Video Data Compression for Multimedia Computing
Statistically Based and Biologically Inspired Techniques
Series: The Springer International Series in Engineering and Computer Science; 378;
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Product details:
- Edition number Softcover reprint of the original 1st ed. 1997
- Publisher Springer US
- Date of Publication 12 October 2012
- Number of Volumes 1 pieces, Book
- ISBN 9781461378624
- Binding Paperback
- No. of pages421 pages
- Size 235x155 mm
- Weight 676 g
- Language English
- Illustrations XVII, 421 p. Illustrations, black & white 0
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Long description:
During the past few years, we have been witnessing the rapid growth of the ap plications of Interactive Digital Video, Multimedia Computing, Desktop Video Teleconferencing, Virtual Reality, and High Definition Television (HDTV). An other information revolution which is tied to Cyberspace is almost within reach. The information, data, text, graphics, video, sound, etc. , in the form of multi media, can be requested, accessed, distributed, and transmitted to potentially every household. This is changing and will continue to change the way of people doing business, functioning in the society, and entertaining. In the foreseeable future, many personalized, portable information terminals, which can be car ried while traveling, will provide the link to central computer network to allow information exchange including videos from a node to node, from a center to a node, or nodes. Facing this opportunity, the question is what are the major significant technical challenges that people have to solve to push the-state-of-the-art for the realiza tion of the above mentioned technology advancement? From our professional judgement We feel that one of the major technical challenges is in Video Data Compression. Video communications in the form of desktop teleconferencing, videophone, network video delivery on demand, even games, are going to be major media traveling in the information super highway, hopping from one node in the Cyberspace to the other.
MoreTable of Contents:
1 Wavelet-Based Video Compression.- 1 Introduction.- 2 Background.- 3 Wavelet-decomposed Multiresolution Motion Compensation.- 4 Bit Allocation and Quantization.- 5 Simulation Results.- References.- 2 Video Compression using Adaptive Wavelet Packets and DPCM.- 1 Introduction.- 2 A 3D Subband Coding for Video Compression.- 3 Adaptive Wavelet Packet Decomposition.- 4 Video Coder Using Adaptive Wavelet Packet and DPCM.- 5 Entropy Encoder.- 6 Experimental Results.- 7 Conclusion.- References.- 3 Spatial Temporal Prediction for Video Data Compression.- 1 Introduction.- 2 Survey of the Existing Techniques.- 3 Mathematical Formulation.- 4 Experimental Results.- 5 Discussion.- 6 Appendix.- References.- 4 On Gibbs-Markov Models for Motion Computation.- 1 Introduction.- 2 Framework.- 3 Motion Trajectory Models.- 4 MAP and Other Bayesian Estimation Criteria.- 5 Models for the Likelihood Distribution.- 6 Pixel-based Motion Models.- 7 Block-based Motion Models.- 8 Region-based Motion Models.- 9 Summary.- 10 Appendix.- References.- 5 The HDTV Grand Alliance System.- 1 Introduction.- 2 System Requirements for Terrestrial Broadcast in the US.- 3 GA System Overview.- 4 HDTV System Description.- 5 Video Scanning Format Considerations.- 6 Migration to Higher Resolution.- References.- 6 Visual Motion Processing and Detection — from Biology to Modeling.- 1 Introduction to Biological Visual System.- 2 Modeling the Visual System.- 3 Detection of Local Motion Velocity in V1.- 4 Detection of Global Motion Velocity in MT.- 5 Detection of Global Motion Patterns in MST.- 6 Questions for Future Studies.- 7 Appendix.- References.- 7 Object Tracking using Hierarchical 2-D Mesh Modeling for Content Based Video Compression.- 1 Introduction.- 2 Modeling.- 3 Mesh Tracking.- 4 Application toContent-Based Video Compression.- 5 Results.- 6 Conclusion.- References.- 8 Separation of Moving Regions from Background in an Image Sequence Acquired with a Mobil Camera.- 1 Introduction.- 2 Motion Model and Motion Estimation.- 3 Motion Detection Algorithm.- 4 Computational Issues.- 5 Experimental Results.- 6 Conclusion.- References.- 9 Very Low Bit Rate Video Coding Based on Statistical Spatio-Temporal Prediction of Motion, Segmentation and Intensity Fields.- 1 Introduction.- 2 Framework of the Proposed Coding Approach.- 3 Motion and Segmentation Estimation and Prediction.- 4 Occlusions.- 5 Motion Model Failures.- 6 Global Refinement.- 7 Experimental Results.- 8 Conclusions.- References.- 10 Gibbs Model Based 3D Motion and Structure Estimation for Object-Based Video Coding Applications.- 1 Introduction.- 2 Estimation of Motion in Video Sequence.- 3 An Object-Based Rigid 3D Motion Analysis.- 4 A Non-rigid Gibbs 3D Motion Model.- 5 Discussion and Conclussion.- References.- 11 Motion Vector Prediction Based on Frame Differences.- 1 Introduction.- 2 Background.- 3 Formulation for Motion Prediction.- 4 Computational Issue.- 5 Experimental Results.- 6 Discussion and Conclusion.- References.
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