Appeal prediction for AI up-scaled Images

TL;DR

This paper introduces a new dataset and evaluation framework for assessing the appeal of AI up-scaled images, highlighting the limitations of existing quality metrics and proposing novel models for appeal prediction.

Contribution

The paper presents a comprehensive dataset with appeal annotations, evaluates multiple up-scaling methods, and develops new appeal prediction models using transfer learning and signal features.

Findings

Real-ESRGAN and BSRGAN produce the most appealing images

Existing image quality models do not predict appeal well

Transfer learning-based appeal models outperform others

Abstract

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.