# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 2 Related Studies
[26] introduced a context-based approach, employing a graphical model to capture the influence of context on image quality. The model describes the relationship between content and background, allowing for the extraction of context features used for quality score prediction. [PERSON] et al. [27] proposed a NR-IQA method that utilizes attention mechanisms for feature fusion. They employed VGG19, VGG16, and ResNet-50 to extract the texture, local, and global information of the images, respectively. By incorporating attention mechanisms, they fused the multilevel features to enable perception of different types of distortions. [PERSON] et al. [28] also proposed a method called MMMNet, which combines multi-scale and multi-level features for fusion. MMMNet treats the extraction of saliency information as a subtask and leverages a multitask learning mechanism to enhance model performance. Finally, it fuses the multi-scale saliency features with content features to predict quality scores. In recent years, some novel deep learning methods have achieved significant success in various visual domains. Researchers have also applied these methods to IQA. [PERSON] et al. applied meta-learning to IQA and proposed MetalQA [29] and MetaIQA+ [30]. They regarded the model's handling of different distortions as having different aims and introduced a task selection strategy to enhance the model's robustness when encountering unseen distortions, thereby improving the model's generalization capability. [PERSON] et al. [31] introduced Continual Learning into IQA, enabling the model to learn continuously from a series of datasets. They use three metrics to measure prediction accuracy, adaptability, and robustness. They introduce a prediction head for each new dataset and incorporate a regularization mechanism that enables the evolution of all prediction heads while safe guarding against catastrophic forgetting. [PERSON] et al. [32] proposed CONTRIQUE, a framework that leverages contrastive learning for training on datasets without quality scores. CONTRIQUE incorporates predicting distortion types and levels as auxiliary tasks and employs contrastive learning for pretraining on datasets without quality scores. Subsequently, the feature extraction parameters are frozen, and the score prediction part is trained on the target dataset. Experimental results demonstrate that CONTRIQUE exhibits outstanding generalization performance. Several GAN-based approaches have been proposed to tackle the issue of a lack of reference images by generating pseudo-reference images. [PERSON] et al. [16] proposed restorative adversarial nets for NR-IQA(RAN4 IQA), which include a restorer, a discriminator, and a predictor. The restorer and discriminator collaborate to generate the pseudo-reference images, and subsequently, the predictor extracts features from the distorted and pseudo-reference images and maps them to quality scores. Similarly, [PERSON] et al. [17] proposed a Hall-IQA, which also generates pseudo-reference images using GAN and uses the difference image between the distorted and pseudo-reference images as input to a regression network for quality score prediction. Both RAN4 IQA and Hall-IQA have achieved good results, but GAN-based methods have a shaky training process and are difficult to achieve good image restoration performance, especially when faced with severely distorted images. [PERSON] et al. [33] proposed a method based on visual compensation restoration (VCR), named VCRNet, which uses the restoration features to avoid the performance degradation caused by the suboptimal quality of the pseudo-reference image. However, it does not use the differential features between the pseudo-reference and the distorted images. Despite the notable achievements of the aforementioned approaches, there are still opportunities for further enhancement.