A Comparative Study of DNN-Based Models for Blind Image Quality Prediction
Xiaohan Yang, Fan Li, Hantao Liu · 2019
Recently, deep learning methods have gained substantial attention in the research community and have proven useful for blind image quality assessment (BIQA). Although previous study of deep neural networks (DNN) methods is presented, some novelty methods, which are recently proposed, are not summarized. In this paper, we provide a comparative study on the application of DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods. Then, we compare the predictive performance of various methods in synthetic and authentic databases, providing important information that can help understand the underlying properties between different methods. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future.