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
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.