Shao‐Yi Chien
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- Advanced Vision and Imaging 117
- Advanced Image and Video Retrieval Techniques 62
- Advanced Image Processing Techniques 49
- Image and Video Quality Assessment 35
- Advanced Data Compression Techniques 34
- Video Analysis and Summarization 27
- Signal Processing top 0.5%
- Video Coding and Compression Technologies 82
- Media Technology top 1%
- Hardware and Architecture top 5%
- Human-Computer Interaction top 5%
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- CCD and CMOS Imaging Sensors 24
Shao‐Yi Chien
254 papers receiving 3.8k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Computer Vision and Pattern Recognition 3.1k
- Signal Processing 1.3k
- Media Technology 365
- Hardware and Architecture 154
- Human-Computer Interaction 100
Countries citing papers authored by Shao‐Yi Chien
This map shows the geographic impact of Shao‐Yi Chien'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 Shao‐Yi Chien with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shao‐Yi Chien more than expected).
Fields of papers citing papers by Shao‐Yi Chien
This network shows the impact of papers produced by Shao‐Yi Chien. 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 Shao‐Yi Chien. The network helps show where Shao‐Yi Chien may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Shao‐Yi Chien, 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 | 2024 | 1 | |
| 2 | 2023 | 0 | |
| 3 | 2020 | 4 | |
| 4 | 2020 | 0 | |
| 5 | Supervised Joint Domain Learning for Vehicle Re-Identification | 2019 | 9 |
| 6 | Learning a Code-Space Predictor by Exploiting Intra-Image-Dependencies. | 2018 | 10 |
| 7 | Speech Dereverberation Based on Integrated Deep and Ensemble Learning. | 2018 | 1 |
| 8 | A Kernel Redundancy Removing Policy for Convolutional Neural Network. | 2017 | 1 |
| 9 | 2017 | 50 | |
| 10 | 2016 | 3 | |
| 11 | 2014 | 6 | |
| 12 | 2013 | 25 | |
| 13 | Hardware architecture design of hybrid distributed video coding with frame level coding mode selection | 2012 | 2 |
| 14 | 2012 | 9 | |
| 15 | 2011 | 2 | |
| 16 | 2011 | 2 | |
| 17 | 2008 | 7 | |
| 18 | Fast Algorithm and Architecture Design of Low-Power Integer Motion Estimation for H.264/AVC | 2007 | 1 |
| 19 | 2007 | 3 | |
| 20 | 2006 | 12 |
About Shao‐Yi Chien
Shao‐Yi Chien is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 268 papers that have together received 4.0k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (117 papers), Video Coding and Compression Technologies (82 papers), Advanced Image and Video Retrieval Techniques (62 papers), Advanced Image Processing Techniques (49 papers), Image and Video Quality Assessment (35 papers), Advanced Data Compression Techniques (34 papers), Video Analysis and Summarization (27 papers) and CCD and CMOS Imaging Sensors (24 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.1k citations), Signal Processing (1.3k citations) and Media Technology (365 citations). Shao‐Yi Chien has collaborated with scholars based in Taiwan, United States and Japan. Frequent co-authors include Liang‐Gee Chen, Yu‐Wen Huang, Shyh-Yih Ma, Tung-Chien Chen, Wei-Chih Tu, Ching-Yeh Chen, Tse-Wei Chen, Qingxiong Yang, Shengfeng He and Yi‐Ling Chen. Their work appears in journals such as IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing and IEEE Access.
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.