IEEE Access (Jan 2023)

Analysis of Appeal for Realistic AI-Generated Photos

  • Steve Goring,
  • Rakesh Rao Ramachandra Rao,
  • Rasmus Merten,
  • Alexander Raake

DOI
https://doi.org/10.1109/ACCESS.2023.3267968
Journal volume & issue
Vol. 11
pp. 38999 – 39012

Abstract

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AI-generated images have gained in popularity in recent years due to improvements and developments in the field of artificial intelligence. This has led to several new AI generators, which may produce realistic, funny, and impressive images using a simple text prompt. DALL-E-2, Midjourney, and Craiyon are a few examples of the mentioned approaches. In general, it can be seen that the quality, realism, and appeal of the images vary depending on the used approach. Therefore, in this paper, we analyze to what extent such AI-generated images are realistic or of high appeal from a more photographic point of view and how users perceive them. To evaluate the appeal of several state-of-the-art AI generators, we develop a dataset consisting of 27 different text prompts, with some of them being based on the DrawBench prompts. Using these prompts we generated a total of 135 images with five different AI-Text-To-Image generators. These images in combination with real photos form the basis of our evaluation. The evaluation is based on an online subjective study and the results are compared with state-of-the-art image quality models and features. The results indicate that some of the included generators are able to produce realistic and highly appealing images. However, this depends on the approach and text prompt to a large extent. The dataset and evaluation of this paper are made publicly available for reproducibility, following an Open Science approach.

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