No-reference noisy image quality assessment incorporating features of entropy, gradient, and kurtosis  

作　　者：Heng YAO Ben MA Mian ZOU Dong XU Jincao YAO 

机构地区：[1]School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China [2]School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China [3]Cancer Hospital of the University of Chinese Academy of Sciences(Zhejiang Cancer Hospital),Hangzhou 310000,China [4]Institute of Basic Medicine and Cancer,Chinese Academy of Sciences,Hangzhou 310000,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2021年第12期1565-1582,共18页信息与电子工程前沿（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(No.61702332);the Zhejiang Provincial Natural Science Foundation of China(Nos.LZY21F030001 and LSD19H180001)。

摘　　要：Noise is the most common type of image distortion affecting human visual perception.In this paper,we propose a no-reference image quality assessment(IQA)method for noisy images incorporating the features of entropy,gradient,and kurtosis.Specifically,image noise estimation is conducted in the discrete cosine transform domain based on skewness invariance.In the principal component analysis domain,kurtosis feature is obtained by statistically counting the significant differences between images with and without noise.In addition,both the consistency between the entropy and kurtosis features and the subjective scores are improved by combining them with the gradient coefficient.Support vector regression is applied to map all extracted features into an integrated scoring system.The proposed method is evaluated in three mainstream databases(i.e.,LIVE,TID2013,and CSIQ),and the results demonstrate the superiority of the proposed method according to the Pearson linear correlation coefficient which is the most significant indicator in IQA.

关 键 词：Noisy image quality assessment Noise estimation KURTOSIS Human visual system Support vector regression 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]