Yaofeng Che
ABSTRACT
With the rapid development of 3D sensing technology, point cloud compression has become a research hotspot in the field of multimedia. Geometry Based Point Cloud Compression (G-PCC) developed by MPEG 3DG is one of the most important frameworks for point cloud compression. Recently, the Level of Detail (LOD) method for attribute data coding in G-PCC has received a lot of attention. In the prediction transformation and lifting transformation methods of G-PCC, the fixed sampling distance is used to divide the level of detail, and the same quantization step is used for the same level. However, point clouds usually have different texture complexity in different local regions. In this paper, we propose an adaptive sampling and quantization method based on the texture complexity of point cloud to improve the attribute compression performance. Experimental results show that the proposed method can achieve better coding performance with maintaining more detail information compared with that of the MPEG G-PCC reference software.
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Index Terms
- Point Cloud Attribute Compression based on Adaptive Sampling and Quantization
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