Efficient Scalable Matching Pursuit Video Coding and Fine-Grained Layered Multicast

It has been shown that matching pursuit video coding achieves a better performance than DCT-based video coding in terms of subjective and objective quality at very low bit-rates. However, due to its massive computational complexity, traditional matching pursuit video encoding is usually only approximated by searching a local maximum atom at each iteration. In this thesis, we introduce a multiple blocks update algorithm for matching pursuit video coding that achieves better coding performance and lower coding complexity than traditional approaches. We also introduce a systematic dictionary design, which combines eigen-dictionary approximation and tree-based atom search. This systematic approach approximates a given target dictionary efficiently and enables a tradeoff between coding performance and complexity. A numerical solution is also developed to achieve the optimal coding performance under the constraint of a given complexity. We propose a fine-grained scalable (FGS) matching pursuit video coding scheme based on progressive atom coding and quadtree representation of atom positions. To this end, we develop a quadtree prediction algorithm to explore the temporal and spatial redundancy of atom positions between adjacent bit-planes. We also propose a two-layer matching pursuit video coding scheme in which the same set of motion vectors, estimated from a weighted combination of previous base and enhancement layers, is applied to motion compensation of both layers. The two-layer system achieves a tradeoff between the coding quality of the base and enhancement layers, while retaining their fixed bit-rates. Finally, we present a new, fine-grained scalable layered multicast scheme based on a hybrid coarse/fine layered structure. We develop a congestion control protocol for hybrid coarse/fine layered multicast by using hierarchical layered probing and one-way delay trend detection. We demonstrate that fine-grained rate adjustment can be achieved through hierarchical probing. By combining the fine-grained layered multicast approach with scalable video coding, we present a framework that achieves efficient scalable video streaming over heterogeneous networks.1 Introduction 1 1.1 Matching Pursuit Video Coding . . . . . . . . . . . 1 1.2 Layered Multicast Video Transmission . . . . . . .. 4 1.3 Outline of Thesis . . . . . . . . . . . . . . . 6 2 Atom Search 11 2.1 Introduction . . . . . . . . . . . . . . . . . . . 12 2.2 Matching Pursuit Algorithm and Atom Extraction . . 14 2.3 Multiple Blocks Approximation .. . . . . . . . . . 16 2.3.1 Block Selection . . . . . . . . . . . . . . . . 17 2.3.2 Block Content Approximation . . . . . . . . . . 21 2.4 Performance Evaluations and Comparisons . . . . . 25 2.5 Conclusions . . . . . . . . . . . . . . . . . . . 31 3 Dictionary Design 33 3.1 Introduction . . . . . . . . . . . . . . . . . . . 34 3.2 Two-Stage-VQ Design . . . . . . . . . . . . . . . 39 3.2.1 Dictionary Approximation . . . . . . . . . . . . 39 3.2.2 Tree Based-VQ on an Eigen-Dictionary . . . . . . 43 3.2.3 Approximation Results and Dictionary Elimination .. 48 3.3 Computational Complexity Analysis . . . . . . .. . 51 3.4 Performance Evaluation . . . . . . . . . . . . . . 55 3.5 Cost-constrained Parameter Selection . . . . . . . 60 3.6 Conclusions . . . . . . . . . . . . . . . . . . . 63 4 Scalable Video Coding 65 4.1 Introduction . . . . . . . . . . . . . . . . . . . 66 4.2 Progressive Atom Coding . . . . . . .. . . . . . . 69 4.2.1 Set Partitioning Coding of Atoms . . . . . . . . 69 4.3 FGS Matching Pursuit Video Codec . . . . . . . . . 80 4.3.1 Proposed FGS . . . . . . . . . . . . . . . . . . 81 4.3.2 Performance Evaluations and Comparisons . . .. . 84 4.4 Two-Layer SNR Scalable System . . . . . . . . . . 89 4.4.1 Motion Estimation from Two Reconstructed Images . 91 4.4.2 Bit-plane-based Residuals Encoding . . . . . . . . 93 4.4.3 Comparisons . . . . . . . . . . .. . . . . . . . . 94 4.5 Conclusions . . .. . . . . . . . . . . . . . . . . . 97 5 Fine-grained Layered Multicast 99 5.1 Introduction . . . . . . . . . . . . . . . . . . . 100 5.2 Delay Trend Detection and BIC Control . . . . . . . 104 5.2.1 Delay Trend Model . . . . . . . . . . . . . . . . 105 5.2.2 BIC Congestion Control for Layered Multicast . . 106 5.3 A Hybrid Coarse/Fine Layered Probing Scheme . . . . 108 5.3.1 Hierarchical Probing Scheme . . . . . . . . . . . 108 5.3.2 Hybrid Coarse/Fine Layered Multicast Structure .. 115 5.4 Experiments . . . . . . . . . . . . . . . . . . . . 118 5.5 Conclusions . . . . . . . . . . . . . . . . . . . . 123 6 Conclusion 12