C.‐C. Jay Kuo

36.2k citations
1.2k papers · 23.6k indexed · 7 hit papers · h-index 69

C.‐C. Jay Kuo

1.1k papers receiving 22.2k citations

Hit Papers

Understanding convolution...2761993202620042015250500750

Peers

C.‐C. Jay Kuo
Comparison fields: 5 of 210
  • Computer Vision and Pattern Recognition 13.1k
  • Signal Processing 5.6k
  • Computer Graphics and Computer-Aided Design 1.1k
  • Media Technology 2.3k
  • Computer Networks and Communications 4.2k
Replace Martin Ester with:
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C.‐C. Jay Kuo relative to Martin Ester Canada Martin Ester's profile →
Citations per field
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Citations per year

Countries citing papers authored by C.‐C. Jay Kuo

Since Specialization
Citations

This map shows the geographic impact of C.‐C. Jay Kuo'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 C.‐C. Jay Kuo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C.‐C. Jay Kuo more than expected).

Fields of papers citing papers by C.‐C. Jay Kuo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C.‐C. Jay Kuo. 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 C.‐C. Jay Kuo. The network helps show where C.‐C. Jay Kuo may publish in the future.

Co-authorship network

The 25 scholars most cited alongside C.‐C. Jay Kuo, 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 C.‐C. Jay Kuo Line = papers co-authored together C.‐C. Jay Kuo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20242
2 20240
3 20240
4 20232
5 20234
6 202229
7 20228
8 202144
9 20219
10 2019105
11 2017120
12 201535
13
Performance comparison of decision fusion strategies in BMMF-based image quality assessment
20122
14 201111
15 2006106
16 20067
17 20062
18 20061
19
An Inter-Channel Redundancy Removal Approach for High-Quality Multichannel Audio Compression
20009
20 199716

About C.‐C. Jay Kuo

C.‐C. Jay Kuo is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computer Graphics and Computer-Aided Design, having authored 1.2k papers that have together received 23.6k indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (205 papers), Advanced Data Compression Techniques (204 papers), Advanced Vision and Imaging (181 papers), Image and Video Quality Assessment (165 papers), Image and Signal Denoising Methods (129 papers), Advanced Image Processing Techniques (121 papers), Wireless Communication Networks Research (88 papers) and Advanced Image and Video Retrieval Techniques (85 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (13.1k citations), Signal Processing (5.6k citations) and Computer Graphics and Computer-Aided Design (1.1k citations). C.‐C. Jay Kuo has collaborated with scholars based in United States, Taiwan and China. Frequent co-authors include T. Chang, Weisi Lin, Y.-W. Peter Hong, Man-On Pun, Wan-Jen Huang, Michele Morelli, Jingliang Peng, Chung-Ping Wu, Selina Chu and J. Thomas Grayston. Their work appears in journals such as Journal of Visual Communication and Image Representation, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia and Signal Processing Image Communication.

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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