MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 25 May 2025.
Builds on
11 of 18 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
- 2022MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessmentcited by 522
- 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 355
- 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
- 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 280
- 2023TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessmentcited by 226
- 2018The 2018 PIRM Challenge on Perceptual Image Super-resolutioncited by 156
- 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144
- 2023ARNIQA: Learning Distortion Manifold for Image Quality Assessmentcited by 89
- 2022Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Modelcited by 22
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