## 2 Background
The authors used the LIVE Image Quality Assessment Dataset [55], TID2008 [10] and the LIVE multiply distorted dataset [62]. Their results showed comparable results to other no-reference IQMs, but not as high correlation coefficients compared to the best full-reference metrics. [PERSON] et al. [48] apply a multi-scale Difference of Gaussian to generate features, which were processed in a deep neural network in their proposed IQM. It used a combination of a stacked auto-encoder with three hidden layers and a support vector machine regression. The IQM was evaluated on the TID2008 dataset [10] and the LIVE Image Quality Assessment Dataset [55]. The proposed no-reference IQM showed higher correlation coefficients compared to other state-of-the-art no-reference IQMs, and comparable correlation coefficients to state-of-the-art full-reference IQMs. [PERSON] et al. [44] introduced a no-reference IQM using CNNs for generic distortions, where quality scores (categories such as bad, poor, fair, good and excellent) are predicted for sub-regions within the image and support vector regression is applied on the CNN features. Their architecture is based on the Caffe network [63], but pre-trained on three image classification tasks. The authors experimented with selecting between 5 and 50 sub-regions randomly from the images. Evaluation was performed using the LIVE In the Wild dataset [64], and they showed higher correlation coefficients than state-of-the-art IQMs. They also evaluated their method on the LIVE Image Quality Assessment Dataset [55], Categorical Subjective Image Quality [57], TID2008 [10], TID2013 [65]. Their correlation coefficients were similar or higher compared to other metrics. [PERSON] et al. [66] merged CNNs and Prewitt magnitude on a segmented image to estimate the quality of images. The CNN model is based on seven layers, using normalized \(32\times 32\) pixel image patches as input. The authors computed weights for the image patches, which is based on a graph-based segmentation of the original image, where the weight is the sum after applying the Prewitt operator on the image. The IQM was evaluated on the TID2008 dataset [10] and the LIVE Image Quality Assessment Dataset [55]. The results show that the introduced IQM has higher correlation coefficients compared to no-reference IQMs, and similar to the best full-reference IQMs. [PERSON] et al. [47] utilized local quality maps as intermediate targets for CNNs. In the proposed IQM, the CNN is trained with respect to each non-overlapping patch in the image, also giving equal weights for every pixel in the image. This results in a local quality score. Further, the pooling stage is incorporated for training. All parameters of the model are optimized simultaneously. The CNN architecture consisted of two convolutional layers and five fully connected layers. The proposed IQM was evaluated on the TID2008 dataset [10] and the LIVE Image Quality Assessment Dataset [55], and the results showed that it is comparable to the best performing IQMs. [PERSON] et al. [49] introduced a full-reference IQM to measure the local similarities between the features from the distorted and reference images using deep neural networks. The reference image and the degraded image are fed separately to the VGGnet [67]. Further, the output of each layer is computed being the feature map. Then, local similarities between the feature map of the reference and the feature map of the degraded image are found. At last, the local similarities are pooled as a final quality score. They evaluated their method on the LIVE Image Quality Assessment Dataset [55], Categorical Subjective Image Quality [57], the LIVE multiply distorted dataset [62], and TID2013 [65]. The performance of the full-reference IQM was similar to that of the best state of the art IQMs. [PERSON] et al. [68] introduced a no-reference IQM. The first step was to identify the distortion of the input image, which was done using a shallow CNN with one convolution layer.