Appeal and quality assessment for AI-generated images
Steve Göring, Rakesh Rao Ramachandra Rao, Rasmus Merten, Alexander Raake
Abstract
Recently AI-generated images gained in popularity. A critical aspect of AI-generated images using, e.g., DALL-E-2 or Midjourney, is that they may look artificial, be of low quality, or have a low appeal in contrast to real images, depending on the text prompt and AI generator. For this reason, we evaluate the quality and appeal of AI-generated images using a crowdsourcing test as an extension of our recently published AVT-AI-Image-Dataset. This dataset consists of a total of 135 images generated with five different AI-text-to-image generators. Based on the collected subjective ratings in the crowdsourcing test, we evaluate the different used AI generators in terms of image quality and appeal of the AI-generated images. We also link image quality and image appeal also with SoA objective models. The extension will be made publicly available for reproducibility.