###### Abstract  
Blind Image Quality Assessment (BIQA) for synthetic and authentic distortions has attracted much attention in the community, and it is still a great challenge. The existing quality metrics are mildly consistent with subjective perception. Traditional handcrafted quality metrics can easily and directly extract low-level features, which mainly account for the outline, edge, color, texture, and shape features, while ignoring the important deep semantics of the distorted image. In the field of popular deep learning, multilevel features can be acquired easily. However, most of them either use only high-level features, ignoring the shallow features, or they simply combine features at different levels, resulting in limited prediction performance. Motivated by these, this paper presents a novel BIQA for synthetic and authentic distortions with hierarchical feature fusion in a flexible vision-Transformer framework. First, multiscale features are extracted from a strong vision-Transformer backbone. Second, an effective hierarchical feature fusion module is proposed to incorporate the features at different levels progressively. To eliminate redundant information, a simple but effective attention mechanism is employed after each fusion. Third, inspired by the human visual system, local and global features from the fusion features are extracted to represent different granularity distortions. Finally, these local and global features are mapped to the final quality score. Extensive experiments on three authentic image databases and two synthetic image datasets show that the proposed method is superior to the state-of-the-art quality metrics for both single-database testing and cross-database testing. vision-Transformer; image quality evaluation; synthetic distortion; authentic distortion; feature fusion +
Footnote †: journal: Medical Image Quality Assessment  
quality metric (RR-IQA), and a full-reference quality metric (FR-IQA) [12; 13; 14; 15; 16; 17; 18; 19; 20]. In FR-IQA, in addition to the distorted image, a reference image without distortion is needed; in RR-IQA, the distorted image and some information of the clear image are needed; so, there is no complete reference image; and in NR-IQA, only the distorted image is given. In practice, there is no ideal reference image most of the time, so NR-IQA is more favored by researchers and is also the focus of this paper. In the past decade, the research community has made great efforts, and numerous traditional NR-IQA metrics have been proposed, which can be divided into opinion-unaware and opinion-aware quality metrics. The former does not require the participation of subjective scores in the model construction. The idea is to first construct a standard quality model based on the clear images and then measure its distance from the distorted image. IL-NIQE [21], which is a typical example, extracts five types of Natural Scene Statistics (NSS) features from a series of clear images and uses these quality-aware features to learn a multivariate Gaussian model of the clear images without distortion. Then, this carefully designed model is used as a benchmark to measure the distance between distorted image blocks. The latter generally follows a two-step framework, feature extraction based on statistics and quality regression based on machine learning. A classic example is the BRISQUE, which first extracts 36 NSS features to quantify the naturalness of the distorted image in the spatial domain; then, the universal regression tool, the Support Vector Regressor (SVR), is used for quality evaluation [22]. Although the above two kinds of methods have achieved promising results in the evaluation of synthetic distortion, their performance still has room for improvement because it is difficult for handcrafted features to represent the semantic information, which is very important for the IQA. In addition, when facing authentic distortion, their performance deteriorates obviously, and satisfactory predictions are rare.