构建了2000张微距摄影图像质量评估数据库，解决高质量数据缺失问题。
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database
- 从1.57万张图中筛选2000张，每张有17个质量评分和详细失真报告
- 实验证明通用图像质量评估模型在微距图像上表现不佳
- 适合图像质量评估、计算机视觉及科研摄影领域研究者使用
微距摄影（MP）是一种近距离拍摄物体以展现细微细节的特殊摄影技术，在科研和医疗等领域具有重要价值。然而，准确的微距图像质量评估（MPIQA）指标发展受限于缺乏高质量标注数据。为此，我们开展大规模MPIQA研究：从三个公开网站收集的15,700张微距图像中，筛选出2,000张具有多样内容与质量的图像；通过实验室研究，为每张图像获取17个（21项中的有效评分）质量评分，并生成包含失真类型、程度与位置的详细质量报告。这些数据构成新型多标签MPIQA数据库MMP-2k。实验表明，现有主流通用图像质量评估方法在微距图像上性能显著下降。数据库及相关材料已开源至https://github.com/Future-IQA/MMP-2k。
原文摘要 · Abstract (English)
Macro photography (MP) is a specialized field of photography that captures objects at an extremely close range, revealing tiny details. Although an accurate macro photography image quality assessment (MPIQA) metric can benefit macro photograph capturing, which is vital in some domains such as scientific research and medical applications, the lack of MPIQA data limits the development of MPIQA metrics. To address this limitation, we conducted a large-scale MPIQA study. Specifically, to ensure diversity both in content and quality, we sampled 2,000 MP images from 15,700 MP images, collected from three public image websites. For each MP image, 17 (out of 21 after outlier removal) quality ratings and a detailed quality report of distortion magnitudes, types, and positions are gathered by a lab study. The images, quality ratings, and quality reports form our novel multi-labeled MPIQA database, MMP-2k. Experimental results showed that the state-of-the-art generic IQA metrics underperform on MP images. The database and supplementary materials are available at https://github.com/Future-IQA/MMP-2k.
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