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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 9689715, 15 pages
Research Article

Autofocus on Depth of Interest for 3D Image Coding

1UEB, CNRS UMR 6164, IETR Lab, INSA de Rennes 20, Avenue des Buttes de Coesmes, CS 70839, 35708 Rennes, France
2Faculty of Engineering I, Lebanese University, Tripoli, Lebanon

Correspondence should be addressed to Khouloud Samrouth; moc.liamg@tuormas.duoluohk

Received 13 October 2016; Accepted 30 January 2017; Published 22 February 2017

Academic Editor: Panajotis Agathoklis

Copyright © 2017 Khouloud Samrouth et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For some 3D applications, one may want to focus on a specific depth zone representing a region of interest in the scene. In this context, we introduce a new functionality called “autofocus” for 3D image coding, exploiting the depth map as an additional semantic information provided by the 3D sequence. The method is based on a joint “Depth of Interest” (DoI) extraction and coding scheme. First, the DoI extraction scheme consists of a precise extraction of objects located within a DoI zone, given by the viewer or deduced from an analysis process. Then, the DoI coding scheme provides a higher quality for the objects in the DoI at the expense of other depth zones. The local quality enhancement supports both higher SNR and finer resolution. The proposed scheme embeds the Locally Adaptive Resolution (LAR) codec, initially designed for 2D images. The proposed DoI scheme is developed without modifying the global coder framework, and the DoI mask is not transmitted, but it is deduced at the decoder. Results showed that our proposed joint DoI extraction and coding scheme provide a high correlation between texture objects and depth. This consistency avoids the distortion along objects contours in depth maps and those of texture images and synthesized views.