Intelligent Image and Video Compression: Communicating Pictures - Tapa blanda

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Professor David R. Bull PhD, FIET, FIEEE, CEng. obtained his PhD from the University of Cardiff in 1988. He currently holds the Chair in Signal Processing at the University of Bristol where he is head of the Visual Information Laboratory and Director of Bristol Vision Institute, a group of some 150 researchers in vision science, spanning engineering, psychology, biology, medicine and the creative arts. In 1996 David helped to establish the UK DTI Virtual Centre of Excellence in Digital Broadcasting and Multimedia Technology and was one of its Directors from 1997-2000. He has also advised Government through membership of the UK Foresight Panel, DSAC and the HEFCE Research Evaluation Framework. He is also now Director of the UK Government’s new MyWorld Strength in Places programme.
David has worked widely across image and video processing focused on streaming, broadcast and wireless applications. He has published over 600 academic papers, various articles and 4 books and has given numerous invited/keynote lectures and tutorials. He has also received awards including the IEE Ambrose Fleming Premium for his work on Primitive Operator Digital Filters and a best Paper Award for his work on Link Adaptation for Video Transmission. David’s work has been exploited commercially and he has acted as a consultant for companies and governments across the globe. In 2001, he co-founded ProVision Communication Technologies Ltd., who launched the world’s first robust multi-source wireless HD sender for consumer use. His recent award-winning and pioneering work on perceptual video compression using deep learning, has produced world-leading rate-quality performance.

Dr. Fan (Aaron) Zhang PhD received the B.Sc. (Hons) and M.Sc. degrees from Shanghai Jiao Tong University (2005 and 2008 respectively), and his Ph.D from the University of Bristol (2012). He is currently a Research Fellow in the Visual Information Laboratory at the University of Bristol, working on video compression and immersive video processing. His research interests include perceptual video compression, video quality assessment and immersive video formats. Aaron has published over 30 academic papers and has contributed to two books previous books on video compression. His work on super-resolution-based video compression, has contributed to international standardization processes and he was a co-winner of the 2017 IEEE Grand Challenge on Video Compression.

De la contraportada

Intelligent Image and Video Compression: Communicating Pictures, 2nd edition

David Bull and Fan Zhang

explains the requirements, analysis, design and application of a modern video coding system. It draws on the authors extensive academic and professional experience in this field to deliver a text that is algorithmically rigorous yet accessible, relevant to modern standards and practical. It builds on a thorough grounding in mathematical foundations and visual perception to demonstrate how modern image and video compression methods can be designed to meet the rate-quality performance levels demanded by today's applications and users, in the context of prevailing network constraints.

Key Features

• An approach that combines algorithmic rigor with practical implementation using numerous worked examples.
• Explains how video compression methods exploit statistical redundancies, natural correlations and knowledge of human perception to improve performance.
• Uses contemporary video coding standards (AVC and HEVC) as a vehicle for explaining block-based compression.
• Provides broad coverage of important topics such as visual quality assessment and video streaming.

New to this edition:

• Coverage of new more immersive applications, explaining compression requirements and solutions for HDR and UHDTV, VR, AR and MR.
• Description of how we can measure viewer engagement with these applications.
• An introduction to machine learning algorithms and coverage of how these can optimize compression tools and their performance.
• Inclusion of the latest advances in perceptual metrics, such as VMAF.
• Description of new and extended databases for video quality evaluation and for training machine learning systems.
• Coverage of recent innovations and standards to support adaptive video streaming.
• A review of the perceptual influences of dynamic range including descriptions of perceptual quantization and new formats.
• A comprehensive update on recent compression standards to include the key attributes of new and emerging standards such as AV1, VVC and AV2.