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Clustering and DCT Based Color Point Cloud Compression

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Abstract

In this paper, a new point cloud compression method is proposed. The 3D color point cloud is firstly mean-shift clustered into many homogeneous blocks based on the similar spatial (XYZ) information of each point. Based on the RANdom SAmple Consensus (RANSAC) algorithm, those points being clustered in the same block are fitted by a 3D plane and all these points belonging to the same block are projected to this corresponding plane. For every plane an optimal rectangle bounding box is identified and is divided into n × n grids, the color (RGB) information associated with each grid point is replaced by the average of RGB values of all the projected points falling in this grid. Finally, a 2D DCT (Discrete Cosine Transform) transform is performed on these n × n grids points. The compressing ratio can reach 32 with negligible spatial and color distortion.

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Acknowledgments

This paper is partially supported by the National Nature Science Foundation of China (No.61373084). The National High Technology Research and Development Program of China (863 Program) under Grant No.2013AA01A603-03, the Innovation Program of Shanghai Municipal Education Commission (No.14YZ011).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Ximin Zhang, Wanggen Wan & Xuandong An

  2. Institute of Physics and Electronic Engineering, Henan Institute of Education, Zhengzhou, China

    Ximin Zhang

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  1. Ximin Zhang
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  2. Wanggen Wan
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  3. Xuandong An
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Correspondence to Ximin Zhang.

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Zhang, X., Wan, W. & An, X. Clustering and DCT Based Color Point Cloud Compression. J Sign Process Syst 86, 41–49 (2017). https://doi.org/10.1007/s11265-015-1095-0

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  • DOI: https://doi.org/10.1007/s11265-015-1095-0

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