## 2 Background  
There is a large number of IQMs in the literature [1; 2; 3; 4; 5], and many different approaches have been taken. In recent years, more and more IQMs based on deep learning have been proposed, and the use of deep learning has been postulated to result in better performing IQMs [15]. [PERSON] et al. [54] handled image quality as a classifying problem through linear discriminant analysis. The authors first extract characterizing features from both the original and degraded images. Then, the type of degradation in the image is found by using a minimum distance criterion. The most appropriate IQM for a given distortion is finally applied. Evaluation was carried out on the TID2008 dataset [10] and the LIVE Image Quality Assessment Dataset [55]. The results showed that the suggested method is improving the correlation coefficients, but that the improvement is distortion dependent. In [56], [PERSON] extended the previous work by [PERSON] et al. [54] through a CNN model for identifying the degradation and prediction of quality. The degraded image goes through two parallel processes, where the distortion type is identified using a CNN model in one process and for the other the most salient patches are found. The quality estimation is also done through a CNN model with two convolutional layers, two pooling steps, one fully connected layer and one output layer. Evaluation was carried out on the TID2008 dataset [10], Categorical Subjective Image Quality [57], and the LIVE Image Quality Assessment Dataset [55], using the following distortion types: white noise, JPEG, JPEG2000, blur and fast fading. The results indicated that the proposed method gave higher correlation coefficients compared to other no-reference IQMs, and comparable results of the best full-reference IQMs. [PERSON] [58] used the pre-trained VGG16 model in a no-reference IQM. A patch selection step based on saliency information using a scanpath predictor was incorporated in the IQM. Patches of the fixation points from the scanpath predictor was used as input to the CNN model. The IQM was evaluated on the CU-Nantes dataset [59]. The results showed that the proposed CNN model had the highest quality performance. [PERSON] et al. [50] suggested a no-reference IQM based on a discriminative deep learning model that was trained to classify natural scene statistics features in five quality levels (excellent, good, fair, poor, and bad). The final predicted quality score was obtained from a quality pooling step. The proposed IQM was evaluated on the LIVE Image Quality Assessment Dataset [55], TID2008 [10], Categorical Subjective Image Quality [57], IVC [60], and MICT [61]. [PERSON] et al. [45] introduced a no-reference metric that predicted image quality of patches in images using CNNs. They contrast normalize the grayscale image, before selecting non-overlapping patches, where each patch is inputted to the network. The network consisted of five layers, where the first convolutional layers filtered the input with 50 kernels. 50 feature maps are created, which are pooled into one max and one min. Further, two fully connected layers of 800 nodes are used. The final layer is a linear regression, giving the final quality score. The no-reference IQM was evaluated on the LIVE Image Quality Assessment Dataset [55] and the TID2008 dataset [10]. The proposed IQM showed an overall correlation higher than other no-reference metrics in the evaluation. [PERSON] et al. [46] extracted simple features from images by using a Shearlet transform, and then further treated image quality as a classification problem using deep neural networks. The first step of extracting features were based on that the statistics of the Shearlet coefficients changed as an image were distorted. The features are extracted from each of the color channels (RGB) and normalized, these features are then evolved in stacked auto-encoders before the final features are inputted to a Softmax classifier.