## 1 Introduction  
With the rapid rise of digital technology, more people utilize applications related to multimedia content, which includes images and video-related content [1]. Unlike in the past, now people have also included another item in their multimedia consumption, which includes 3D images and video content. People are inclined toward 3D content more as it focuses on providing a near-reality experience and perception. An end user demands multimedia content, which could be either images, videos or 3D content. However, the crucial part is that the transmitted content should be of a high quality [2]. The development continued, as users expect high-quality images. The process starts with image acquisition, where an image is simply captured and converted into a digital format using reliable cameras. Furthermore, preprocessing is performed on the images, and they are transmitted over the channel [3]. Finally, image restoration is performed, after which an end user can have an experience with the multimedia content [4]. The problem is that each of these subsystems from image acquisition to displaying the image can induce certain types of distortions in the image. Therefore, the end resultant image will be a distorted version. On the other hand, the end user requires higher-quality images and multimedia content. A lot of research has been conducted to develop solutions to assess the quality of images in a way that should be quite accurate, and this assessment needs to be automatic [5]. Nonetheless, image quality assessment (IQA) is essential to ensure that the image is not affected by any type of distortion before the image reaches the end user. IQA can play a vital role in improving the quality of an image and in image restoration as well. Further down the line, IQA can be divided up into subjective quality scores and objective quality scores. In subjective image quality assessment (SIQA), the quality of an image is assessed from a human visual perspective. As the human eye can perceive and assess the quality in the best way possible, this gives SIQA an edge to be more accurate and reliable. However, this process can be more time-exhaustive and more expensive, and this process is not automatic, so it cannot be applied in practical real-time situations as, in this digital age, everything is moving toward automation [1]. On the contrary, objective image quality assessment (OIQA) focuses on the development of mathematical models assessing the quality of images, where the image quality will be quite accurate and automatic, which will allow this to be suitable for real-time applications [6]. Due to this reason, more research has been carried out on objective IQA. Objective IQA is further split into three types. The first one is full-reference IQA (FR-IQA), in which a pristine image is available, termed as a reference image. This reference image is used in the image quality assessment process to determine the quality scores of distorted images [7]. The second type is reduced-reference IQA (RR-IQA), which contains only partial information about the pristine image (also termed a reference image) [8]. The last type is no-reference IQA (NR-IQA), in which there is no reference image available which is a pristine version of a distorted image. NR-IQA is more practical and applies to real-time situations as well [9]. As the NR-IQA approach is more adaptive and suitable for real-time applications, it is hence widely applicable in multimedia content applications. This reason makes NR-IQA a hot topic for research in the research community. Furthermore, NR-IQA is subdivided into two types, which include NR-IQA methods that are designed for a specific type of distortion (i.e., Gaussian blur). The other type includes NR-IQA methods which are designed for general purposes and do not focus on a specific type of distortion and noise in an image. Rather, they focus on image quality assessment on a general scale [6]. As was briefly described above that, FR-IQA and RR-IQA are only able to assess the image quality by using reference images.