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Edge-aware wedgelet estimation for depth maps compression
Sebai, Dorsaf (University of Manouba (Tunísia). Cristal Laboratoy, National School of Computer Science)
Chaieb, Faten (University of Manouba (Tunísia). Cristal Laboratoy, National School of Computer Science)
Ghorbel, Faouzi (University of Manouba (Tunísia). Cristal Laboratoy, National School of Computer Science)

Data: 2016
Resum: In recent years, Multi-view Video plus Depth (MVD) compression has received much attention thanks to its relevance to free viewpoint applications needs. An efficient compression, that causes the least distortion without excessive rate and complexity increase, becomes a must particularly for depth maps. These latter can be compressed efficiently by the 3D extension of High Efficiency Video Coding (3D-HEVC), which has explored wedgelets. Such functions lead to significant Rate-Distortion tradeoffs. However, they require a very large computational complexity involved by the exhaustive search used for the estimation of the wedgelet subdivision line. In this paper, we propose a rapid localization of this latter using an edge detection approach. The experimental results show that the proposed approach allows an important gain in terms of encoding delay, while providing better depth maps and synthesized views quality compared to the exhaustive search approach.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: article ; recerca ; publishedVersion
Matèria: Depth maps ; 3D-HEVC ; Wedge lets ; Subdivision line estimation ; Exhaustive search ; Edge detection ; Synthesized views
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 15 núm. 1 (2016) , p. 14-26 (Regular Issue) , ISSN 1577-5097

Adreça original:
DOI: 10.5565/rev/elcvia.807

13 p, 327.3 KB

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