King Ngi Ngan

9.9k citations
378 papers · 7.1k indexed · h-index 41

King Ngi Ngan

348 papers receiving 6.8k citations

Peers

King Ngi Ngan
Comparison fields: 5 of 133
  • Computer Vision and Pattern Recognition 6.3k
  • Signal Processing 1.7k
  • Media Technology 1.2k
  • Sensory Systems 291
  • Human-Computer Interaction 233
Replace S.S. Hemami with:
S.S. Hemami United States
Jianjun Lei China
Hongliang Li China
Chunping Hou China
Shang‐Hong Lai Taiwan
Xiongkuo Min China
Lihi Zelnik‐Manor Israel
Shao‐Yi Chien Taiwan
Patrick Le Callet France
Yuming Fang China
King Ngi Ngan relative to S.S. Hemami United States S.S. Hemami's profile →
Citations per field
00.5×1.5×2.5×
S.S. Hemami · 1×
Citations per year

Countries citing papers authored by King Ngi Ngan

Since Specialization
Citations

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

Fields of papers citing papers by King Ngi Ngan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by King Ngi Ngan. 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 King Ngi Ngan. The network helps show where King Ngi Ngan may publish in the future.

Co-authorship network

The 25 scholars most cited alongside King Ngi Ngan, 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 King Ngi Ngan Line = papers co-authored together King Ngi Ngan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20250
4 20243
5 20231
6 202316
7 20233
8 202118
9 20214
10 202112
11
Blind Tone-mapped Image Quality Assessment and Enhancement via Disentangled Representation Learning
20201
12 202025
13 202015
14 202045
15 201933
16 201975
17 201617
18
Visual Signal Quality Assessment: Quality of Experience (QoE)
20145
19
Foreground/Background Video Coding using H.261.
19971
20 198221

About King Ngi Ngan

King Ngi Ngan is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 378 papers that have together received 7.1k indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (113 papers), Advanced Vision and Imaging (106 papers), Advanced Data Compression Techniques (96 papers), Image and Video Quality Assessment (91 papers), Advanced Image and Video Retrieval Techniques (80 papers), Advanced Image Processing Techniques (60 papers), Visual Attention and Saliency Detection (56 papers) and Image Enhancement Techniques (48 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.3k citations), Signal Processing (1.7k citations) and Media Technology (1.2k citations). King Ngi Ngan has collaborated with scholars based in Hong Kong, China and Australia. Frequent co-authors include Hongliang Li, Douglas Chai, Lin Ma, Songnan Li, Fanman Meng, Qingbo Wu, Thomas Meier, Zhenyu Wei, Zhenzhong Chen and Hao Zhang. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Geophysical Research Atmospheres and Journal of Applied Physics.

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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