IoT as a Service. 5th EAI International Conference, IoTaaS 2019, Xi’an, China, November 16-17, 2019, Proceedings

Research Article

Compressive-Sensing Based Codec of the Y Color Component for Point Cloud

  • @INPROCEEDINGS{10.1007/978-3-030-44751-9_22,
        author={Weiwei Wang and Hui Yuan and Hao Liu and Qi Liu},
        title={Compressive-Sensing Based Codec of the Y Color Component for Point Cloud},
        proceedings={IoT as a Service. 5th EAI International Conference, IoTaaS 2019, Xi’an, China, November 16-17, 2019, Proceedings},
        proceedings_a={IOTAAS},
        year={2020},
        month={6},
        keywords={Compressive sensing K-SVD algorithm Pointcloud},
        doi={10.1007/978-3-030-44751-9_22}
    }
    
  • Weiwei Wang
    Hui Yuan
    Hao Liu
    Qi Liu
    Year: 2020
    Compressive-Sensing Based Codec of the Y Color Component for Point Cloud
    IOTAAS
    Springer
    DOI: 10.1007/978-3-030-44751-9_22
Weiwei Wang1,*, Hui Yuan1,*, Hao Liu1,*, Qi Liu1,*
  • 1: Shandong University
*Contact email: wangweiwei@mail.sdu.edu.cn, yuanhui0325@gmail.com, liuhaoxb@gmail.com, sdqi.liu@gmail.com

Abstract

The point cloud obtained by the 3D laser scanner contains a very large amount of data, in order to transmit the point cloud data as much as possible with the limited bandwidth, the effective compression of point cloud data has become a problem that needs to be solved urgently nowadays. In this paper, we use the compressive sensing theory to compress and reconstruct one of the point features, that is, the Y color component, served as the signal. We also use the K-SVD algorithm to explore the signal’s sparsity according to its unique structural features, the K-SVD algorithm can learns a sparse basis matrix that is common to all point cloud models used in our experiments. For experimental results, we use rate-distortion metric. The results show that for each point cloud model, our method can achieve a higher probability to reconstruct the original data after compressed.