AI-generated Image Quality Assessment in Visual Communication
This addresses the need for better quality assessment in advertising applications, but it is incremental as it focuses on dataset creation and benchmarking.
The paper tackles the problem of assessing AI-generated image quality for real-world visual communication by introducing AIGI-VC, a dataset of 2,500 images with human annotations, and finds that existing methods have strengths and weaknesses in predicting preferences.
Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality assessment (IQA) algorithms primarily focus on low-level visual perception, while existing IQA works on AIGIs overemphasize the generated content itself, neglecting its effectiveness in real-world applications. To bridge this gap, we propose AIGI-VC, a quality assessment database for AI-Generated Images in Visual Communication, which studies the communicability of AIGIs in the advertising field from the perspectives of information clarity and emotional interaction. The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types. It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning. We conduct an empirical study of existing representative IQA methods and large multi-modal models on the AIGI-VC dataset, uncovering their strengths and weaknesses.