AI-generated Image Quality Assessment in Visual Communication

TL;DR

This paper introduces AIGI-VC, a new dataset and evaluation framework for assessing the quality of AI-generated images in advertising, focusing on their communicability and emotional impact.

Contribution

It presents a novel dataset for AI-generated image quality assessment in visual communication and benchmarks existing IQA methods and models on this dataset.

Findings

Existing IQA methods have limitations in preference prediction.

Large multi-modal models show potential but also weaknesses.

The dataset enables comprehensive evaluation of AI-generated image quality.

Abstract

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.