No-Reference Image Quality Assessment Method Based on Visual Parameters  

作　　者：Yu-Hong Liu Kai-Fu Yang Hong-Mei Yan 

机构地区：[1]Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China [2]Department of Biomedical Engineering, Chengdu Medical College, Chengdu 610050, China

出　　处：《Journal of Electronic Science and Technology》2019年第2期171-184,共14页电子科技学刊（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grants No.61773094,No.61573080,No.91420105,and No.61375115;National Program on Key Basic Research Project(973 Program)under Grant No.2013CB329401;National High-Tech R&D Program of China(863 Program)under Grant No.2015AA020505;Sichuan Province Science and Technology Project under Grants No.2015SZ0141 and No.2018ZA0138

摘　　要：Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.Recent studies on no-reference image quality assessment(NR-IQA)methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples.This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning.We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image.In this paper,the definitions,computational method,and relationships among these visual metrics were described.We subsequently proposed a no-reference assessment function,which was referred to as a visual parameter measurement index(VPMI),based on the integration of these visual metrics to assess image quality.It is established that the maximum of VPMI corresponds to the best quality of the color image.We verified this method using the popular assessment database—image quality assessment database(LIVE),and the results indicated that the proposed method matched better with the subjective assessment of human vision.Compared with other image quality assessment models,it is highly competitive.VPMI has low computational complexity,which makes it promising to implement in real-time image assessment systems.

关 键 词：BANDWIDTH human VISUAL system information entropy LUMINANCE NO-REFERENCE image QUALITY assessment (NR-IQA) VISUAL parameter measurement index (VPMI) 

分 类 号：TN[电子电信]