Actived Edge Strength for Image Quality Assessment

被引：0

作者：

Gao, Minjuan ^{[1
]}

Zhang, Xuande ^{[2
]}

机构：

[1] ShaanXi Univ Sci & Technol, Sch Elect & Control Engn, Xian, Peoples R China

[2] ShaanXi Univ Sci & Technol, Shool Elect Informaiton & Artif Intelligence, Xian, Peoples R China

来源：

2019 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS) | 2019年

关键词：

Image quality assessment; active function; edge strength; SIMILARITY;

D O I：

10.1109/ispacs48206.2019.8986261

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

These years has witnessed the success of deep learning methods in computer vision. The approximation capabilities of neural networks is partly responsible for these success, and active function is crucial for the approximation capability. Motivated by the success of deep learning, this paper presents an Actived Edge Strength Similarity (AESSIM) based image quality assessment algorithm. Numerical experiments on the public datasets indicates that AESSIM is quite competitive in assessing performance.

引用

页数：2

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