###### Abstract
In recent years, more attention has been paid to deep-learning-based approaches, and as a result, several good algorithms have been produced. [PERSON] et al. proposed a quality prediction model based on a multitask learning framework, called the MEON model [23]. It consisted of a distortion identification task and a quality regression task. First, the deep features were extracted based on a conventional Convolutional Neural Network (CNN). Then, the distortion identification network was trained by the image features. Finally, the quality features and the output of the distortion identification network were used to predict the visual quality. [PERSON] et al. proposed a weight-based quality metric, which focused on the local information of the distorted images. The local quality and local weights could be jointly learned [24]. With the support of CNN, the prediction performance of these algorithms was further improved and proved to be significantly better than the traditional handcrafted algorithms. However, most of them only used the features of a single scale, that is, the features of the highest level, and ignored the effective description of other scale features to the distortion. Therefore, this kind of method has much room for improvement. To alleviate this problem, researchers have focused on research algorithms based on multiscale features. For example, HyperNet [25] extracts deep features at each level, capturing both local and global distortions. However, multilevel features are only gathered together by a simple concatenation operation, and there is a lack of research and exploration on the relationship between levels. Compared with the former, this kind of algorithm can often obtain better performance. However, how to integrate multilevel features effectively needs further research and discussion. Although deep learning-based IQA methods have made significant progress in both synthetic and authentic distortions, there is still much room for improvement. Inspired by the above, we propose a novel blind image quality index for synthetic and authentic distortions with hierarchical feature fusion, dubbed HFNet. First, multiscale quality-aware features are extracted in a strong vision transformer network. Then, an effective hierarchical feature fusion module is proposed to incorporate features at different levels progressively. In order for the model to focus on more important information during learning, an attention mechanism is used after each fusion process. Then, to simulate human visual characteristics, local and global features from the fusion features are extracted to represent different granularity distortions. Finally, these elaborate features are mapped to the quality of the distorted image. Extensive experiments on synthetic distortion datasets and authentic distortion datasets show the superiority of the proposed HFNet in terms of both single-database testing and cross-database testing. The contributions of the proposed HFNet are summarized as follows:  
* Different from the previous simple stitching-based method, the proposed HFNet gradually integrates the features of different levels; so, the low-level features to high-level features can be used to represent the different types and degrees of distortion more effectively. * It has been proved in the literature that people always view images in a combination of local and global ways in order to better understand the content of images. To simulate this visual property, we propose to extract local and global features to understand the distortion more comprehensively. * We conducted extensive experiments on both synthetic and authentic distortion datasets, and the proposed HFNet is compared with both the traditional and deep learning state-of-the-art quality metrics. The superiority of the proposed HFNet is verified by extensive experiments. This paper consists of five sections, first introducing the motivation for this work and then describing two types of related work, providing some of the early groundwork.