Jongyoo Kim

1.9k total citations · 1 hit paper
26 papers, 1.2k citations indexed

About

Jongyoo Kim is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Cognitive Neuroscience. According to data from OpenAlex, Jongyoo Kim has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 13 papers in Media Technology and 6 papers in Cognitive Neuroscience. Recurrent topics in Jongyoo Kim's work include Image and Video Quality Assessment (14 papers), Advanced Image Fusion Techniques (8 papers) and Visual Attention and Saliency Detection (7 papers). Jongyoo Kim is often cited by papers focused on Image and Video Quality Assessment (14 papers), Advanced Image Fusion Techniques (8 papers) and Visual Attention and Saliency Detection (7 papers). Jongyoo Kim collaborates with scholars based in South Korea, China and United States. Jongyoo Kim's co-authors include Sanghoon Lee, Duc Chien Nguyen, Alan C. Bovik, Deepti Ghadiyaram, Lei Zhang, Hui Zeng, Heeseok Oh, Sewoong Ahn, Xin Tong and Jiaolong Yang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Jongyoo Kim

25 papers receiving 1.2k citations

Hit Papers

Fully Deep Blind Image Quality Predictor 2016 2026 2019 2022 2016 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jongyoo Kim South Korea 12 1.1k 655 58 57 55 26 1.2k
Xiangyu Xu China 14 1.3k 1.1× 497 0.8× 72 1.2× 49 0.9× 15 0.3× 33 1.5k
Yuqian Zhou United States 18 1.7k 1.6× 545 0.8× 51 0.9× 127 2.2× 17 0.3× 32 1.9k
Greg Shakhnarovich United States 12 1.4k 1.3× 733 1.1× 119 2.1× 46 0.8× 24 0.4× 19 1.6k
Martin Čadík Czechia 16 780 0.7× 221 0.3× 98 1.7× 62 1.1× 53 1.0× 36 978
Songnan Li Hong Kong 15 821 0.7× 278 0.4× 65 1.1× 23 0.4× 22 0.4× 41 915
Jia Yan China 9 686 0.6× 370 0.6× 18 0.3× 59 1.0× 17 0.3× 35 878
Weixia Zhang China 9 865 0.8× 441 0.7× 11 0.2× 82 1.4× 33 0.6× 22 1.0k
Ke Yu China 10 889 0.8× 288 0.4× 27 0.5× 43 0.8× 57 1.0× 34 1.1k
Anish Mittal United States 10 1.1k 1.0× 596 0.9× 15 0.3× 15 0.3× 92 1.7× 19 1.2k

Countries citing papers authored by Jongyoo Kim

Since Specialization
Citations

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

Fields of papers citing papers by Jongyoo Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jongyoo Kim. 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 Jongyoo Kim. The network helps show where Jongyoo Kim may publish in the future.

Co-authorship network of co-authors of Jongyoo Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Jongyoo Kim. A scholar is included among the top collaborators of Jongyoo Kim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jongyoo Kim. Jongyoo Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nguyen, Duc Chien, et al.. (2023). Double reverse diffusion for realistic garment reconstruction from images. Engineering Applications of Artificial Intelligence. 127. 107404–107404. 4 indexed citations
2.
Huang, Yangyu, Xi Chen, Jongyoo Kim, et al.. (2023). FreeEnricher: Enriching Face Landmarks without Additional Cost. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 962–970. 1 indexed citations
3.
Kim, Jinwoo, Beom Kwon, Jongyoo Kim, & Sanghoon Lee. (2023). MNET++: Music-Driven Pluralistic Dancing Toward Multiple Dance Genre Synthesis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 15036–15050. 6 indexed citations
4.
Yang, Jiaolong, et al.. (2022). MPS-NeRF: Generalizable 3D Human Rendering From Multiview Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(8). 6110–6121. 25 indexed citations
5.
Nguyen, Duc Chien, Woojae Kim, Jongyoo Kim, et al.. (2022). Single-Image 3-D Reconstruction: Rethinking Point Cloud Deformation. IEEE Transactions on Neural Networks and Learning Systems. 35(5). 6613–6627. 10 indexed citations
6.
Kim, Woojae, Duc Chien Nguyen, Jinwoo Kim, et al.. (2021). Diverse and Adjustable Versatile Image Enhancer. IEEE Access. 9. 80883–80896. 1 indexed citations
7.
Kim, Jongyoo, Jiaolong Yang, & Xin Tong. (2021). Learning High-Fidelity Face Texture Completion without Complete Face Texture. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 13970–13979. 11 indexed citations
8.
Kim, Jongyoo, Duc Chien Nguyen, & Sanghoon Lee. (2018). Deep CNN-Based Blind Image Quality Predictor. IEEE Transactions on Neural Networks and Learning Systems. 30(1). 11–24. 231 indexed citations
9.
Kim, Jongyoo, Duc Chien Nguyen, Sewoong Ahn, Chong Luo, & Sanghoon Lee. (2018). Multiple Level Feature-Based Universal Blind Image Quality Assessment Model. 291–295. 26 indexed citations
10.
Lee, Sanghoon & Jongyoo Kim. (2017). An identification framework for print-scan books in a large database. Information Sciences. 396. 33–54. 6 indexed citations
11.
Oh, Heeseok, Sewoong Ahn, Jongyoo Kim, & Sanghoon Lee. (2017). Blind Deep S3D Image Quality Evaluation via Local to Global Feature Aggregation. IEEE Transactions on Image Processing. 26(10). 4923–4936. 69 indexed citations
12.
Kim, Jongyoo, Hui Zeng, Deepti Ghadiyaram, et al.. (2017). Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment. IEEE Signal Processing Magazine. 34(6). 130–141. 204 indexed citations
13.
Kim, Jongyoo & Sanghoon Lee. (2017). Deep blind image quality assessment by employing FR-IQA. 3180–3184. 13 indexed citations
14.
Kim, Jongyoo, Taewan Kim, Sanghoon Lee, & Alan C. Bovik. (2017). Quality Assessment of Perceptual Crosstalk on Two-View Auto-Stereoscopic Displays. IEEE Transactions on Image Processing. 26(10). 4885–4899. 15 indexed citations
15.
Kim, Jongyoo & Sanghoon Lee. (2016). Fully Deep Blind Image Quality Predictor. IEEE Journal of Selected Topics in Signal Processing. 11(1). 206–220. 346 indexed citations breakdown →
16.
Kim, Woojae, et al.. (2016). No-reference perceptual sharpness assessment for ultra-high-definition images. 10. 86–90. 2 indexed citations
17.
Kim, Taewan, et al.. (2016). Perceptual Crosstalk Prediction on Autostereoscopic 3D Display. IEEE Transactions on Circuits and Systems for Video Technology. 27(7). 1450–1463. 6 indexed citations
18.
Kim, Jongyoo, et al.. (2016). Blind Sharpness Prediction for Ultrahigh-Definition Video Based on Human Visual Resolution. IEEE Transactions on Circuits and Systems for Video Technology. 27(5). 951–964. 11 indexed citations
19.
Kwon, Beom, et al.. (2016). Framework Implementation of Image-Based Indoor Localization System Using Parallel Distributed Computing. The Journal of Korean Institute of Communications and Information Sciences. 41(11). 1490–1501. 1 indexed citations
20.
Oh, Heeseok, Jongyoo Kim, Sanghoon Lee, & Alan C. Bovik. (2015). 3D visual discomfort predictor based on neural activity statistics. 85. 3560–3564. 1 indexed citations

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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