Faculty.AlBovik History

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!! [[http://ideal.ece.utexas.edu/ghosh/ | Al Bovik]] %rfloat% http://wncg.org/sites/wncg.org/files/styles/faculty_portrait_-_small/public/bovik%2520mar%252015-cropped.jpeg

!!!! Cockrell Family Regents Endowed Chair
!!!! Professor
!!!! Electrical and Computer Engineering

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His research interests include

• Monitoring
and control of streaming video quality
Measuring and optimizing video quality of experience (QoE)
Deep learning for image and video analytics
Visual discomfort prediction on immersive videos
Computational neuroscience
to:
'''Research Interests: '''

* Monitoring
and control of streaming video quality
* Measuring and optimizing video quality of experience (QoE)
* Deep learning for image and video analytics
* Visual discomfort prediction on immersive videos
* Computational neuroscience
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Al Bovik is a Cockrell Family Regents Endowed Chair Professor at The University of Texas at Austin. He received the 2017 Edwin H. Land Medal from the Optical Society of America, a 2015 Primetime Emmy Award for Outstanding Achievement in Engineering Development, and the 2013 IEEE Signal Processing Society ‘Society’ Award, along with about 10 journal paper awards. His algorithms are used throughout the global television cable, satellite, and broadcast industries, as well as by the streaming video industry, to monitor and control the visual quality of cinematic, Television, and Internet video content viewed by hundreds of millions of viewers every day. A Fellow of the IEEE, OSA, and SPIE, his books include The Handbook of Image and Video Processing, Modern Image Quality Assessment, and The Essential Guides to Image and Video Processing. He co-founded and was longest-serving Editor-in-Chief of the IEEE Transactions on Image Processing, and created the IEEE International Conference on Image Processing which he chaired in Austin, Texas, in 1994.

Al’s research interests include
to:
Al Bovik is a Cockrell Family Regents Endowed Chair Professor at The University of Texas at Austin.

He received the 2017 Edwin H. Land Medal from the Optical Society of America, a 2015 Primetime Emmy Award for Outstanding Achievement in Engineering Development, and the 2013 IEEE Signal Processing Society ‘Society’ Award, along with about 10 journal paper awards. His algorithms are used throughout the global television cable, satellite, and broadcast industries, as well as by the streaming video industry, to monitor and control the visual quality of cinematic, Television, and Internet video content viewed by hundreds of millions of viewers every day. A Fellow of the IEEE, OSA, and SPIE, his books include The Handbook of Image and Video Processing, Modern Image Quality Assessment, and The Essential Guides to Image and Video Processing. He co-founded and was longest-serving Editor-in-Chief of the IEEE Transactions on Image Processing, and created the IEEE International Conference on Image Processing which he chaired in Austin, Texas, in 1994.

His research interests include
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Al Bovik is a Cockrell Family Regents Endowed Chair Professor at The University of Texas at Austin. He received the 2017 Edwin H. Land Medal from the Optical Society of America, a 2015 Primetime Emmy Award for Outstanding Achievement in Engineering Development, and the 2013 IEEE Signal Processing Society ‘Society’ Award, along with about 10 journal paper awards. His algorithms are used throughout the global television cable, satellite, and broadcast industries, as well as by the streaming video industry, to monitor and control the visual quality of cinematic, Television, and Internet video content viewed by hundreds of millions of viewers every day. A Fellow of the IEEE, OSA, and SPIE, his books include The Handbook of Image and Video Processing, Modern Image Quality Assessment, and The Essential Guides to Image and Video Processing. He co-founded and was longest-serving Editor-in-Chief of the IEEE Transactions on Image Processing, and created the IEEE International Conference on Image Processing which he chaired in Austin, Texas, in 1994.

Al’s research interests include

• Monitoring and control of streaming video quality
• Measuring and optimizing video quality of experience (QoE)
• Deep learning for image and video analytics
• Visual discomfort prediction on immersive videos
• Computational neuroscience