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Efficient Predictive Algorithms for Image Compression

Efficient Predictive Algorithms for Image Compression

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This book discusses efficient prediction techniques for the current state-of-the-art High Efficiency Video Coding (HEVC) standard, focusing on the compression of a wide range of video signals, such as 3D video, Light Fields and natural images. The authors begin with a review of the state-of-the-art predictive coding methods and compression technologies for both 2D and 3D multimedia contents, which provides a good starting point for new researchers in the field of image and video compression. New prediction techniques that go beyond the standardized compression technologies are then presented and discussed. In the context of 3D video, the authors describe a new predictive algorithm for the compression of depth maps, which combines intra-directional prediction, with flexible block partitioning and linear residue fitting. New approaches are described for the compression of Light Field and still images, which enforce sparsity constraints on linear models. The Locally Linear Embedding-based prediction method is investigated for compression of Light Field images based on the HEVC technology. A new linear prediction method using sparse constraints is also described, enabling improved coding performance of the HEVC standard, particularly for images with complex textures based on repeated structures. Finally, the authors present a new, generalized intra-prediction framework for the HEVC standard, which unifies the directional prediction methods used in the current video compression standards, with linear prediction methods using sparse constraints. Experimental results for the compression of natural images are provided, demonstrating the advantage of the unified prediction framework over the traditional directional prediction modes used in HEVC standard.

1 Introduction1.1 Motivation1.2 Objectives and contributions of the book1.3 Outline of the book2 Prediction techniques for image and video coding2.1 Digital video representation2.2 Image prediction overview2.3 State-of-the-art prediction methods2.3.1 Intra-frame prediction2.3.2 Inter-frame prediction2.4 Least-squares prediction methods2.4.1 Linear prediction of images and video using LSP2.4.2 Context-based adaptive LSP2.4.3 Block-based LSP2.4.4 Spatio-temporal LSP2.5 Sparse representation for image prediction2.5.1 Sparse prediction problem formulation2.5.2 Matching Pursuit methods2.5.3 Template Matching algorithm2.5.4 Neighbour embedding methods2.6 Conclusions3 Image and video coding standards3.1 Hybrid video compression3.2 Compression of 2D video3.2.1 H.265/HEVC standard3.2.2 Experimental results3.3 Compression of 3D video3.3.1 3D video systems3.3.2 3D video coding standards3.3.3 Experimental results3.4 Conclusions4 Compression of depth maps using predictive coding4.1 Overview of intra techniques for depth map coding4.1.1 Directional intra prediction4.1.2 Depth Modelling Modes4.1.3 Depth Lookup Table4.1.4 Segment-wise DC Coding4.1.5 Single Depth Intra Mode4.1.6 View Synthesis Optimisation4.2 Overview of Predictive Depth Coding4.3 Coding techniques of PDC algorithm4.3.1 Flexible block partitioning4.3.2 Directional intra prediction framework4.3.3 Constrained Depth Modelling Mode4.3.4 Residual signal coding4.3.5 Bitstream syntax and context modelling4.4 PDC encoder control4.5 Experimental Results4.5.1 Evaluation of PDC algorithm for intra coding4.5.2 Evaluation of PDC algorithm using VSO metric4.5.3 Evaluation of PDC algorithm combined with 3D-HEVC standard4.6 Conclusions5 Sparse representation methods for image prediction5.1 3D holoscopic image coding using LLE-based prediction5.1.1 Proposed HEVC encoder using LLE-based prediction5.1.2 Experimental Results5.2 The sparse-LSP method for intra prediction5.2.1 Algorithm description5.2.2 Mathematical interpretation5.3 Application of sparse-LSP to HEVC standard5.3.1 Implementation details5.3.2 Experimental results5.4 Conclusions6 Generalised optimal sparse predictors6.1 Two-stage interpretation of directional prediction6.2 Generalising directional prediction6.3 Sparse model estimation algorithms6.3.1 Matching Pursuit algorithms6.3.2 Least Angle Regression6.3.3 LASSO regression6.3.4 Elastic Net regression6.4 Proposed algorithm based on adaptive sparse predictors for HEVC6.5 Experimental results6.5.1 Effect of sparsity constraints6.5.2 Regularisation parameters for optimal RD performance6.5.3 RD performance relative to other intra prediction methods6.6 Conclusions7 Conclusions and other research directions8 Test signals8.1 Test images8.2 Holoscopic imagesReferences
Springer Verlag
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169 Seiten
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