Sungdae Cho

491 total citations
25 papers, 324 citations indexed

About

Sungdae Cho is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, Sungdae Cho has authored 25 papers receiving a total of 324 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 10 papers in Signal Processing and 2 papers in Computer Networks and Communications. Recurrent topics in Sungdae Cho's work include Advanced Data Compression Techniques (17 papers), Video Coding and Compression Technologies (9 papers) and Image and Signal Denoising Methods (8 papers). Sungdae Cho is often cited by papers focused on Advanced Data Compression Techniques (17 papers), Video Coding and Compression Technologies (9 papers) and Image and Signal Denoising Methods (8 papers). Sungdae Cho collaborates with scholars based in United States, South Korea and Singapore. Sungdae Cho's co-authors include William A. Pearlman, Nitin Singhal, In Kyu Park, Chris Kim, In Kyu Park, Xiaoli Tang, Tae‐Jin Lee, Yunje Oh, Jaehoon Lee and Hoon Kim and has published in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology, Signal Processing and IEEE Transactions on Instrumentation and Measurement.

In The Last Decade

Sungdae Cho

25 papers receiving 300 citations

Peers

Sungdae Cho
Comparison fields: 5 of 49
  • Computer Vision and Pattern Recognition 259
  • Signal Processing 103
  • Computer Networks and Communications 42
  • Media Technology 34
  • Electrical and Electronic Engineering 34
Replace Chae Eun Rhee with:
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Ju-Yuan Hsiao Taiwan
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Chae Eun Rhee South Korea View profile →
Citations per field, relative to Sungdae Cho
Sungdae Cho · 1×
Citations per year, relative to Sungdae Cho
Sungdae Cho · 1×

Countries citing papers authored by Sungdae Cho

Since Specialization
Citations

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

Fields of papers citing papers by Sungdae Cho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungdae Cho

This figure shows the co-authorship network connecting the top 25 collaborators of Sungdae Cho. A scholar is included among the top collaborators of Sungdae Cho 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 Sungdae Cho. Sungdae Cho 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
# Work Indexed citations
1 3
2 3
3 1
4 101
5 5
6 5
7 14
8 7
9
Noise insensitive demosaicing algorithm
1
10 2
11 5
12 17
13 2
14 7
15 17
16 11
17 1
18 7
19 1
20 20

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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