Efficient predictive algorithms for image compression
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...
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| Main Authors: | , , , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2017.
|
| Subjects: | |
| ISBN: | 9783319511801 9783319511795 |
| Physical Description: | 1 online resource (xix, 169 pages) : illustrations (some color) |
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Table of contents
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| 245 | 0 | 0 | |a Efficient predictive algorithms for image compression / |c Luís Filipe Rosário Lucas, Eduardo Antônio Barros da Silva, Sérgio Manuel Maciel de Faria, Nuno Miguel Morais Rodrigues, Carla Liberal Pagliari. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c 2017. | |
| 300 | |a 1 online resource (xix, 169 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a počítač |b c |2 rdamedia | ||
| 338 | |a online zdroj |b cr |2 rdacarrier | ||
| 505 | 0 | |a List of Figures; List of Tables; Acronyms; 1 Introduction; 1.1 Motivation; 1.2 Objectives and Contributions of the Book; 1.3 Outline of the Book; 2 Prediction Techniques for Image and Video Coding; 2.1 Digital Video Representation; 2.2 Image Prediction Overview; 2.3 State-of-the-Art Prediction Methods; 2.3.1 Intra-Frame Prediction; 2.3.1.1 Directional Prediction Modes; 2.3.1.2 Planar and DC Prediction Modes; 2.3.1.3 Sample Smoothing; 2.3.1.4 Prediction Mode Coding; 2.3.1.5 Chroma Intra Prediction; 2.3.2 Inter-Frame Prediction. | |
| 505 | 8 | |a 2.3.2.1 Motion Compensation Using Block-Matching Algorithm2.3.2.2 Merge Mode; 2.3.2.3 Motion Vector Prediction; 2.4 Least-Squares Prediction Methods; 2.4.1 Linear Prediction of Images and Video Using LSP; 2.4.2 Context-Based Adaptive LSP; 2.4.3 Block-Based LSP; 2.4.4 Spatio-Temporal LSP; 2.5 Sparse Representation for Image Prediction; 2.5.1 Sparse Prediction Problem Formulation; 2.5.2 Matching Pursuit Methods; 2.5.2.1 Orthogonal Matching Pursuit; 2.5.3 Template Matching Algorithm; 2.5.4 Neighbour Embedding Methods; 2.5.4.1 Image Prediction Based on LLE; 2.6 Conclusions. | |
| 505 | 8 | |a 3 Image and Video Coding Standards3.1 Hybrid Video Compression; 3.2 Compression of 2D Video; 3.2.1 H.265/HEVC Standard; 3.2.1.1 HEVC Coding Structures; 3.2.1.2 Intra-Frame Prediction; 3.2.1.3 Inter-Frame Prediction; 3.2.1.4 Transform and Quantisation; 3.2.1.5 Entropy Coding; 3.2.1.6 In-Loop Filters; 3.2.1.7 HEVC Screen Content Coding Extensions; 3.2.2 Experimental Results; 3.3 Compression of 3D Video; 3.3.1 3D Video Systems; 3.3.1.1 Stereo Video; 3.3.1.2 Multiview Video; 3.3.1.3 Multiview Video+Depth; 3.3.1.4 Holoscopic Video; 3.3.2 3D Video Coding Standards. | |
| 505 | 8 | |a 3.3.2.1 Dependent View Coding Tools3.3.2.2 Depth Map Coding Tools; 3.3.3 Experimental Results; 3.4 Conclusions; 4 Compression of Depth Maps Using Predictive Coding; 4.1 Overview of Intra Techniques for Depth Map Coding; 4.1.1 Directional Intra Prediction; 4.1.2 Depth Modelling Modes; 4.1.3 Depth Lookup Table; 4.1.4 Segment-Wise DC Coding; 4.1.5 Single Depth Intra Mode; 4.1.6 View Synthesis Optimisation; 4.2 Overview of Predictive Depth Coding; 4.3 Coding Techniques of PDC Algorithm; 4.3.1 Flexible Block Partitioning; 4.3.2 Directional Intra Prediction Framework. | |
| 505 | 8 | |a 4.3.3 Constrained Depth Modelling Mode4.3.4 Residual Signal Coding; 4.3.5 Bitstream Syntax and Context Modelling; 4.4 PDC Encoder Control; 4.5 Experimental Results; 4.5.1 Evaluation of PDC Algorithm for Intra Coding; 4.5.2 Evaluation of PDC Algorithm Using VSO Metric; 4.5.3 Evaluation of PDC Algorithm Combined with 3D-HEVC Standard; 4.6 Conclusions; 5 Sparse Representation Methods for Image Prediction; 5.1 3D Holoscopic Image Coding Using LLE-Based Prediction; 5.1.1 Proposed HEVC Encoder Using LLE-Based Prediction; 5.1.2 Experimental Results; 5.2 The Sparse-LSP Method for Intra Prediction. | |
| 504 | |a Includes bibliographical references and index. | ||
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a 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. Presents a state-of-the-art review of existing prediction technologies for compression of both 2D and 3D multimedia content; Discusses the most recent advances beyond the current, standardized technologies for image and video compression, such as using the HEVC standard in the context of natural images, 3D and Light Field content; Includes new prediction methods based on alternative techniques and concepts, including flexible block partitioning, linear prediction, sparse representation. | ||
| 590 | |a SpringerLink |b Springer Complete eBooks | ||
| 650 | 0 | |a Image compression. | |
| 650 | 0 | |a Coding theory. | |
| 650 | 0 | |a Predictive control. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Lucas, Luís Filipe Rosário, |e author. | |
| 700 | 1 | |a Rodrigues, Nuno Miguel Morais, |e author. | |
| 700 | 1 | |a Pagliari, Carla Liberal, |e author. | |
| 700 | 1 | |a Da Silva, Eduardo A. B. |q (Eduardo Antônio Barros), |d 1963- |e author. | |
| 700 | 1 | |a Faira, Sérgio Manuel Maciel de, |e author. | |
| 776 | 0 | 8 | |i Print version: |t Efficient predictive algorithms for image compression. |d Cham, Switzerland : Springer, 2017 |z 3319511793 |z 9783319511795 |w (OCoLC)964292585 |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-3-319-51180-1 |y Plný text |
| 992 | |c NTK-SpringerENG | ||
| 999 | |c 99803 |d 99803 | ||
| 993 | |x NEPOSILAT |y EIZ | ||
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