Cargando…
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...
Autores principales: | , , , , |
---|---|
Lenguaje: | eng |
Publicado: |
Springer
2017
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-319-51180-1 http://cds.cern.ch/record/2253866 |
_version_ | 1780953581642317824 |
---|---|
author | Rosário Lucas, Luís Filipe Barros da Silva, Eduardo Antônio Maciel de Faria, Sérgio Manuel Morais Rodrigues, Nuno Miguel Liberal Pagliari, Carla |
author_facet | Rosário Lucas, Luís Filipe Barros da Silva, Eduardo Antônio Maciel de Faria, Sérgio Manuel Morais Rodrigues, Nuno Miguel Liberal Pagliari, Carla |
author_sort | Rosário Lucas, Luís Filipe |
collection | CERN |
description | 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. |
id | cern-2253866 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
publisher | Springer |
record_format | invenio |
spelling | cern-22538662021-04-21T19:19:27Zdoi:10.1007/978-3-319-51180-1http://cds.cern.ch/record/2253866engRosário Lucas, Luís FilipeBarros da Silva, Eduardo AntônioMaciel de Faria, Sérgio ManuelMorais Rodrigues, Nuno MiguelLiberal Pagliari, CarlaEfficient predictive algorithms for image compressionEngineeringThis 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.Springeroai:cds.cern.ch:22538662017 |
spellingShingle | Engineering Rosário Lucas, Luís Filipe Barros da Silva, Eduardo Antônio Maciel de Faria, Sérgio Manuel Morais Rodrigues, Nuno Miguel Liberal Pagliari, Carla Efficient predictive algorithms for image compression |
title | Efficient predictive algorithms for image compression |
title_full | Efficient predictive algorithms for image compression |
title_fullStr | Efficient predictive algorithms for image compression |
title_full_unstemmed | Efficient predictive algorithms for image compression |
title_short | Efficient predictive algorithms for image compression |
title_sort | efficient predictive algorithms for image compression |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-3-319-51180-1 http://cds.cern.ch/record/2253866 |
work_keys_str_mv | AT rosariolucasluisfilipe efficientpredictivealgorithmsforimagecompression AT barrosdasilvaeduardoantonio efficientpredictivealgorithmsforimagecompression AT macieldefariasergiomanuel efficientpredictivealgorithmsforimagecompression AT moraisrodriguesnunomiguel efficientpredictivealgorithmsforimagecompression AT liberalpagliaricarla efficientpredictivealgorithmsforimagecompression |