## 2 Background
Further, for every distortion type they designed a CNN, which is used to calculate a quality score for each patch in the image. At last, a fusion algorithm was used to generate one single quality score for the entire image. Evaluation was carried out on the LIVE Image Quality Assessment Dataset [55] and Categorical Subjective Image Quality [57] dataset. Performance of the introduced no-reference IQM was comparable to state of the art IQMs, but the correlation coefficients were slightly lower than the best full-reference IQMs. [PERSON] et al. [69] proposed a no-reference IQM, in which they classify the distortions present in the degraded image. For each distortion class they compute a quality score. These are further combined through a weighted average-pooling algorithm to obtain a single regressor output. The IQM was evaluated on LIVE Image Quality Assessment Dataset [55], Categorical Subjective Image Quality [57] and the TID2008 dataset [10]. The evaluation showed comparable results to other state of the art IQMs. This approach can be is similar to that of [54,56]. [PERSON] [70] introduced no-reference IQM using multi-level inception features from a pretrained CNN. The method uses the entire to extract image resolution independent features. The IQM was evaluated on the LIVE in the wild dataset [64], and obtained higher correlation values compared to many state of the art methods. [PERSON] et al. [71] proposed a no-reference IQM mimicking the mimicking the human visual system, more precisely by using an active inference module of a generative adversarial network to predict the main content of the image. Then by using a multi-stream convolutional neural network (CNN) they assess the quality related to scene information, distortion type and content degradation. The proposed IQM was evaluated on LIVE Image Quality Assessment Dataset [55], Categorical Subjective Image Quality [57], TID2013 [65], LIVE In the Wild dataset [64] and LIVE multiply distorted dataset [62]. The method showed comparable or higher correlation values compared to other state of the art IQMs. [PERSON] et al. [51] introduced a full-reference IQM using self-similarity and a CNN model. It used CNN features across multiple levels to calculate the similarity between the reference image and the degraded image. The IQM was based on the Alexnet [72] architecture. The method extracts feature maps at five convolutional layers, and these are compared using a histogram-based quality metric. A quality value at each layer is computed, and further pooled using a geometrical mean to get a final quality value. The proposed IQM was evaluated on the LIVE Image Quality Assessment Dataset [55], Categorical Subjective Image Quality [57], the Colourlab Image Quality Dataset [52], and TID2013 [65]. The results showed that the proposed IQM gave similar performance to the best state-of-the-art IQMs. The same IQM was also evaluated on a dataset for image contrast enhancement evaluation [73], where it also performed quite well [36]. The approach by [PERSON] et al. [51] was improved in [74] where the feature maps were compared using traditional IQMs such as SSIM [18], PSNR and mean squared error. The proposed IQM was evaluated on the LIVE Image Quality Assessment Dataset [55], Categorical Subjective Image Quality [57], the Colourlab Image Quality Dataset [52], and TID2013 [65]. They showed an improvement in performance of the IQMs (on average an increase of 23%) using a CNN based approach. In this study, we advance the current research compared to the existing CNN-based IQMs by predicting image quality without a reference for different viewing distances. To achieve this, relevant patches were selected based on saliency information and the viewing distance was included to features extracted from a modified pre-trained CNN model.