# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 2 Related Studies  
Owing to the absence of reference images, numerous conventional NR-IQA approaches concentrate on particular distortion types present in images. For example, filtering-based methods are used to estimate noise in images [18], and sharpness and blurriness estimation algorithms are used to evaluate the quality of blurry images [19]. These methods can achieve higher accuracy when the distortion type is identifiable. In addition, some methods [7; 8; 9] do not target specific distortion(s), but instead extract general quality features that can describe multiple types of distortion to evaluate the quality. The focus and difficulty of this method lies in selecting which features to measure the level of distortion. This is generally manually extracted through natural scene statistics (NSS) in traditional methods, and it can be automatically learned in deep learning-based methods [20; 21; 22; 23]. [PERSON] and [PERSON] [7] first proposed a blind image quality index (BIQI) for general distortion types. This method fits the wavelet decomposition coefficients of images with a Generalized Gaussian Distribution (GGD) and uses the parameters of the GGD model as features. [PERSON] et al. [8] proposed a blind/referenceless image spatial quality evaluator (BRISQUE) to utilize NSS in the spatial domain. This approach first computes the multi-scale mean-subtracted contrast-normalized (MSCN) coefficients of distorted images. Then, it fits the MSCN coefficients and their related coefficients to predict quality scores. [PERSON] et al. [9] proposed a feature map-based referenceless image quality evaluation engine (FRIQUEE) for authentic distortion assessment. The aim of this method is to capture the statistical consistency or deviations from consistency in authentically distorted images without assuming the presence of a specific distortion type. With the development and improvement of deep learning, its application in IQA has received increasing attention. Deep learning has powerful fitting and generalization capabilities, enabling it to learn feature representations from large amounts of training data and associate these features with image quality. Therefore, more and more researchers have explored the use of deep learning algorithms to improve the accuracy and reliability of IQA. [PERSON] et al. [20] first proposed an IQA-CNN method that employs convolutional neural networks (CNNs). IQA-CNN utilizes a single convolutional layer for feature extraction and maps the extracted features to quality scores through two fully connected layers. [PERSON] et al. [21] employed a deeper CNN to extract high-level features from images. The method involves a modified VGG16 and two fully connected layers for feature extraction and score prediction. Results indicate that it can significantly improve the performance of the model using high-level image features. [PERSON] et al. [22] proposed HyperIQA for authentically distorted images. This method predicts quality scores based on the perception of content. [PERSON] et al. [23] proposed a deep bilinear CNN (DB-CNN), which utilizes two streams to extract synthetic distortion features and authentic distortion features from images. [PERSON] et al. [24] proposed an NR-IQA method called blind predicting similar quality map (BPSQM), which consists of a fully convolutional neural network and a pooling network. The global convolutional network is trained using the similarity maps from traditional FR-IQA methods, enabling the network to predict the corresponding similarity quality map for distorted images. The pooling network is then employed to regress the quality map into a quality score. [PERSON] et al. [25] proposed a Distortion-Aware CNN (DACNN) for NR-IQA. DACNN employs a pretraining strategy to extract distinctive features for both synthetic and authentic distortions. These features are then fused using a feature fusion module and mapped to quality scores using a quality prediction module. [PERSON] et al.