为AI生成图像在广告中的传播效果建库并评估质量。
AI-generated Image Quality Assessment in Visual Communication
- 构建面向视觉传播的AIGI质量评估数据集，关注信息清晰度与情感互动。
- 包含2500张图像，覆盖14类广告主题和8种情绪类型，提供细粒度偏好标注。
- 适合研究AI图像评估、广告内容设计及多模态模型应用的学者与工程师。
评估人工智能生成图像（AIGIs）的质量在真实应用场景中至关重要。然而，传统图像质量评估（IQA）算法主要关注低层视觉感知，而现有针对AIGIs的研究过度强调生成内容本身，忽视其在实际应用中的传播有效性。为填补这一空白，我们提出AIGI-VC，一个面向视觉传播的AI生成图像质量评估数据库，从信息清晰度与情感互动角度，研究AIGIs在广告领域的可传播性。该数据集包含2500张图像，涵盖14个广告主题和8种情绪类型，提供粗粒度人类偏好标注与细粒度偏好描述，用于基准测试IQA方法在偏好预测、解释与推理方面的能力。我们对现有代表性IQA方法及大型多模态模型在AIGI-VC数据集上进行了实证研究，揭示了它们的优势与不足。
原文摘要 · Abstract (English)
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.
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