Model-Based Joint Bit Allocation Between Geometry and Color for Video-Based 3D Point Cloud Compression

被引:44
|
作者
Liu, Qi [1 ,2 ]
Yuan, Hui [2 ,3 ]
Hou, Junhui [4 ]
Hamzaoui, Raouf [5 ]
Su, Honglei [6 ]
机构
[1] Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Peoples R China
[2] Shandong Univ, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
[3] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China
[4] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
[5] De Montfort Univ, Sch Engn & Sustainable Dev, Leicester LE1 9BH, Leics, England
[6] Qingdao Univ, Sch Elect Informat, Qingdao 266071, Peoples R China
基金
中国国家自然科学基金;
关键词
Bit allocation; distortion-quantization (D-Q) model; point cloud compression; rate-distortion optimization (RDO); rate-quantization (R-Q) model;
D O I
10.1109/TMM.2020.3023294
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry, and color distortions. Finding an optimal allocation of the total bitrate between the geometry coder, and the color coder is a challenging task due to the large number of possible solutions. To solve this bit allocation problem, we first propose analytical distortion, and rate models for the geometry, and color information. Using these models, we formulate the joint bit allocation problem as a constrained convex optimization problem, and solve it with an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66% of its time complexity.
引用
收藏
页码:3278 / 3291
页数:14
相关论文
共 50 条
  • [1] Video-based point cloud compression artifact removal based on the geometry video enhancement
    Wu, Fan
    Shen, Liquan
    Chen, Tianyi
    Wang, Feifeng
    JOURNAL OF ELECTRONIC IMAGING, 2024, 33 (02)
  • [2] 3D Motion Estimation and Compensation Method for Video-Based Point Cloud Compression
    Kim, Junsik
    Im, Jiheon
    Rhyu, Sungryeul
    Kim, Kyuheon
    IEEE ACCESS, 2020, 8 : 83538 - 83547
  • [3] Advanced 3D Motion Prediction for Video-Based Dynamic Point Cloud Compression
    Li, Li
    Li, Zhu
    Zakharchenko, Vladyslav
    Chen, Jianle
    Li, Houqiang
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 289 - 302
  • [4] Model-Based Encoding Parameter Optimization for 3D Point Cloud Compression
    Liu, Qi
    Yuan, Hui
    Hou, Junhui
    Liu, Hao
    Hamzaoui, Raouf
    2018 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2018, : 1981 - 1986
  • [5] Deep Learning Geometry Compression Artifacts Removal for Video-Based Point Cloud Compression
    Jia, Wei
    Li, Li
    Li, Zhu
    Liu, Shan
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 129 (11) : 2947 - 2964
  • [6] Deep Learning Geometry Compression Artifacts Removal for Video-Based Point Cloud Compression
    Wei Jia
    Li Li
    Zhu Li
    Shan Liu
    International Journal of Computer Vision, 2021, 129 : 2947 - 2964
  • [7] Model-Based Joint Bit Allocation Between Texture Videos and Depth Maps for 3-D Video Coding
    Yuan, Hui
    Chang, Yilin
    Huo, Junyan
    Yang, Fuzheng
    Lu, Zhaoyang
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2011, 21 (04) : 485 - 497
  • [8] Improved grid refine segmentation for 3D point cloud in video-based point cloud compression (V-PCC)
    Lin, Ting-Lan
    Lin, Ching-Hsuan
    Chiou, Yih-Shyh
    Chen, Shih-Lun
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (23) : 62701 - 62720
  • [9] Spatially Scalable Video-Based Point Cloud Compression
    Li, Shanshan
    Li, Li
    Liu, Dong
    Li, Houqiang
    2022 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS 22), 2022, : 3135 - 3139
  • [10] Video-Based Point Cloud Compression Artifact Removal
    Akhtar, Anique
    Gao, Wen
    Li, Li
    Li, Zhu
    Jia, Wei
    Liu, Shan
    IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 24 : 2866 - 2876