D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 74 Citations 19,269 468 World Ranking 636 National Ranking 12

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Computer vision, Image quality, Pattern recognition and Image processing. His research on Artificial intelligence frequently links to adjacent areas such as Metric. While the research belongs to areas of Computer vision, he spends his time largely on the problem of Entropy, intersecting his research to questions surrounding Quality assessment.

The various areas that Weisi Lin examines in his Image quality study include Data mining, Pooling, Transform coding, Histogram and Image restoration. His biological study spans a wide range of topics, including Image retrieval, Semantic gap, Relevance feedback and Feature detection. His study in Image processing is interdisciplinary in nature, drawing from both Uncompressed video, Seam carving and Robustness.

His most cited work include:

  • Perceptual visual quality metrics: A survey (706 citations)
  • Image Quality Assessment Based on Gradient Similarity (484 citations)
  • Saliency Detection in the Compressed Domain for Adaptive Image Retargeting (260 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Artificial intelligence, Computer vision, Image quality, Pattern recognition and Human visual system model. His research on Artificial intelligence frequently connects to adjacent areas such as Metric. In Computer vision, Weisi Lin works on issues like Video quality, which are connected to Frame rate.

His Image quality study combines topics from a wide range of disciplines, such as JPEG, Data mining, Pooling, Machine learning and Image restoration. His Pattern recognition research includes themes of Transform coding, Histogram and Feature detection. His Human visual system model research incorporates elements of Orientation, Visual perception and Just-noticeable difference.

He most often published in these fields:

  • Artificial intelligence (81.51%)
  • Computer vision (57.55%)
  • Image quality (33.21%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (81.51%)
  • Pattern recognition (31.51%)
  • Computer vision (57.55%)

In recent papers he was focusing on the following fields of study:

Weisi Lin mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Image quality and Feature extraction. His research in Visualization, Feature, Human visual system model, Deep learning and Pixel are components of Artificial intelligence. His work is dedicated to discovering how Pattern recognition, Salience are connected with Visual saliency and other disciplines.

As part of one scientific family, Weisi Lin deals mainly with the area of Computer vision, narrowing it down to issues related to the Salient, and often Constraint. Weisi Lin combines subjects such as Image processing, Transform coding, Visual perception, Machine learning and Rendering with his study of Image quality. The study incorporates disciplines such as Normalization, Convolutional neural network, Data mining and Benchmark in addition to Feature extraction.

Between 2016 and 2021, his most popular works were:

  • Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (174 citations)
  • No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization (139 citations)
  • A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures (132 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Statistics

His primary scientific interests are in Artificial intelligence, Computer vision, Image quality, Visualization and Pattern recognition. His Artificial intelligence study often links to related topics such as Machine learning. His work on Seam carving and Image processing as part of general Computer vision research is often related to Focus, thus linking different fields of science.

His Image quality research is multidisciplinary, relying on both Image warping, Data mining, Support vector machine and Metric. His Visualization research includes elements of Similarity, Sparse approximation, Stereoscopy, Convolutional neural network and Rendering. His Pattern recognition research integrates issues from Histogram, Luminance, Overfitting and Categorization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Perceptual visual quality metrics: A survey

Weisi Lin;C. C. Jay Kuo.
Journal of Visual Communication and Image Representation (2011)

929 Citations

Image Quality Assessment Based on Gradient Similarity

Anmin Liu;Weisi Lin;M. Narwaria.
IEEE Transactions on Image Processing (2012)

627 Citations

Saliency Detection in the Compressed Domain for Adaptive Image Retargeting

Yuming Fang;Zhenzhong Chen;Weisi Lin;Chia-Wen Lin.
IEEE Transactions on Image Processing (2012)

310 Citations

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

Ke Gu;Guangtao Zhai;Weisi Lin;Min Liu.
IEEE Transactions on Systems, Man, and Cybernetics (2016)

268 Citations

Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation

Zhongkang Lu;W. Lin;X. Yang;EePing Ong.
IEEE Transactions on Image Processing (2005)

249 Citations

No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics

Yuming Fang;Kede Ma;Zhou Wang;Weisi Lin.
IEEE Signal Processing Letters (2015)

242 Citations

Perceptual Quality Metric With Internal Generative Mechanism

Jinjian Wu;Weisi Lin;Guangming Shi;Anmin Liu.
IEEE Transactions on Image Processing (2013)

233 Citations

A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform

N. Imamoglu;Weisi Lin;Yuming Fang.
IEEE Transactions on Multimedia (2013)

231 Citations

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Ke Gu;Dacheng Tao;Jun-Fei Qiao;Weisi Lin.
IEEE Transactions on Neural Networks (2018)

220 Citations

A Psychovisual Quality Metric in Free-Energy Principle

Guangtao Zhai;Xiaolin Wu;Xiaokang Yang;Weisi Lin.
IEEE Transactions on Image Processing (2012)

218 Citations

Best Scientists Citing Weisi Lin

Guangtao Zhai

Guangtao Zhai

Shanghai Jiao Tong University

Publications: 111

Ke Gu

Ke Gu

Beijing University of Technology

Publications: 92

Siwei Ma

Siwei Ma

Peking University

Publications: 74

Xiaokang Yang

Xiaokang Yang

Shanghai Jiao Tong University

Publications: 66

Shiqi Wang

Shiqi Wang

City University of Hong Kong

Publications: 65

Wen Gao

Wen Gao

Peking University

Publications: 64

King Ngi Ngan

King Ngi Ngan

University of Electronic Science and Technology of China

Publications: 53

Alan C. Bovik

Alan C. Bovik

The University of Texas at Austin

Publications: 48

Sam Kwong

Sam Kwong

City University of Hong Kong

Publications: 42

Yuming Fang

Yuming Fang

Jiangxi University of Finance and Economics

Publications: 38

Debin Zhao

Debin Zhao

Harbin Institute of Technology

Publications: 36

C.-C. Jay Kuo

C.-C. Jay Kuo

University of Southern California

Publications: 36

Guangming Shi

Guangming Shi

Xidian University

Publications: 35

Lin Ma

Lin Ma

Harbin Institute of Technology

Publications: 33

Wenjun Zhang

Wenjun Zhang

Shanghai Jiao Tong University

Publications: 32

Qingming Huang

Qingming Huang

Chinese Academy of Sciences

Publications: 30

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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