TY - GEN
T1 - MMP-2K
T2 - 32nd IEEE International Conference on Image Processing, ICIP 2025
AU - Chang, Jiashuo
AU - Li, Zhengyi
AU - Lou, Jianxun
AU - Qiu, Zhen
AU - Lin, Hanhe
N1 - Publisher Copyright:
©2025 IEEE.
PY - 2025/8/18
Y1 - 2025/8/18
N2 - 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.
AB - 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.
KW - benchmark database
KW - image quality assessment
KW - Macro photography
KW - subjective study
UR - https://www.scopus.com/pages/publications/105028562161
U2 - 10.1109/ICIP55913.2025.11084596
DO - 10.1109/ICIP55913.2025.11084596
M3 - Conference contribution
AN - SCOPUS:105028562161
SN - 9798331523800
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 169
EP - 174
BT - 2025 IEEE International Conference on Image Processing, ICIP 2025 - Proceedings
PB - IEEE
CY - USA
Y2 - 14 September 2025 through 17 September 2025
ER -