## 2 Materials and Methods
### Brief Overview of the No-Reference Image Quality Assessment Methods  
The no-reference objective IQA metrics are particularly interesting for many applications where \"pristine\" reference images without any distortions are unavailable. However, due to the lack of possibility of comparisons of the distorted images with them, each NR method should not only determine the amount and/or type of distortions in the way as highly correlated with their subjective perception as possible. Typically, such methods should also be able to detect the presence of some types of distortions without the knowledge of the original reference image. These requirements cause much lower universality of the NR metrics in comparison to the full-reference IQA methods as well as their significantly lower correlation with subjective quality evaluation results available in the IQA datasets, such as LIVE or TID2013. Therefore, many various general-purpose or more specialized NR metrics have been proposed by various researchers, and some of them are briefly presented below. Some of the most widely known NR metrics for natural images, currently implemented i.a. in MATLAB environment are Naturalness Image Quality Evaluator (NIQE) [26], Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) [36], and Perception-based Image Quality Evaluator (PIQE) [37]. The first one is based on the measurement of the distances between the image features utilizing the natural scene statistics (NSS) for the assessed image and the same features obtained from an image database during model training with the use of a multivariate Gaussian model. Its extension, known as IL-NIQE [25], utilizes such models calculated for each image patch and the overall quality score is obtained by average pooling. Another, relatively well-known NR metric based on the NSS is the Blind Image Quality Index (BIQI) [38], being a two-step framework composed of 5 sub-indexes sensitive to distortion types present in the LIVE database, utilizing wavelet decomposition, generalized Gaussian distribution (GGD) and a classifier based on the support vector machine (SVM). The BLIINDS-2 [39] metric utilizes the NSS model of the discrete cosine transform (DCT) coefficients a simple Bayesian inference approach for quality prediction, whereas Distortion Identification-based Image Verity and Integrity Evaluation (DIIVINE) [40] does not use any distortion-specific models to extend its universality. An interesting general-purpose NR objective metric, known as the COdebook Representation for No-reference Image Assessment (CORNIA), has been proposed by [PERSON] et al. [41] utilizing unsupervised feature learning. Instead of the handcrafted features, raw-image-patches extracted from a set of unlabeled images have been used as local descriptors. [PERSON] et al. [42] have proposed a quality-aware clustering (QAC) method to learn a set of centroids for each quality level without the necessity of using the images scored by a human in learning. Some examples of more specialized metrics, e.g., for sharpness evaluation might be ARISM [43] based on the autoregressive model parameters, Cumulative Probability of Blur Detection (CPBDM) [20], JNBM [19] based on the idea of just noticeable blur (JNB), or the blur metric proposed by [PERSON] et al. [44]. Some other examples may be the wavelet-based Fast Image SHarpness (FISH) [45] metric, as well as Perceptual Sharpness Index (PSI) [46]. A specialized metric designed to measure blocking effects and relative blur is known as WNJE [47], and an exemplary metric designed for blur assessment based on discrete orthogonal moments is known as BIBLE [48]. The use of High Order Statistics Aggregation (HOSA) has been proposed by [PERSON] et al. [49], whereas [PERSON] et al. proposed the BPRI metric [50] based on the use of pseudo-reference image, consisting of three sub-indexes: PSS blockiness measure, LSSs sharpness measure, and LSSn noisiness measure.