H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 105 Citations 115,460 673 World Ranking 116 National Ranking 72

Research.com Recognitions

Awards & Achievements

2019 - IEEE Fourier Award for Signal Processing “For seminal contributions and high-impact innovations to the theory and application of perception-based image and video processing.”

2008 - SPIE Fellow

2007 - OSA Fellows For fundamental research contributions to and technical leadership in digital image and video processing.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image quality, Image processing and Distortion. His Artificial intelligence research is multidisciplinary, relying on both Scene statistics, Video quality and Pattern recognition. His research on Computer vision often connects related topics like Algorithm.

His studies deal with areas such as Structural similarity, Wavelet and Discrete cosine transform as well as Image quality. His studies examine the connections between Image processing and genetics, as well as such issues in Video processing, with regards to Context-adaptive binary arithmetic coding. His research in Transform coding tackles topics such as JPEG which are related to areas like Visibility.

His most cited work include:

  • Image quality assessment: from error visibility to structural similarity (24396 citations)
  • A universal image quality index (3978 citations)
  • Image information and visual quality (2348 citations)

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

His primary areas of investigation include Artificial intelligence, Computer vision, Image quality, Pattern recognition and Image processing. His studies in Artificial intelligence integrate themes in fields like Distortion, Video quality and Scene statistics. His study in Video quality is interdisciplinary in nature, drawing from both Multimedia and Quality of experience.

As part of his studies on Computer vision, Alan C. Bovik often connects relevant subjects like Algorithm. His Image quality study combines topics from a wide range of disciplines, such as Perception, Transform coding, Image compression, Structural similarity and Visualization. His study ties his expertise on Video processing together with the subject of Image processing.

He most often published in these fields:

  • Artificial intelligence (68.05%)
  • Computer vision (52.07%)
  • Image quality (21.89%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (68.05%)
  • Video quality (15.50%)
  • Image quality (21.89%)

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

His primary areas of study are Artificial intelligence, Video quality, Image quality, Computer vision and Distortion. His Artificial intelligence study incorporates themes from Scene statistics, Machine learning and Pattern recognition. His work deals with themes such as Frame rate, Multimedia, Quality of experience and Data mining, which intersect with Video quality.

Alan C. Bovik works mostly in the field of Image quality, limiting it down to topics relating to Perception and, in certain cases, Artificial neural network. His Computer vision research focuses on Codec and how it relates to Digital television and Quantization. His work on Image processing as part of his general Image study is frequently connected to Process, thereby bridging the divide between different branches of science.

Between 2017 and 2021, his most popular works were:

  • A Subjective and Objective Study of Stalling Events in Mobile Streaming Videos (39 citations)
  • Large-Scale Study of Perceptual Video Quality (38 citations)
  • In-Capture Mobile Video Distortions: A Study of Subjective Behavior and Objective Algorithms (34 citations)

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

  • Artificial intelligence
  • Statistics
  • Computer vision

Artificial intelligence, Video quality, Distortion, Image quality and Pattern recognition are his primary areas of study. His Artificial intelligence research incorporates elements of Machine learning, Perception, Scene statistics and Computer vision. In the subject of general Computer vision, his work in Transform coding and Image resolution is often linked to Process, thereby combining diverse domains of study.

The concepts of his Image quality study are interwoven with issues in Eye tracking, Gaze and Benchmark. His Pattern recognition research is multidisciplinary, incorporating elements of Image processing and Stereoscopy. While the research belongs to areas of Image processing, Alan C. Bovik spends his time largely on the problem of Energy, intersecting his research to questions surrounding Normalization.

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

Image quality assessment: from error visibility to structural similarity

Zhou Wang;A.C. Bovik;H.R. Sheikh;E.P. Simoncelli.
IEEE Transactions on Image Processing (2004)

27742 Citations

Multiscale structural similarity for image quality assessment

Z. Wang;E.P. Simoncelli;A.C. Bovik.
asilomar conference on signals, systems and computers (2003)

3528 Citations

Image information and visual quality

H.R. Sheikh;A.C. Bovik.
IEEE Transactions on Image Processing (2006)

3061 Citations

Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

Zhou Wang;A.C. Bovik.
IEEE Signal Processing Magazine (2009)

2467 Citations

A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms

H.R. Sheikh;M.F. Sabir;A.C. Bovik.
IEEE Transactions on Image Processing (2006)

2314 Citations

No-Reference Image Quality Assessment in the Spatial Domain

A. Mittal;A. K. Moorthy;A. C. Bovik.
IEEE Transactions on Image Processing (2012)

2162 Citations

Multichannel texture analysis using localized spatial filters

A.C. Bovik;M. Clark;W.S. Geisler.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1990)

2113 Citations

Making a “Completely Blind” Image Quality Analyzer

A. Mittal;R. Soundararajan;A. C. Bovik.
IEEE Signal Processing Letters (2013)

1695 Citations

Video Quality Assessment Based on Structural Distortion Measurement

Zhou Wang;Zhou Wang;Ligang Lu;Alan C. Bovik.
Signal Processing-image Communication (2004)

1447 Citations

Multi-scale structural similarity for image quality assessment

Zhou Wang;Eero P. Simoncelli;Alan C. Bovik.
asilomar conference on signals, systems and computers (2003)

1374 Citations

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