No reference image quality assessment using sparse feature representation in two dimensions spatial correlation

Universal no reference image quality assessment is attracting significant attention in the fields of image processing, visual and machine learning. This study presents a novel method to evaluate the image quality from human subjective scores of the training samples by regressing. The primary characteristic of this novel method is the learning dictionary derived from extracting features of two dimensional spatial correlations of the sample images. Each atom in the dictionary includes 10 elements. They are DMOS (differential mean opinion score), three extracted features and their corresponding image structure patches and PCMs (pixel correlative matrix). The three extracted features are the standard deviation, the gray scale deviation and the distribution width. During the quality assessment, a distorted image is transformed into an image with structural information and partitioned into patches. The patch with the largest feature value is selected and represented sparsely in the learning dictionary. Afterwards the image quality index is obtained by quantifying the sparse representation coefficients, DMOS values and feature values. Comparing with other image quality assessment models, the proposed NSRCIQ method is simple and effective. The resulted IQA scores have not only comparable accuracy, but also high linearity to human perception of image quality. Moreover, the algorithm can be implemented in real-time.