## 1 Introduction  
Images have become a vital channel for conveying information due to the rapid volant of digital technology. This led to an immersive user experience and the exponential stacking of digital images in large data volumes. A decline in the quality of digital images takes place during the cyclic period of acquiring the images, performing image compression, processing, and transmitting the images to the data storage point. This inevitable fact consequently governs the immersive user experience, leading to abysmal image quality, thus making a space for an image quality assessment IQA approach to be filled in [1]. On the other side of the spectrum, being able to robustly assess the image quality deeply affects various computer vision applications, which broadly include bio-medical imaging, self-driving vehicles, and object detection, to name a few [2, 3]. Among the two quality assessment approaches, subjective and objective IQA, the latter is widely utilized, even though subjective IQA is a definitive and nearly error-free approach. The induction of human evaluation in the latter approach makes it a time extensive and tedious task, thus giving a selective priority to objective IQA, which automatically deduces the image quality by deploying computational methods capable of speculating the image quality in a manner that is harmonious with the image quality scores of human subjects [4]. Objective IQA is sub-classed into full reference IQA, reduce-reference IQA, and no-reference IQA, widely labeled as Blind IQA. The availability of distortion-free versions of images, termed as pristine images, classifies the three types. The reference or pristine image is unavailable during the process of BIQA, making it a noteworthy assessment approach among the three objective IQA methods [4, 5]. Moreover, BIQA is further classified into distortion-specific and general-purpose image quality assessment approaches. The latter approach is designed keeping in mind the fact that multiple distortions can occur in a digital image, making it superior to the distortion-specific approach. As the general-purpose approach is not limited to a specific type of image distortion, its ability to assess image quality highly relies on obtaining the features that capture the essential information of multiple image distortions [6]. Conventional BIQA methods utilizing hand-crafted features are based on platforms such as the human visual system HVS and natural scene statistics NSS. However, due to the higher extent of similarity among BIQA and human perception, compiling such hand-crafted features that can effectively capture and represent the image quality degradation levels becomes a tedious task [7]. Improper lighting, constraints put on imaging lenses, and limitations of sensors can induce distortions that are more complex in nature, and such cases in digital images usually occur in reality-based scenarios. The traditional quality assessment methods usually diverged towards artificially distorted images with distortions such as JPEG, gaussian noise, and blue, to name a few [8]. However, the bottleneck in these datasets is that they contain a limited quantity of images, which are less diverse in terms of distortions. Furthermore, as multiple distortions may exist in an image in reality-based cases, such artificially distorted images do not emulate the complex nature of distortions that can occur in real world images [9]. Traditional BIQA models extract low-level features from distorted images and predict the image quality by applying machine learning regression models [10, 11]. This way of applying NSS to represent image quality does not capture the local distortions in an image. A recent utilization of deep learning methods in IQA research prompted the use of convolutional neural networks CNN to extract image features in order to highlight degradation regions in an image. One major issue arises that deep learning based IQA methods are usually trained on smaller and artificially induced image datasets [12, 13].