## 1 Introduction  
Various types of multimedia content have become an integral part of consumers' daily lives due to such an enormous rise in technology. Multiple distortions can be induced during the pipeline of multimedia generation to the utilization phase. Consequently, the constraints of technology degrade digital images. Social media content and captured images of natural scenes are both among the widely utilized content. The pipeline involves acquiring and processing the images, along with compressing and transmitting, and the final step involves the storage of digital images. This flow is induced with various types of distortions which consequently affect the quality of images and other multimedia content [1]. The visual quality of digital images deteriorates due to multiple effects of distortion, which is sub-categorized into three types, i.e., various distortions grouped can reduce the image quality; a unique single distortion can degrade the image and a scenario where the existing distortion is unidentified can occur in the images which are captured by imaging cameras [2]. The optimum quality of multimedia content is a key aspect for measuring user satisfaction [3]. The high utilization of social media content and images has given valuable significance to image quality assessment (IQA) [4]. The assessment of image quality is subdivided into two broader classes--objective IQA and subjective IQA. Subjective IQA deduces the quality of distorted images based on the observations of humans. The reason behind this is that humans are the end users of multimedia content and images. Due to this reason, the subjective IQA is set as a milestone in assessing image quality. It is considered a irresome and time-consuming task for evaluating image quality. Other than that, human prejudice can affect the image quality evaluation process [5]. Its counterpart, Objective IQA, assesses the image quality based on mathematical models and evaluation metrics. Objective IQA is further categorizedinto full-reference-IQA (FR-IQA), reduced-reference-IQA (RR-IQA) and no-reference-IQA (NR-IQA), also termed IQA (BIQA) [6]. When analyzing the quality of a distorted picture in FR-IQA, a reference image is used. A reference image is a distortion-free or pristine version of a distorted image. While evaluating picture quality with RR-IQA, only specific information about the pristine image is accessible. In NR-IQA, no information about the immaculate image is used to determine the quality of the damaged image [7; 8]. A method termed as region of interest or as visual saliency can be deployed to determine the locations or regions in an image that are more prominent for human eyes. Visual saliency establishes which regions of a picture draw the observer's attention. Image quality evaluation and visual saliency go hand in hand, since a distortion in an image affects the saliency map. Image quality assessment and visual saliency go hand in hand as the saliency map gets affected if there is a distortion present in an image [9]. It can be deployed to obtain the quality of an ROI in an image, consequently determining the significance of a region of an image in patch-based IQA approaches [10]. A distortion occurring in an ROI in an image will be more prominent and attractive to observers; this base theory illustrates that visual saliency is a key aspect of IQA. IQA metrics should incorporate visual saliency as a prominent metric for evaluating image quality as the entire quality of the image can be depicted by only the perceived image quality of the region of interest in an image [11]. Traditional blind image quality assessment approaches were designed for specific distortions by embedding the information about the distortions to evaluate image quality [12; 13; 14; 15]. More recent approaches have been designed based on the content of the images, i.e., images of natural scenes. Methods such as natural scene statistics (NSS)-based approaches assess the image quality irrespective of the type of distortion present in an image [16].