ZTE Communications ›› 2023, Vol. 21 ›› Issue (4): 29-37.DOI: 10.12142/ZTECOM.202304004
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收稿日期:2023-10-07出版日期:2023-12-07发布日期:2023-12-07
Lossy Point Cloud Attribute Compression with Subnode-Based Prediction
YIN Qian1, ZHANG Xinfeng2, HUANG Hongyue1, WANG Shanshe1, MA Siwei1(
)
- 1.School of Computer Science, Peking University, Beijing 100871, China
2.School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing 100049, China
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Received:2023-10-07Online:2023-12-07Published:2023-12-07 -
About author:YIN Qian received her MS degree in signal and information processing from University of Electronic Science and Technology of China in 2021. She is currently pursuing a PhD degree in computer science at Peking University, China. She is actively participating in the research work of the Audio Video Coding Standard (AVS) Workgroup of China and Moving Picture Experts Group (MPEG). Her research interests include video and point cloud compression.
ZHANG Xinfeng received his BS degree in computer science from Hebei University of Technology, China in 2007 and PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences in 2014. From 2014 to 2017, he was a research fellow with the Rapid-Rich Object Search Lab, Nanyang Technological University, Singapore. From October 2017 to October 2018, he was a postdoctoral fellow with the School of Electrical Engineering System, University of Southern California, Los Angeles, USA. From December 2018 to August 2019, he was a research fellow with the Department of Computer Science, City University of Hong Kong, China. He is currently an assistant professor with the School of Computer Science and Technology, University of Chinese Academy of Sciences. He has authored more than 150 refereed journal/conference papers. His research interests include video compression and processing, image/video quality assessment, and 3D point cloud processing.
HUANG Hongyue received his BE degree in communication engineering from Beijing University of Posts and Telecommunications, China in 2015, MS degree in computer science from the Technical University of Berlin (TUB), Germany in 2018, and PhD degree in computer science from the Free University of Brussels (VUB), Belgium in 2021. He is currently a postdoctoral researcher with the National Engineering Research Center of Visual Technology, Peking University, China. His research interests include inventing and optimizing deep-learning-based compression methods for 2D images/videos and 3D visual content such as immersive videos, point clouds, and light field images.
WANG Shanshe received his BS degree from the Department of Mathematics, Heilongjiang University, China in 2004, MS degree in computer software and theory from Northeast Petroleum University, China in 2010, and PhD degree in computer science from the Harbin Institute of Technology, China. He held a postdoctoral position with Peking University, China from 2016 to 2018. He is currently an associate researcher with the School of Electronics Engineering and Computer Science, Institute of Digital Media, Peking University. His current research interests include video compression and image and video quality assessment.
MA Siwei (swma@stu.pku.edu.cn) received his BS degree from Shandong Normal University, China in 1999, and PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences in 2005. He held a postdoctoral position with the University of Southern California, Los Angeles, USA from 2005 to 2007. He is currently a professor with the School of Electronics Engineering and Computer Science, Institute of Digital Media, Peking University, China. He has authored over 300 technical articles in refereed journals and proceedings in image and video coding, video processing, video streaming, and transmission. He served/serves as an associate editor for the IEEE Transactions on Circuits and Systems for Video Technology and Journal of Visual Communication and Image Representation. -
Supported by:China Postdoctoral Science Foundation(2022M720234);the National Natural Science Foundation(62071449)
引用本文
. [J]. ZTE Communications, 2023, 21(4): 29-37.
YIN Qian, ZHANG Xinfeng, HUANG Hongyue, WANG Shanshe, MA Siwei. Lossy Point Cloud Attribute Compression with Subnode-Based Prediction[J]. ZTE Communications, 2023, 21(4): 29-37.
Table 1 Common test conditions in G-PCC
| G-PCC Platform | Conditions | Datasets | |||
|---|---|---|---|---|---|
| C1 | C2 | Cat1 | Cat2 | Cat3 | |
| TMC13 | √ | √ | √ | √ | |
| GeS-TM | √ | √ | √ | ||
Table 1 Common test conditions in G-PCC
| G-PCC Platform | Conditions | Datasets | |||
|---|---|---|---|---|---|
| C1 | C2 | Cat1 | Cat2 | Cat3 | |
| TMC13 | √ | √ | √ | √ | |
| GeS-TM | √ | √ | √ | ||
Table 2 Performance of the proposed method against TMC13-v22.0 under C1 and C2 configurations
| Dataset Category | C1 End-to-End BD-Attribute Rate/% | C2 End-to-End BD-Attribute Rate/% | ||||||
|---|---|---|---|---|---|---|---|---|
| Luma | Chroma Cb | Chroma Cr | Reflectance | Luma | Chroma Cb | Chroma Cr | Reflectance | |
| Solid average | -0.4 | -0.3 | -0.4 | / | -0.2 | -0.3 | -0.2 | / |
| Dense average | -0.2 | -0.2 | -0.2 | / | -0.2 | -0.5 | -0.1 | / |
| Sparse average | -0.2 | -0.2 | -0.1 | / | -0.2 | -0.1 | -0.3 | / |
| Scant average | -0.2 | -0.2 | -0.3 | / | -0.2 | -0.3 | -0.2 | / |
| Am-fused average | -0.3 | -1.2 | -1.1 | -1.1 | -0.1 | -0.6 | -0.7 | -0.2 |
| Am-frame spinning average | / | / | / | -0.3 | / | / | / | -0.2 |
| Am-frame non-spinning average | / | / | / | -0.6 | / | / | / | -0.2 |
| Overall average | -0.2 | -0.3 | -0.3 | -0.5 | -0.2 | -0.3 | -0.2 | -0.2 |
| Average encoding/decoding time/% | 102/103 | 100/107 | ||||||
Table 2 Performance of the proposed method against TMC13-v22.0 under C1 and C2 configurations
| Dataset Category | C1 End-to-End BD-Attribute Rate/% | C2 End-to-End BD-Attribute Rate/% | ||||||
|---|---|---|---|---|---|---|---|---|
| Luma | Chroma Cb | Chroma Cr | Reflectance | Luma | Chroma Cb | Chroma Cr | Reflectance | |
| Solid average | -0.4 | -0.3 | -0.4 | / | -0.2 | -0.3 | -0.2 | / |
| Dense average | -0.2 | -0.2 | -0.2 | / | -0.2 | -0.5 | -0.1 | / |
| Sparse average | -0.2 | -0.2 | -0.1 | / | -0.2 | -0.1 | -0.3 | / |
| Scant average | -0.2 | -0.2 | -0.3 | / | -0.2 | -0.3 | -0.2 | / |
| Am-fused average | -0.3 | -1.2 | -1.1 | -1.1 | -0.1 | -0.6 | -0.7 | -0.2 |
| Am-frame spinning average | / | / | / | -0.3 | / | / | / | -0.2 |
| Am-frame non-spinning average | / | / | / | -0.6 | / | / | / | -0.2 |
| Overall average | -0.2 | -0.3 | -0.3 | -0.5 | -0.2 | -0.3 | -0.2 | -0.2 |
| Average encoding/decoding time/% | 102/103 | 100/107 | ||||||
Table 3 Performance of the proposed method against GeSTM-v2.0 under C1 and C2 configurations
| Dataset Category | C1 BD-Rate/% | C2 BD-Rate/% | ||||
|---|---|---|---|---|---|---|
| L | Cb | Cr | L | Cb | Cr | |
| Cat2-A average | -0.4 | -0.5 | -0.5 | -0.3 | -0.3 | -0.4 |
| Cat2-B average | -0.3 | -0.3 | -0.3 | -0.2 | -0.2 | -0.2 |
| Cat2-C average | -0.5 | -0.4 | -0.5 | -0.4 | -0.4 | -0.3 |
| Overall average | -0.4 | -0.4 | -0.5 | -0.3 | -0.3 | -0.4 |
| Avgerage encoding/decoding time (%) | 106/109 | 101/110 | ||||
Table 3 Performance of the proposed method against GeSTM-v2.0 under C1 and C2 configurations
| Dataset Category | C1 BD-Rate/% | C2 BD-Rate/% | ||||
|---|---|---|---|---|---|---|
| L | Cb | Cr | L | Cb | Cr | |
| Cat2-A average | -0.4 | -0.5 | -0.5 | -0.3 | -0.3 | -0.4 |
| Cat2-B average | -0.3 | -0.3 | -0.3 | -0.2 | -0.2 | -0.2 |
| Cat2-C average | -0.5 | -0.4 | -0.5 | -0.4 | -0.4 | -0.3 |
| Overall average | -0.4 | -0.4 | -0.5 | -0.3 | -0.3 | -0.4 |
| Avgerage encoding/decoding time (%) | 106/109 | 101/110 | ||||
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