Science Explorer Interactive view Map

Image and Video Quality Assessment

Image and Video Quality Assessment is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 20k research works in it since 1951. 17.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the assessment of image and video quality, covering topics such as perceptual quality, no-reference assessment, deep learning, structural similarity index, HTTP adaptive streaming, blur assessment, and quality of experience in multimedia content.

  • Image Quality Assessment
  • Perceptual Quality
  • Video Streaming
  • No-Reference Assessment
  • Deep Learning
  • Structural Similarity Index
  • HTTP Adaptive Streaming
  • Blur Assessment
  • Quality of Experience
  • Stereoscopic Images
Research works
20k
fractional, since 1951
In the world top 10%
3.5k
per year above
Top-10% rate
17.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+2%
the tick is no change

Which countries lead Image and Video Quality Assessment research?

By volume, China and the United States publish the most (1.3k and 363 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.3k works
  2. 2 United States 363 works
  3. 3 India 282 works
  4. 4 South Korea 138 works
  5. 5 Japan 136 works
  6. 6 United Kingdom 108 works
  7. 7 Germany 105 works
  8. 8 France 86 works
  9. 9 Brazil 76 works
  10. 10 Canada 75 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 49.2%United States: 13.5%India: 10.5%South Korea: 5.1%6 others listed: 21.7%49%largest
China1,328 · 49.2%United States363 · 13.5%India282 · 10.5%South Korea138 · 5.1%6 others listed586 · 21.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Image and Video Quality Assessment research?

By volume in 2022–2025, Shanghai Jiao Tong University publishes the most Image and Video Quality Assessment research, followed by Beijing University of Posts and Telecommunications and Tsinghua University.

Who are the leading researchers in Image and Video Quality Assessment?

The most-cited researchers publishing on Image and Video Quality Assessment include Ming–Hsuan Yang, Robert W. Heath and Dacheng Tao.

  1. 1 Ming–Hsuan Yang United States 7.4k citations
  2. 2 Robert W. Heath United States 7.1k citations
  3. 3 Dacheng Tao Australia 6.1k citations
  4. 4 Lei Zhang Hong Kong 5.3k citations
  5. 5 Xuemin Shen Canada 5.1k citations
  6. 6 Chao Dong China 4.9k citations
  7. 7 Zhu Han United States 4.7k citations
  8. 8 Shuicheng Yan Singapore 4.6k citations
  9. 9 Dusit Niyato Singapore 4.5k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Image and Video Quality Assessment research done?

The largest centres of Image and Video Quality Assessment research in 2022–2025 are Beijing (China), Shanghai (China), Shenzhen (China) and Seoul (South Korea). Among places with at least 20 works in it, it is an unusually large share of all research in Shenzhen.

Largest cities, 2022–2025

  1. 1 Beijing China 288 works
  2. 2 Shanghai China 128 works
  3. 3 Shenzhen China 83 works
  4. 4 Seoul South Korea 83 works
  5. 5 Nanjing China 70 works
  6. 6 Xi'an China 69 works
  7. 7 Hangzhou China 61 works
  8. 8 Tokyo Japan 59 works
  9. 9 Guangzhou China 48 works
  10. 10 Wuhan China 43 works

Where it is the local speciality

  1. ShenzhenCN · 82.7 works6.8×
← less than its size predictsmore →

Location quotient: how much more of its research is in Image and Video Quality Assessment than the world average.

See Image and Video Quality Assessment on the map

Where is the best place to study Image and Video Quality Assessment?

Among universities, judged by research, Nanyang Technological University, Shanghai Jiao Tong University and City University of Hong Kong score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 22.45%fractional works in this node (log) →share in the world top 10% →Nanyang Technological University: 19, 40.2%Shanghai Jiao Tong University: 55, 29.3%City University of Hong Kong: 16, 33.4%Xidian University: 29, 18.5%Jiangxi University of Finance and Economics: 11, 19.9%Shenzhen University: 20, 19.2%Beijing University of Posts and Telecommunications: 39, 10.5%Hangzhou Dianzi University: 12, 12.4%Tsinghua University: 32, 25.3%Ningbo University: 18, 15.8%Nanyang Technologica…City University of H…Shanghai Jiao Tong U…Xidian University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Nanyang Technological UniversitySingapore 66.540.2%6.8×19 +7.7%
2 Shanghai Jiao Tong UniversityChina 65.429.3%7.2×55 +20.6%
3 City University of Hong KongHong Kong 65.333.4%7.6×16 +40.2%
4 Xidian UniversityChina 61.618.5%11.0×29 +86.0%
5 Jiangxi University of Finance and EconomicsChina 60.219.9%36.4×11 +257.1%
6 Shenzhen UniversityChina 59.719.2%7.5×20 +399.3%
7 Beijing University of Posts and TelecommunicationsChina 55.610.5%17.9×39 +39.0%
8 Hangzhou Dianzi UniversityChina 51.612.4%9.8×12 +163.2%
9 Tsinghua UniversityChina 51.325.3%4.6×32 +50.7%
10 Ningbo UniversityChina 50.215.8%9.8×18 +11.4%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Image and Video Quality Assessment research growing?

Output in 2018–2022 was 2% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Image and Video Quality Assessment.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.