## 2 Related Work
### Blind Image Quality Assessment  
Traditional quality metrics rely on extracting discriminating handcrafted features to construct prediction models. BRISQUE [22] extracts multiple sets of features to represent all kinds of distortions; thus, in theory, it can deal with all the distortions. CORNIA [26] verified the possibility of learning to distinguish the quality-aware features directly from the original pixel information and adopted unsupervised feature learning methods, soft task coding, and a maximum pooling layer to obtain image representation, which achieved good image prediction results. In [27], the authors constructed a quality evaluation method that did not require subjective scores or prior knowledge, NIQE for short. Based on codebook construction and high-order statistics aggregation, the authors constructed an image quality evaluation method called HOSA [28]. BLINES-II [29] uses DCT coefficients as features and combines coefficients with statistical models of natural scenes to enrich the extraction of image features. Feature vectors are directly mapped to subjective scores through regression methods. The above quality metrics to study the various possible feature extraction methods used different regression strategies and tools (such as SVR and RF), but the handcrafted features do not completely conform to the vision mechanisms, and it is difficult to express image semantic information. Thus, the improvement in performance of the NR-IQA metrics was limited, so researchers began to explore quality metrics based on deep learning. With the success of deep learning in computer vision, researchers have applied it to IQA. The research community has been working to build high-performance high-generalization quality prediction models. In [23], the authors constructed some convolutional layers as shared feature extractors and then designed distortion discriminator and quality regression subnetworks. To obtain better performance, the features of the discriminant network were integrated into the prediction network to provide more information. DeepBIQ [30] extracted image features by using the pretraining model in ImageNet and then returned the image features to the image quality score. In [31], the feature extractor consisted of several groups of residual blocks, and multilevel features were extracted by a simple encoder. Then, these multiscale features were stitched together and used as quality regression. In [32], the model extracted multiscale deep features from a CNN network to describe the macro and micro information of the images, respectively, and then predicted the quality score through full connection or traditional regression. In [33], the authors conducted quality evaluation by constructing a CNN containing some general operations. Finally, the features of different layers were fused together to obtain more distinctive features. The fusion of local and global features is not studied further for IQA in this paper. BIECON [34] first predicts the scores of the image blocks and then aggregates the scores together. In [35], a probabilistic quality representation (PQR) was proposed. During training, a more robust loss function was employed. For the distorted image, the generated adversarial network was used to generate the restored image, and the distorted image and the restored image were input into the evaluation network together to generate the quality score [36]. In [37], the paper paid more attention to the influence of the content variation of the image on the NR-IQA quality models. Some unique opinions were analyzed in the experiments. To take advantage of traditional handcrafted features, a naturalness-aware BIQA metric was designed in [38]. This metric took the prediction of the handcrafted features as a subtask to assist in the quality prediction subtask. In [39], a two-stream BIQA model was proposed for both synthetic and authentic distortions. As discussed in [39], the two-stream model was versatile and extensible. [PERSON] et al.