# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 1 Introduction  
Images play a pivotal role in conveying valuable information and have gained significant prominence across diverse domains, encompassing advertising, entertainment, medicine, education, and numerous other areas of application. However, factors (e.g., lighting conditions during image acquisition and stability of the transmission path) may result in the loss of original image information, which can seriously affect users' visual experience and the effectiveness of image usage [1]. The goal of image quality assessment (IQA) is to assess the quality of images using certain objective methods and provide valuable evaluation results. Accurate and efficient IQA methods are of great significance in improving user experience and optimizing the accuracy of vision-related applications. IQA methods can be categorized into three types based on the reference image usage: full-reference IQA (FR-IQA) [2, 3, 4], reduced-reference IQA (RR-IQA) [5, 6], and no-reference IQA (NR-IQA) [7, 8, 9, 10]. NR-IQA has higher practicality, especially in situations where it is difficult to obtain reference images in practical applications. Therefore, NR-IQA has become one of the research hotspots in the field of IQA and has important practical research value [11]. The primary difficulty in NR-IQA lies in the absence of reference images, which renders it infeasible to assess quality by comparing the features of distorted images with thoseof reference images. However, to perceive distorted images, based on prior knowledge in the brain, the human visual system (HVS) utilizes an internal generative mechanism (IGM) [12; 13; 14] to reconstruct the reference image as much as possible. Subsequently, the evaluation is performed by analyzing the distinctions between the reconstructed image and the distorted image [15], with greater differences indicating more severe image distortion. To acquire prior knowledge of reference images, some researchers have proposed methods based on generative adversarial networks (GANs) to generate pseudo-reference images [16; 17]. Following that, the assessment of image quality is conducted by evaluating the difference between the distorted image and the pseudo-reference image. However, GAN-based methods typically face two challenges. First, the training process of GANs is often unstable, making it difficult to achieve satisfactory image restoration performance. Secondly, when dealing with severely distorted images, GANs may struggle to effectively restore image quality. To address the aforementioned problems, a NR-IQA method based on a multitask image restoration network (MT-IRN) is proposed in this paper. MT-IRN includes a multitask image restoration sub-network and a score prediction sub-network. The multitask image restoration sub-network is used to generate pseudo-reference images and structural similarity index measure (SSIM) maps between distorted images and reference images, as well as to extract quality restoration features. The score prediction sub-network is used to extract high-level features and multi-scale content features of the distorted image, and difference features between the distorted and pseudo-reference images, and it maps these features to quality scores after concatenation. Specifically, the contributions of this paper are as follows:  
* First, a multitask image restoration network is introduced to generate high-quality surrogate reference images. The multitask image restoration network has a main task to generate pseudo-reference images and an auxiliary task to generate structurally similar images, with the two tasks mutually reinforcing each other to generate higher-quality pseudo-reference images.