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Edge-aware spatial-frequency extrapolation for consecutive block loss
To improve the spatial error concealment (SEC) for consecutive block loss, an edge-aware spatial-frequency extrapolation (ESFE) algorithm and its edge-guided parametric model are proposed by selectively incorporating the Hough-based edge synthesis into the frequency-based extrapolation architecture....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4864749/ https://www.ncbi.nlm.nih.gov/pubmed/27247889 http://dx.doi.org/10.1186/s40064-016-2213-6 |
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author | Liu, Hao Wang, Dengcheng Wang, Bing Li, Kangda Tang, Hainie |
author_facet | Liu, Hao Wang, Dengcheng Wang, Bing Li, Kangda Tang, Hainie |
author_sort | Liu, Hao |
collection | PubMed |
description | To improve the spatial error concealment (SEC) for consecutive block loss, an edge-aware spatial-frequency extrapolation (ESFE) algorithm and its edge-guided parametric model are proposed by selectively incorporating the Hough-based edge synthesis into the frequency-based extrapolation architecture. The dominant edges that cross the missing blocks are firstly identified by the Canny detector, and then the robust Hough transformation is utilized to systematically connect these discontinuous edges. During the generation of edge-guided parametric model, the synthesized edges are utilized to divide the missing blocks into the structure-preserving regions, and thus the residual error is reliably reduced. By successively minimizing the weighted residual error and updating the parametric model, the known samples are approximated by a set of basis functions which are distributed in a region containing both known and unknown samples. Compared with other state-of-the-art SEC algorithms, experimental results show that the proposed ESFE algorithm can achieve better reconstruction quality for consecutive block loss while keeping relatively moderate computational complexity. |
format | Online Article Text |
id | pubmed-4864749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-48647492016-05-31 Edge-aware spatial-frequency extrapolation for consecutive block loss Liu, Hao Wang, Dengcheng Wang, Bing Li, Kangda Tang, Hainie Springerplus Research To improve the spatial error concealment (SEC) for consecutive block loss, an edge-aware spatial-frequency extrapolation (ESFE) algorithm and its edge-guided parametric model are proposed by selectively incorporating the Hough-based edge synthesis into the frequency-based extrapolation architecture. The dominant edges that cross the missing blocks are firstly identified by the Canny detector, and then the robust Hough transformation is utilized to systematically connect these discontinuous edges. During the generation of edge-guided parametric model, the synthesized edges are utilized to divide the missing blocks into the structure-preserving regions, and thus the residual error is reliably reduced. By successively minimizing the weighted residual error and updating the parametric model, the known samples are approximated by a set of basis functions which are distributed in a region containing both known and unknown samples. Compared with other state-of-the-art SEC algorithms, experimental results show that the proposed ESFE algorithm can achieve better reconstruction quality for consecutive block loss while keeping relatively moderate computational complexity. Springer International Publishing 2016-05-11 /pmc/articles/PMC4864749/ /pubmed/27247889 http://dx.doi.org/10.1186/s40064-016-2213-6 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Liu, Hao Wang, Dengcheng Wang, Bing Li, Kangda Tang, Hainie Edge-aware spatial-frequency extrapolation for consecutive block loss |
title | Edge-aware spatial-frequency extrapolation for consecutive block loss |
title_full | Edge-aware spatial-frequency extrapolation for consecutive block loss |
title_fullStr | Edge-aware spatial-frequency extrapolation for consecutive block loss |
title_full_unstemmed | Edge-aware spatial-frequency extrapolation for consecutive block loss |
title_short | Edge-aware spatial-frequency extrapolation for consecutive block loss |
title_sort | edge-aware spatial-frequency extrapolation for consecutive block loss |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4864749/ https://www.ncbi.nlm.nih.gov/pubmed/27247889 http://dx.doi.org/10.1186/s40064-016-2213-6 |
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