DNN- or AI-based up-scaling algorithms are gaining in popularity due to the improvements in machine learning. Various up-scaling models using CNNs, GANs or mixed approaches have been published. The majority of models are evaluated using PSRN and SSIM or only a few example images. However, a performance evaluation with a wide range of real-world images and subjective evaluation is missing, which we tackle in the following paper. For this reason, we describe our developed dataset, which uses 136 base images and five different up-scaling methods, namely Real-ESRGAN, BSRGAN, waifu2x, KXNet, and Lanczos. Overall the dataset consists of 1496 annotated images. The labeling of our dataset focused on image appeal and has been performed using crowd-sourcing employing our open-source tool AVRate Voyager. We evaluate the appeal of the different methods, and the results indicate that Real-ESRGAN and BSRGAN are the best. Furthermore, we train a DNN to detect which up-scaling method has been used, the trained models have a good overall performance in our evaluation. In addition to this, we evaluate state-of-the-art image appeal and quality models, here none of the models showed a high prediction performance, therefore we also trained two own approaches. The first uses transfer learning and has the best performance, and the second model uses signal-based features and a random forest model with good overall performance. We share the data and implementation to allow further research in the context of open science.

本研究解决了当前图像超分辨率模型在真实世界中缺乏有效性能评估的问题。通过构建包含136张基础图像和5种不同超分辨率方法的数据库，我们评估了不同方法的图像吸引力，发现Real-ESRGAN和BSRGAN效果最佳。此外，我们还训练了深度学习模型来识别所使用的超分辨率方法，并评估了现有图像质量模型的性能。此项研究对图像超分辨率技术的进一步发展具有重要影响。

人工智能超分辨率图像的吸引力预测