## 1 Introduction
Therefore, both of these approaches depend on the reference images for image quality assessment. In NR-IQA, there is no dependence on the reference image in the process of assessing the image quality, and due to this, NR-IQA is also called blind image quality assessment (BIQA). A brief comparative review of the FR-IQA and NR-IQA methods from the literature shows that in FR-IQA, the conventional methods are the structural similarity index (SSIM) and feature similarity index (FSIM) [5]. However, when NR-IQA is considered, this quality assessment approach has a vital part which is termed feature extraction. Feature extraction (FE) in NR-IQA is split into a few types, which are handcrafted FE (HC-FE), using deep neural networks (DNNs) and using convolutional neural networks (CNNs). In the HC-FE feature extraction approach, the features are mainly extracted by using either of the two methods, which are the human visual system (HVS) or by utilizing natural scene statistics (NSS) [1,10,11]. The feature extraction part is considered a core of IQA, as it defines the robustness and accuracy of the image quality assessment method. The human visual system (HVS) extracts the structural information from an image, while in the case of NSS, its basis is that the scenes have statistical characteristics, and these characteristics are affected by the type and level of distortion present in an image. The HVS and NSS are not robust enough to accurately predict the image quality of a distorted image [1]. In this situation, CNNs and DNNs are better options for IQA. CNNs and DNNs are widely used in various other fields [12, 13, 14, 15, 16], and similarly, they can be utilized in IQA to predict the quality of distorted images. Many CNN-based techniques are proposed in the literature [17,18]. A CNN is used for feature extraction, and further down the line, regression is used to build a model which can predict the image quality [1]. IQA algorithms that are based on CNNs are based on a special network structure (SNS). The depth of the networks determines how much detailed information can be captured, which is further utilized to predict the quality of distorted images [19]. As the depth of the network increases, more image information can be recorded to be used for quality assessment. The CNN-based IQA approach has higher accuracy for predicting the quality of distorted images. Deep neural networks for IQA are divided into two categories, which are the DNN feature extraction approach and the DNN end-to-end approach for IQA. The DNN feature extraction approach for IQA uses deep neural networks to extract featuresfrom distorted images and then utilizes support vector regression to obtain quality scores for distorted images [5]. On the other hand, the DNN end-to-end IQA approach directly obtains and predicts the quality scores of distorted images just by using an input image whose quality is to be predicted or by using an input image patch [20; 21]. Image quality assessment has a drawback, as IQA data are in a very limited amount, and this DNN end-to-end approach is a data-extensive method. This puts a limitation on this approach, but various solutions are provided in the literature for this situation. The biggest solution is to use the pretrained models and fine-tune them to achieve better performance. In this work, we have proposed a framework with a visual saliency module that helps to extract only those regions in the image which are important to predicting the quality of the image. We have used the static fine-grained visual saliency technique as images have no motion involved, and therefore, we intend to use a technique that requires less computational resources. The proposed framework is designed to be used in real time to evaluate the images acquired from the drone while engaged in surveillance. However, it is important that the framework has to be fine-tuned on drone images once the dataset is prepared.