Reconstruction of lost depth data in multiview video-plus-depth communications using geometric transforms |
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| Affiliation: | 1. Universidade de Trás-os-Montes e Alto Douro/ECT Engineering Department, Portugal;2. IEETA, UA Campus, Portugal;3. Instituto de Telecomunicações, Portugal;4. Instituto Politécnico Leiria, ESTG, Portugal;1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. University Multimedia, Malaysia;3. The Chinese University of Hong Kong, Hong Kong;1. Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Bangladesh;2. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh;1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Information, Qilu University of Technology, Jinan 250353, China;1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China;2. Department of Computer Science, Xiamen University, Xiamen 361005, China;3. Faculty of Science and Technology, University of Macau, Macao;4. Institute of Information Science, Academia Sinica, Taipei 115, Taiwan |
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| Abstract: | This paper addresses depth data recovery in multiview video-plus-depth communications affected by transmission errors and/or packet loss. The novel aspects of the proposed method rely on the use of geometric transforms and warping vectors, capable of capturing complex motion and view-dependent deformations, which are not efficiently handled by traditional motion and/or disparity compensation methods. By exploiting the geometric nature of depth information, a region matching approach combined with depth contour reconstruction is devised to achieve accurate interpolation of arbitrary shapes within lost regions of depth maps. The simulation results show that, for different packet loss rates, up to 20%, the depth maps recovered by the proposed method produce virtual views with better quality than existing methods based on motion information and spatial interpolation. An average PSNR gain of 1.48 dB is obtained in virtual views synthesised from depth maps using the proposed method. |
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| Keywords: | Multiview video-plus-depth Depth map reconstruction Depth loss Error concealment Geometric transforms |
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