Yun-Qing Shi
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- Advanced Steganography and Watermarking Techniques 90
- Digital Media Forensic Detection 88
- Chaos-based Image/Signal Encryption 46
- Generative Adversarial Networks and Image Synthesis 18
- Advanced Data Compression Techniques 9
- Advanced Image Processing Techniques 7
- Media Technology top 0.5%
- Image Processing Techniques and Applications 19
- Signal Processing top 1%
- Video Coding and Compression Technologies 10
- Artificial Intelligence top 2%
Yun-Qing Shi
120 papers receiving 5.4k citations
Hit Papers
Peers
Comparison fields: 5 of 112
- Computer Vision and Pattern Recognition 5.1k
- Media Technology 481
- Signal Processing 502
- Computer Graphics and Computer-Aided Design 82
- Artificial Intelligence 666
Countries citing papers authored by Yun-Qing Shi
This map shows the geographic impact of Yun-Qing Shi'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 Yun-Qing Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun-Qing Shi more than expected).
Fields of papers citing papers by Yun-Qing Shi
This network shows the impact of papers produced by Yun-Qing Shi. 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 Yun-Qing Shi. The network helps show where Yun-Qing Shi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yun-Qing Shi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 8 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 0 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 12 | |
| 8 | 2022 | 18 | |
| 9 | 2022 | 17 | |
| 10 | 2021 | 85 | |
| 11 | 2021 | 68 | |
| 12 | 2021 | 11 | |
| 13 | 2021 | 38 | |
| 14 | 2020 | 7 | |
| 15 | 2020 | 24 | |
| 16 | 2019 | 52 | |
| 17 | 2019 | 130 | |
| 18 | 2018 | 57 | |
| 19 | 2018 | 68 | |
| 20 | 2018 | 32 |
About Yun-Qing Shi
Yun-Qing Shi is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 128 papers that have together received 5.6k indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (90 papers), Digital Media Forensic Detection (88 papers), Chaos-based Image/Signal Encryption (46 papers), Image Processing Techniques and Applications (19 papers), Generative Adversarial Networks and Image Synthesis (18 papers), Video Coding and Compression Technologies (10 papers), Advanced Data Compression Techniques (9 papers) and Advanced Image Processing Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (5.1k citations), Media Technology (481 citations) and Signal Processing (502 citations). Yun-Qing Shi has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Bin Ma, Jiwu Huang, Hanzhou Wu, Xiaolong Li, Guanshuo Xu, Jiangqun Ni, Hao‐Tian Wu, Chunpeng Wang, Xingyuan Wang and Fangjun Huang. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, IEEE Access, Computers, materials & continua/Computers, materials & continua (Print), IEEE Transactions on Information Forensics and Security and IEEE Transactions on Multimedia.
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.