Joint coding of multi-view video and corresponding depth map
In this paper, we propose a joint coding scheme for both multi-view video and its corresponding depth map. After we synthesize a virtual image for the target view using adjacent view images and their depth information, we apply a view interpolation prediction (VIP) method for both multi-view video c...
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creator | Sang-Tae Na Kwan-Jung Oh Yo-Sung Ho |
description | In this paper, we propose a joint coding scheme for both multi-view video and its corresponding depth map. After we synthesize a virtual image for the target view using adjacent view images and their depth information, we apply a view interpolation prediction (VIP) method for both multi-view video coding and its depth data coding. In order to improve the synthesized virtual view, we also propose a hole filling method that can compensate for empty regions caused by the 3D warping operation. With the proposed algorithm, we have obtained approximately 0.65 dB of the PSNR gain on average for the multi-view depth data, and 0.17 dB of the PSNR gain for the multi-view video data, compared to JMVM 1.0. |
doi_str_mv | 10.1109/ICIP.2008.4712293 |
format | Conference Proceeding |
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After we synthesize a virtual image for the target view using adjacent view images and their depth information, we apply a view interpolation prediction (VIP) method for both multi-view video coding and its depth data coding. In order to improve the synthesized virtual view, we also propose a hole filling method that can compensate for empty regions caused by the 3D warping operation. 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After we synthesize a virtual image for the target view using adjacent view images and their depth information, we apply a view interpolation prediction (VIP) method for both multi-view video coding and its depth data coding. In order to improve the synthesized virtual view, we also propose a hole filling method that can compensate for empty regions caused by the 3D warping operation. With the proposed algorithm, we have obtained approximately 0.65 dB of the PSNR gain on average for the multi-view depth data, and 0.17 dB of the PSNR gain for the multi-view video data, compared to JMVM 1.0.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2008.4712293</doi><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | 3D warping Automatic voltage control Cameras Depth data Filling Hole filling Image coding Interpolation Layout Multi-view video PSNR Transform coding Video coding Video compression View interpolation prediction |
title | Joint coding of multi-view video and corresponding depth map |
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