OCCUPANCY-MAP-BASED RATE DISTORTION OPTIMIZATION FOR VIDEO-BASED POINT CLOUD COMPRESSION

被引:0
|
作者
Li, Li [1 ]
Li, Zhu [1 ,4 ]
Liu, Shan [2 ]
Li, Houqiang [3 ]
机构
[1] Univ Missouri, Kansas City, MO 64110 USA
[2] Tencent Amer, Palo Alto, CA USA
[3] Univ Sci & Technol China, Hefei, Peoples R China
[4] Peng Cheng Lab, Shenzhen, Peoples R China
关键词
Occupancy map; Point cloud compression; Rate distortion optimization; Sample adaptive offset; Video-based point cloud compression; ATTRIBUTE COMPRESSION;
D O I
10.1109/icip.2019.8803233
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
The state-of-the-art video-based point cloud compression scheme projects the 3D point cloud to 2D patch by patch and organizes the patches into frames to compress them using the efficient video compression scheme. Such a scheme shows a good trade-off between the number of points projected and the video continuity to utilize the video compression scheme. However, some unoccupied pixels between different patches are compressed using almost the same quality with the occupied pixels, which will lead to the waste of lots of bits since the unoccupied pixels are useless for the reconstructed point cloud. In this paper, we propose to consider only the rate instead of the rate distortion cost for the unoccupied pixels during the rate distortion optimization process. The proposed scheme can be applied to both the geometry and attribute frames. The experimental results show that the proposed algorithm can achieve an average of 11:9% and 15:4% bitrate savings for the geometry and attribute, respectively.
引用
收藏
页码:3167 / 3171
页数:5
相关论文
共 50 条
  • [1] Occupancy-Map-Based Rate Distortion Optimization and Partition for Video-Based Point Cloud Compression
    Li, Li
    Li, Zhu
    Liu, Shan
    Li, Houqiang
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 31 (01) : 326 - 338
  • [2] Perceptually Weighted Rate Distortion Optimization for Video-Based Point Cloud Compression
    Zhang, Yun
    Ding, Keqin
    Li, Na
    Wang, Hanli
    Huang, Xiaoxia
    Kuo, C. -C. Jay
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 32 : 5933 - 5947
  • [3] Leveraging occupancy map to accelerate video-based point cloud compression
    Wang, Wenyu
    Ding, Gongchun
    Ding, Dandan
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2024, 104
  • [4] 3D-Distortion Based Rate Distortion Optimization for Video-Based Point Cloud Compression br
    Fu, Yihao
    Shen, Liquan
    Chen, Tianyi
    [J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2023, 17 (02): : 435 - 449
  • [5] Occupancy Map Guided Attributes Deblocking for Video-based Point Cloud Compression
    Chen, Peilin
    Wang, Shiqi
    Li, Zhu
    [J]. 2023 DATA COMPRESSION CONFERENCE, DCC, 2023, : 332 - 332
  • [6] Global Rate-distortion Optimization of Video-based Point Cloud Compression with Differential Evolution
    Yuan, Hui
    Hamzaoui, Raouf
    Neri, Ferrante
    Yang, Shengxiang
    Wang, Tingting
    [J]. IEEE MMSP 2021: 2021 IEEE 23RD INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP), 2021,
  • [7] Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression
    Yang, Runyu
    Yan, Ning
    Li, Li
    Liu, Dong
    Wu, Feng
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2020, : 479 - 482
  • [8] Occupancy map-based low complexity motion prediction for video-based point cloud compression
    Wang, Yihan
    Wang, Yongfang
    Cui, Tengyao
    Fang, Zhijun
    [J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2024, 100
  • [9] Rate Control for Video-Based Point Cloud Compression
    Li, Li
    Li, Zhu
    Liu, Shan
    Li, Houqiang
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 6237 - 6250
  • [10] Convolutional Neural Network-Based Occupancy Map Accuracy Improvement for Video-Based Point Cloud Compression
    Jia, Wei
    Li, Li
    Akhtar, Anique
    Li, Zhu
    Liu, Shan
    [J]. IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 24 : 2352 - 2365