Name
Abstract | ||
|---|---|---|
3D point cloud compression (PCC) has been an attractive field with increasing applications in recent years. Moving Picture Experts Group (MPEG) is building an open standard for point cloud compression, consisting of two solutions, video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC). As an essential process in G-PCC, K-nearest neighbors (KNN) algorithm is adopted to perform intra prediction, which only considering distance-based local similarity but neglecting the overall geometric distribution of the neighbor set. In this paper, we propose an enhanced intra prediction scheme based on point-cloud geometric distribution. The centroid-based criterion is introduced to measure the uniformity of spatial distribution of predictive reference points. Our scheme is implemented in G-PCC reference software. Experimental results demonstrate that our proposed method can optimize the selection of predictors, which leads to better rate-distortion (R-D) performance than the G-PCC anchor on point cloud datasets under the common test conditions (CTC). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VCIP47243.2019.8966001 | 2019 IEEE Visual Communications and Image Processing (VCIP) |
Keywords | Field | DocType |
point cloud compression,MPEG Standard,G-PCC,enhanced intra prediction,geometric distribution | Open standard,Computer vision,Compression (physics),Common test conditions,Computer science,Algorithm,Mpeg standards,Artificial intelligence,Geometric distribution,Point cloud,Reference software,Centroid | Conference |
ISSN | ISBN | Citations |
1018-8770 | 978-1-7281-3724-7 | 1 |
PageRank | References | Authors |
0.39 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honglian Wei | 1 | 1 | 0.39 |
| Yiting Shao | 2 | 15 | 3.61 |
| Jing Wang | 3 | 1 | 1.74 |
| shan liu | 4 | 96 | 49.62 |
| Ge Li | 5 | 1 | 0.39 |