Overview
- Explores motion anchoring strategies for video compression systems, which represent a shift from a “prediction-centric” point of view to a “physical” representation of the underlying motion of the scene
- Investigates new motion anchoring strategies that are targeted at wavelet-based highly scalable video compression (WSVC)
- Develops an analytical model to determine the weights of the different spatiotemporal subbands and assess the suitability and benefits of this reference-based WSVC for (highly scalable) video compression
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
- Wavelet-based Highly Scalable Video Compression (WSVC)
- Temporal Frame Interpolation (TFI)
- Bidirectional Hierarchical Anchoring (BIHA)
- Forward-Only Hierarchical Anchoring (FOHA)
- Base-Anchored Motion (BAM)
- Selective Wavelet Coefficient Attenuation (SWCA)
- Optical Blur Synthesis
- Texture Optimizations
- Disocclusion and Folding Likelihood Map (DFLM)
- Scalable Image
About this book
A key element of any modern video codec is the efficient exploitation of temporal redundancy via motion-compensated prediction. In this book, a novel paradigm of representing and employing motion information in a video compression system is described that has several advantages over existing approaches. Traditionally, motion is estimated, modelled, and coded as a vector field at the target frame it predicts. While this “prediction-centric” approach is convenient, the fact that the motion is “attached” to a specific target frame implies that it cannot easily be re-purposed to predict or synthesize other frames, which severely hampers temporal scalability.
In light of this, the present book explores the possibility of anchoring motion at reference frames instead. Key to the success of the proposed “reference-based” anchoring schemes is high quality motion inference, which is enabled by the use of a more “physical” motion representation than the traditionally employed “block” motion fields. The resulting compression system can support computationally efficient, high-quality temporal motion inference, which requires half as many coded motion fields as conventional codecs. Furthermore, “features” beyond compressibility — including high scalability, accessibility, and “intrinsic” framerate upsampling — can be seamlessly supported. These features are becoming ever more relevant as the way video is consumed continues shifting from the traditional broadcast scenario to interactive browsing of video content over heterogeneous networks.
This book is of interest to researchers and professionals working in multimedia signal processing, in particular those who are interested in next-generation video compression. Two comprehensive background chapters on scalable video compression and temporal frame interpolation make the book accessible for students and newcomers to the field.
Authors and Affiliations
About the author
From 2011 to 2013, he was with the Image and Visual Representation Group (IVRG) at EPFL as a research engineer, where he was working on computational photography problems, with emphasis on near-infrared imaging. He currently holds a post-doctoral position at UNSW Sydney, working on next-generation video compression systems. His research interests are in computational photography and highly scalable and accessible video compression, with a focus on temporal scalability.
Bibliographic Information
Book Title: Novel Motion Anchoring Strategies for Wavelet-based Highly Scalable Video Compression
Authors: Dominic Rüfenacht
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-981-10-8225-2
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN: 978-981-10-8224-5Published: 13 April 2018
Softcover ISBN: 978-981-13-4097-0Published: 10 January 2019
eBook ISBN: 978-981-10-8225-2Published: 03 April 2018
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XXIII, 182
Number of Illustrations: 30 b/w illustrations, 62 illustrations in colour
Topics: Signal, Image and Speech Processing, Image Processing and Computer Vision, Visualization