Content-oriented image quality assessment with multi-label SVM classifier

Abstract

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.