Autofocus on Depth of Interest for 3D Image Coding

Abstract : 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.
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Khouloud Samrouth, Olivier Déforges, Yi Liu, Mohamad Khalil, Wassim El Falou. Autofocus on Depth of Interest for 3D Image Coding. Journal of electrical and computer engineering, 2017, 2017, pp.9689715. ⟨10.1155/2017/9689715⟩. ⟨hal-01502344⟩

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