$，它显著提升了医学图像上的性能。结果表明，在现有框架内针对医学图像调整通用IQA指标，可以为使用更具体的基于任务的评估方法提供有价值且可推广的补充。