## 1 Introduction  
Image quality assessment is central in acquisition, processing, analysis and reproduction of images. The interest and need for image quality assessment has increased in the last decades, resulting in increasing research on this topic. Subjective assessment is even considered to be the \"gold standard\", but objective assessment is becoming increasingly popular. A plethora of objective assessment methods, commonly known as Image Quality Metrics (IQMs), have been suggested in the literature over the last decades [1, 2, 3, 4, 5, 6]. These metrics have also been considerably evaluated [7, 8, 9, 10, 11, 12]. Despite the large number of existing IQMs and their extensive evaluation, there are still several limitations and unsolved challenges [8, 13, 14, 15, 16].  
IQMs can, depending on the availability of the reference image, be divided into full-reference, reduced-reference, or no-reference [17]. Full-reference IQMs need the complete reference, while reduced-reference IQMs need partial information of the images, and no-reference IQMs do not need access to the reference image. Conventional IQMs only utilize information on the intensity of the distortion, such as mean-squared-error and peak-signal-to-noise-ratio (PSNR). In spite of this, these IQMs have been used with success in different applications, but they have only been moderately correlated with perceived quality for natural images [11]. IQMs based on structural similarity have become very popular in the last decade [18], and they showed to correlate better with subjective scores than PSNR [11]. There were also proposed many other IQMs based on different approaches, such as the spatial CIELAB [19], total variation of difference [20], PSNR-HVS-M [21], Difference of Gaussians [22], machine learning [23], spatial hue angle metric [24]. They have also incorporated different aspects related to the human visual system, such as contrast sensitivity [19], visual masking [25; 26], gaze information [27; 28]. These IQMs have been applied to a a wide range of applications, including color printing [29; 30; 31], displays [32], compression [33; 34], cameras [35], image enhancement [36], gamut mapping [37; 38], medical imaging [39; 40], and biometrics [41; 42; 43].  
Recently the use of deep learning has attracted the attention of many researchers in image quality [44; 45; 46; 47; 48; 49; 50; 51]. The distance from the image to the observers is an important aspect when observers evaluate quality [20; 52; 53]. This well-known fact was, however, overlooked in many of the existing IQMs, and very few of the IQMs based on deep learning incorporate viewing distance. In addition, the existing datasets for evaluation of the performance of IQMs were only carried out at a single viewing distance or the distance was not controlled (i.e., fixed). The Colourlab Image Database: Image Quality (CID:IQ) [52] is one of a handful publicly available datasets where observers evaluated quality of images at two different viewing distances, namely 50 cm and 100 cm.  
The main contributions of this work are:  
* The integration of the viewing distance on a modified version of the pre-trained VGG16 model.
* The integration of the saliency information to extract patches according to their importance.
* The comparison of our modified model with several configurations.
* Evaluation of the proposed method against other state-of-the-art methods on two datasets.  
We utilize a Convolutional Neural Network (CNN) to predict perceived image quality at different viewing distances. To the best of our knowledge, this is the first work where viewing distance is included in a CNN-based IQM. First, we will introduce related background, then we present the proposed method. Furthermore, we present our experimental results, and then the conclusion is given.