Kurtosis-Based Blind Noisy Image Quality Assessment in Wavelet Domain

Noise distortions introduced in natural images generally break the initial probability distributions by dispersing image pixels randomly. We found that there exists a big difference between the distributions of Discrete Wavelet Transform (DWT) coefficients of natural images and noisy images: (1) for natural images, their distributions are sharp with high peaked ness and slight tail, (2) for noisy images, the shapes are much flatter with lower peaked ness and heavier tail. Kurtosis is able to measure and differentiate the probability distributions of noisy images with various noise levels. Moreover, the kurtosis values of DWT coefficients are stable for varying frequency filters. In this paper, we propose a Blind Noisy Image Quality Assessment model using Kurtosis (BNIQAK). Five types of noisy images in the three biggest databases are taken for testing BNIQAK. Experimental results show that BNIQAK has better evaluation performance compared with existing blind noisy models, as well as some general blind and full-reference (FR) methods.