MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database
This addresses a data limitation for developing MPIQA metrics, which is important for domains like scientific research and medical applications, but it is incremental as it primarily provides a new dataset.
The authors tackled the lack of a specialized dataset for macro photography image quality assessment (MPIQA) by creating MMP-2K, a benchmark database with 2,000 images, 17 quality ratings per image, and detailed distortion reports, and found that state-of-the-art generic IQA metrics underperform on macro images.
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.