Tsung-Jung Liu

1.6k citations
56 papers · 1.2k indexed · h-index 19

Tsung-Jung Liu

50 papers receiving 1.2k citations

Peers

Tsung-Jung Liu
Comparison fields: 5 of 82
  • Computer Vision and Pattern Recognition 1.0k
  • Media Technology 397
  • Signal Processing 93
  • Industrial and Manufacturing Engineering 70
  • Urban Studies 20
Replace Zhengfang Duanmu with:
Zhengfang Duanmu Canada
Anil Singh Parihar India
Michele A. Saad United States
Junru Wu United States
Songnan Li Hong Kong
Jari Korhonen Denmark
Ruxin Wang China
Weixia Zhang China
Zhangkai Ni China
Xiangyu Xu China
Tsung-Jung Liu relative to Zhengfang Duanmu Canada Zhengfang Duanmu's profile →
Citations per field
00.5×10×15×20×24×
Zhengfang Duanmu · 1×
Citations per year

Countries citing papers authored by Tsung-Jung Liu

Since Specialization
Citations

This map shows the geographic impact of Tsung-Jung Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tsung-Jung Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tsung-Jung Liu more than expected).

Fields of papers citing papers by Tsung-Jung Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tsung-Jung Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tsung-Jung Liu. The network helps show where Tsung-Jung Liu may publish in the future.

Co-authorship network

The 16 scholars most cited alongside Tsung-Jung Liu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tsung-Jung Liu Line = papers co-authored together Tsung-Jung Liu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20240
3 20240
4 20240
5 20240
6 20232
7 20233
8 202264
9 2022119
10 202213
11 20211
12 201925
13 201917
14 201930
15 201927
16 20197
17 201582
18
Performance comparison of decision fusion strategies in BMMF-based image quality assessment
20122
19
A fusion approach to video quality assessment based on temporal decomposition
20125
20 2012149

About Tsung-Jung Liu

Tsung-Jung Liu is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Signal Processing, Urban Studies and Instrumentation, having authored 56 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (22 papers), Image and Video Quality Assessment (17 papers), Advanced Image Fusion Techniques (16 papers), Generative Adversarial Networks and Image Synthesis (16 papers), Face recognition and analysis (16 papers), Image and Signal Denoising Methods (11 papers), Image Enhancement Techniques (10 papers) and Video Surveillance and Tracking Methods (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.0k citations), Media Technology (397 citations), Signal Processing (93 citations), Industrial and Manufacturing Engineering (70 citations) and Urban Studies (20 citations). Tsung-Jung Liu has collaborated with scholars based in Taiwan, United States and Singapore. Frequent co-authors include Kuan-Hsien Liu, C.‐C. Jay Kuo, Weisi Lin, Hsin‐Hua Liu, Soo‐Chang Pei, Joe Yuchieh Lin, Yu-Chieh Lin, Haiqiang Wang, Chi-Hao Wu and Rui Song. Their work appears in journals such as IEEE Access, IEEE Transactions on Image Processing, Journal of Visual Communication and Image Representation, Applied Sciences and IEEE Transactions on Neural Networks and Learning Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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