Image content is a fundamental attribute of images and plays an important role in human perception of image information. However, the influence of image content type, which is derived based on the classification of the image content, has been largely ignored in the image quality assessment (IQA). In this paper, a new IQA database based on the classification of image content is built. In particular, the database contains four content types, including landscape, human face, handcrafted scene and the hybrid scene. In total, 80 reference images with 20 images for each type of content are involved, and 1600 distorted images with mean opinion scores (MOSs) are generated by using five types and four levels of distortion. Furthermore, to classify these images, especially for the hybrid case, a Support Vector Machine (SVM) based multi-label (ML) classification is presented. Extensive experiments based on existing no reference IQA (NR-IQA) models show that content classification can greatly facilitate the image quality evaluation. The database and code are made publicly available at: https://github.com/jingchao17/Content-oriented-Database.
Bibliographical noteThis work was supported in part by Hong Kong RGC General Research Fund 9042489 under Grant CityU 11206317, in part by Hong Kong RGC General Research Fund 9042322 under Grant CityU 11200116, in part by the Hong Kong RGC Early Career Scheme under Grant 9048122 ( CityU 21211018 ), in part by the City University of Hong Kong under Grant 7200539/CS and in part by the National Natural Science Foundation of China under Grant 61772344 and Grant 61811530324 .
- Image content classification
- Image quality assessment
- Objective quality
- Subjective quality