Scalable Object Encoding Using Multiplicative Multilinear Inter-camera Prediction in the Context of Free View 3D Video

Abstract : Recent advancements in 3D television allow for the capture of scene depth from multiple cameras and the interactive selection of view point and direction within a certain range, the so-called Free Viewpoint Video (FVV). State-of-the-art video codecs such as H.264/AVC exploit the large amount of inter-view statistical dependencies by combined temporal and inter-view prediction, i.e. prediction from temporally neighboring images as well as from images in adjacent views. This is known as Multi-view Video Coding (MVC). We propose herein an alternative object oriented video coding scheme for multi-view video with associated multiple depth data (N-video plus N-depth). A structure that we call a Multi-view Video Plane (MVP) is introduced. Object planes associated with a certain view are approximated as multilinear components of an image that are projected onto other views in a tensor-like fashion. The order of the tensor equals the number of multiple views. The coefficients of the tensor subspace projections as well as the updates of the multi-linear components (object-planes) are quantized and transmitted in the MPEG stream. Motion-compensated prediction is carried out in order to transmit the residual object planes (P-frames) using conventional MPEG algorithms.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.414-424, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_43〉
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Ioannis Stephanakis, George Anastassopoulos. Scalable Object Encoding Using Multiplicative Multilinear Inter-camera Prediction in the Context of Free View 3D Video. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.414-424, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_43〉. 〈hal-01521401〉

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