Unifying Pictorial and Textual Features for Screen Content Image Quality Evaluation-Reference-Cited by-同舟云学术

Unifying Pictorial and Textual Features for Screen Content Image Quality Evaluation

Published:2024-05-30 Issue: Volume:11874 Page:1099-1103
ISSN:
Container-title:Proceedings of the 2024 International Conference on Multimedia Retrieval
language:
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Author:

Chen Yihua¹^ORCID,Liang Xiaoping²^ORCID,Yu Mengzhu¹^ORCID,Tang Zhenjun¹^ORCID

Affiliation:

1. Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China

2. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, China

Funder

Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing

Guangxi Talent Highland Project of Big Data Intelligence and Application

Innovation Project of Guangxi Graduate Education

Guangxi Natural Science Foundation

Guangxi ?Bagui Scholar? Team for Innovation and Research

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652583.3657610

Reference30 articles.

1. Naturalization Module in Neural Networks for Screen Content Image Quality Assessment

2. Dual-Feature Aggregation Network for No-Reference Image Quality Assessment

3. Blind Quality Assessment for Cartoon Images

4. Zhiyuan Chen, Yihua Chen, Xiaoping Liang, and Zhenjun Tang. 2022. Multi-level feature aggregaton network for full-reference image quality assessment. In Proceedings of the 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2022). 861--866.

5. A Fast No-Reference Screen Content Image Quality Prediction Using Convolutional Neural Networks