Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images-Reference-Cited by-同舟云学术

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Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images

Published:2017-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 25th ACM international conference on Multimedia
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Author:

Li Dingquan¹,Jiang Tingting¹,Jiang Ming¹

Affiliation:

1. National Engineering Lab for Video Technology & Peking University, Beijing, China

Funder

Natural Science Foundation of China

National Basic Research Program of China (973 Program)

Sino-German Center

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3123266.3123322

Reference47 articles.

1. A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation

2. Efficient Image Sharpness Assessment Based on Content Aware Total Variation

3. Sebastian Bosse Dominique Maniry Thomas Wiegand and Wojciech Samek. 2016. A deep neural network for image quality assessment IEEE International Conference on Image Processing. 3773--3777. Sebastian Bosse Dominique Maniry Thomas Wiegand and Wojciech Samek. 2016. A deep neural network for image quality assessment IEEE International Conference on Image Processing. 3773--3777.

4. No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers

5. A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

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