A No-Reference Image Quality Assessment Metric by Multiple Characteristics of Light Fight Images

被引：44
|
作者：
Shan, Liang [2 ]
An, Ping [1 ,2 ]
Meng, Chunli [2 ]
Huang, Xinpeng [2 ]
Yang, Chao [2 ]
Shen, Liquan [2 ]
机构：
[1] Shanghai Univ, Key Lab Adv Display & Syst Applicat, Minist Educ, Shanghai 200072, Peoples R China
[2] Shanghai Univ, Sch Commun & Informat Engn, Shanghai Inst Adv Commun & Data Sci, Shanghai 200444, Peoples R China
来源：
IEEE ACCESS | 2019年 / 7卷
基金：
中国国家自然科学基金;
关键词：
Light field images; subjective quality assessment; objective quality assessment; light field characteristics; SVR; PERCEPTUAL QUALITY; INDEX;
D O I：
10.1109/ACCESS.2019.2940093
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Evaluation of light field image (LFI), especially micro-lens camera light field (LF), is a new and challenging work. The development of image quality assessment (IQA) metric of LFIs relies on the subjective quality assessment database. In this paper, we establish a perceptual quality assessment dataset consisting of 240 distorted images from 8 source images with five distortion types. Furthermore, a no-reference IQA metric is proposed by combining 2D and 3D characteristics of LFI with the Support Vector Regression (SVR) model. The performance of the proposed metric is demonstrated by comparing with some classical full reference IQA metrics both on the presented dataset and a third-party dataset. The experiment results show that our method has a better performance than others.
引用
收藏
页码：127217 / 127229
页数：13
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