首个图像调和质量评估基准与模型，精准预测人眼偏好。
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment
- 构建1350张调和图像数据集，涵盖9种算法生成结果。
- 在人眼偏好评估上超越现有方法，跨数据集泛化更强。
- 适合图像合成、视觉感知研究者使用。
图像合成需将前景物体从原图提取并粘贴至背景图中，通过图像调和算法（IHAs）调整前景外观以匹配背景。现有图像质量评估（IQA）方法因对细微色彩或光照不一致敏感度不足，难以契合人眼偏好。为此，我们提出首个用于图像调和评估的图像质量评估数据库（HarmonyIQAD），包含由9种不同IHAs生成的1,350张调和图像及对应的人类视觉偏好评分。基于该数据集，我们设计了调和图像质量评估模型（HarmonyIQA），可预测人眼对调和图像的偏好。大量实验表明，HarmonyIQA在调和图像的人眼偏好评估上达到当前最优性能，并在传统IQA任务中表现竞争力。跨数据集评估进一步显示，其泛化能力优于自监督学习基线方法。HarmonyIQAD与HarmonyIQA将在论文发表后公开共享。
原文摘要 · Abstract (English)
Image composition involves extracting a foreground object from one image and pasting it into another image through Image harmonization algorithms (IHAs), which aim to adjust the appearance of the foreground object to better match the background. Existing image quality assessment (IQA) methods may fail to align with human visual preference on image harmonization due to the insensitivity to minor color or light inconsistency. To address the issue and facilitate the advancement of IHAs, we introduce the first Image Quality Assessment Database for image Harmony evaluation (HarmonyIQAD), which consists of 1,350 harmonized images generated by 9 different IHAs, and the corresponding human visual preference scores. Based on this database, we propose a Harmony Image Quality Assessment (HarmonyIQA), to predict human visual preference for harmonized images. Extensive experiments show that HarmonyIQA achieves state-of-the-art performance on human visual preference evaluation for harmonized images, and also achieves competing results on traditional IQA tasks. Furthermore, cross-dataset evaluation also shows that HarmonyIQA exhibits better generalization ability than self-supervised learning-based IQA methods. Both HarmonyIQAD and HarmonyIQA will be made publicly available upon paper publication.
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