MMP-2k: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database

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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Published
2025-08-18
DOI
https://doi.org/10.1109/icip55913.2025.11084596
Primary Topic
Advanced Image Fusion Techniques
Type
article
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article

MMP-2k: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database

Jenghwa Chang, Hanhe Lin, Jianxun Lou, Zhengyi Li et al.
Advanced Image Fusion Techniques
article

MMP-2k: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database

Jenghwa Chang, Hanhe Lin, Jianxun Lou, Zhengyi Li, Zhen Qiu
article en

Abstract

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.

Central South University (CN), University of Dundee (GB), Northeast Electric Power University (CN)
Openalex Percentile: Top 71%
Advanced Image Fusion Techniques
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