AVT-AI-Image-Dataset - Appeal and Quality Assessment for AI-Generated Images
This dataset examines the appeal and quality of AI-generated images, addressing the critical question of to what extent AI-generated images are realistic or of high appeal from a photographic point of view and how users perceive them. Published alongside research in IEEE Access (2023) and at QoMEX 2023, the dataset was developed as part of the Deutsche Forschungsgemeinschaft (DFG) funded research (DFG-437543412).
Description
This dataset examines the appeal and quality of AI-generated images, addressing the critical question of to what extent AI-generated images are realistic or of high appeal from a photographic point of view and how users perceive them. Published alongside research in IEEE Access (2023) and at QoMEX 2023, the dataset was developed as part of the Deutsche Forschungsgemeinschaft (DFG) funded research (DFG-437543412).
The dataset comprises 135 images generated using five different AI text-to-image generators (including DALL-E-2, Midjourney, and Craiyon) based on 27 different text prompts. Some prompts were derived from the DrawBench benchmark, ensuring diversity in scene complexity and description specificity. The generated images were combined with real photographs for comparison in subjective evaluation studies.
A companion paper at QoMEX 2023 (DOI: 10.1109/QoMEX58391.2023.10178486) extends this analysis by evaluating quality and appeal through crowdsourcing tests and correlating subjective ratings with objective quality assessment models.
Access
Openly available for download from GitHub repository
License
GNU General Public License v3.0