Nonparametric Quality Assessment Of Natural Images

In this article,the authors explore an alternative way to perform no-reference image quality assessment (NR-IQA). Following a feature extraction stage in which spatial domain statistics are utilized as features,a two-stage nonparametric NR-IQA framework is proposed.This approach requires no training phase,and it enables prediction of the image distortion type as well as local regions' quality, which is not available in most current algorithms. Experimental results on IQA databases show that the proposed framework achieves high correlation to human perception of image quality and delivers competitive performance to state-of-the-art NR-IQA algorithms

Nonparametric Quality Assessment of Natural Images

In this article, the authors explore an alternative way to perform no-reference image quality assessment (NR-IQA). Following a feature extraction stage in which spatial domain statistics are utilized as features, a two-stage nonparametric NR-IQA framework is proposed. This approach requires no training phase, and it enables prediction of the image distortion type as well as local regions' quality, which is not available in most current algorithms. Experimental results on IQA databases show that the proposed framework achieves high correlation to human perception of image quality and delivers competitive performance to state-of-the-art NR-IQA algorithms