HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment
This work addresses the need for better quality assessment in image harmonization, which is important for computer vision applications, but it is incremental as it builds on existing IQA methods with a new dataset and model.
The paper tackles the problem of assessing image harmonization quality by introducing HarmonyIQAD, a benchmark with 1,350 images and human scores, and HarmonyIQA, a model that achieves state-of-the-art performance in predicting human visual preference for harmonized images.
Image composition involves extracting a foreground object from one image and pasting it into another image through Image harmonization algorithms (IHAs), which aim to adjust the appearance of the foreground object to better match the background. Existing image quality assessment (IQA) methods may fail to align with human visual preference on image harmonization due to the insensitivity to minor color or light inconsistency. To address the issue and facilitate the advancement of IHAs, we introduce the first Image Quality Assessment Database for image Harmony evaluation (HarmonyIQAD), which consists of 1,350 harmonized images generated by 9 different IHAs, and the corresponding human visual preference scores. Based on this database, we propose a Harmony Image Quality Assessment (HarmonyIQA), to predict human visual preference for harmonized images. Extensive experiments show that HarmonyIQA achieves state-of-the-art performance on human visual preference evaluation for harmonized images, and also achieves competing results on traditional IQA tasks. Furthermore, cross-dataset evaluation also shows that HarmonyIQA exhibits better generalization ability than self-supervised learning-based IQA methods. Both HarmonyIQAD and HarmonyIQA will be made publicly available upon paper publication.