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Multifocal multiview imaging and data compression based on angular-focal-spatial representation
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DOI：10.1364/OL.505496.png)
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Multifocal multiview (MFMV) is an emerging highdimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high -dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first -of -its -kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits interview, inter -stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved. (c) 2024 Optica Publishing Group
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3.3
论文数:
4.0W
被引数:
7.6W
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