Blind Noisy Image Quality Assessment Using Spatial, Frequency and Wavelet Statistical Features

Abstract

In this paper, we propose a blind noisy image quality estimation method of simultaneously utilizing three statistical features extracted from three different domains of the input noisy image. The statistical features used in this paper are (i) eigen-based variance by the covariance matrix of image blocks in the spatial domain, (ii) the spectral entropy of the power spectrum in the frequency domain, and the standard deviation in the wavelet domain. The extracted statistical features are fed into an extreme learning machine algorithm for mapping into perceptual quality scores. The model is trained and tested on images with six common noise distortion types commonly occurring in real-world applications: additive white Gaussian noise, additive Gaussian noise in color component, high-frequency noise, masked noise, impulse noise, and multiplicative noise. For the CSIQ, TID2008, TID2013, and KADID10k databases, the experimental results show that our method covers noise distortions wider than those of the conventional methods and achieves consistently better performance for blind noisy image quality assessment.