Recently, deep neural networks, particularly convolutional neural networks (CNN) have gained a significant amount of attention in the literature due to their improved performance in many fields [43][44][45][46] compared to other approaches and paradigms. In NR-IQA, Kang et al. [47] applied first a CNN successfully. Namely, the authors implemented a traditional CNN which accepts image patches of 32 × 32 and predicts the patches' quality independently from each other. The entire image's perceptual quality was obtained by taking the arithmetic mean of the patches' quality scores. Similar to [47], Kim and Lee [48] trained a CNN on image patches but the patches' desired quality scores were determined by a traditional FR-IQA metric which restricts this method to the evaluation of artificially distorted images. Bare et al. [49] developed a network that operates on image patches similar to [47] but the patches target score is calculated from a traditional FR-IQA metric (feature similarity index [50]) similar to [48]. On the whole, the entire image's perceptual quality is estimated by the predicted feature similarity index [50] scores of the image patches. In contrast, Conde et al. [51] took a CNN backbone network and trained it using a loss function [52] which aims to minimize the mean squared error and maximize linear correlation coefficient between the predicted and ground-truth quality scores. Further, the authors applied several **data** augmentation techniques, such as horizontal flips, vertical flips, rotations, and random cropping. To handle images with different aspect ratios, Ke et al. [53] introduced a transformer [54] based NR-IQA model which applied a hashbased 2D absolute-position-encoding for embedding image patches extracted from multiple scales. In contrast, Zhu et al. [55] embedded the input images' original aspect ratios into the self-attention module of a swin transformer [56]. Sun et al. [57] introduced the distortion graph representation framework which contains a distortion type discrimination network aiming to discriminate between distortion types and a fuzzy prediction network for perceptual quality estimation. Liu et al. [58] introduced lifelong learning for NR-IQA to learn new distortion types without accessing to previous training data. First, the authors utilized a split-and-merge distillation strategy for compiling a single-head regression network. In the split phase, a distortion-specific generator was implemented for generating pseudo-features for unseen distortions. In the merge phase, these pseudo-features were coupled with pseudo-labels to distill knowledge about distortions.