TY - GEN
T1 - Kurtosis-Based Blind Noisy Image Quality Assessment in Wavelet Domain
AU - Wang, Shuigen
AU - Deng, Chenwei
AU - Li, Cheng
AU - Liu, Xun
AU - Zhao, Baojun
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/12
Y1 - 2016/1/12
N2 - 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.
AB - 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.
KW - Blind Noisy Image Quality Assessment
KW - Discrete Wavelet Transform
KW - Kurtosis
UR - http://www.scopus.com/inward/record.url?scp=84964411855&partnerID=8YFLogxK
U2 - 10.1109/SMC.2015.275
DO - 10.1109/SMC.2015.275
M3 - Conference contribution
AN - SCOPUS:84964411855
T3 - Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
SP - 1557
EP - 1560
BT - Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Y2 - 9 October 2015 through 12 October 2015
ER -