NR-IQA algorithms can be classified into learning-free and learning-based categories. As the name indicates, learning-based methods rely on various machine and/or deep learning techniques to construct a model for perceptual quality estimation. Learning-free methods can be further divided into two groups, i.e., spatial [26] and spectral domain [27] based approaches. A common method [28] for score prediction involves fitting a portion of the training **data** to the joint distribution of the feature vector and the related opinion scores. Given the test **data** feature vector, the score prediction in this instance entails maximizing the likelihood of the test **data** opinion score. Other methods [29] that are both opinion-and distortion-unaware measure the separation in sparse feature space between the reference and distorted images. In contrast, Leonardi et al. [30] elaborated an opinion-unaware method that exploits the activation maps of pretrained convolutional neural networks [31] by considering the correlations between feature maps.