{"title":"Fast Video-Based Point Cloud Compression Based on Early Termination and Transformer Model","authors":"Yihan Wang;Yongfang Wang;Tengyao Cui;Zhijun Fang","doi":"10.1109/TETCI.2024.3360290","DOIUrl":null,"url":null,"abstract":"Video-based Point Cloud Compression (V-PCC) was proposed by the Moving Picture Experts Group (MPEG) to standardize Point Cloud Compression (PCC). The main idea of V-PCC is to project the Dynamic Point Cloud (DPC) into auxiliary information, occupancy, geometry, and attribute videos for encoding utilizing High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC), etc. Compared with the previous PCC algorithms, V-PCC has achieved a significant improvement in compression efficiency. However, it is accompanied by substantial computational complexity. To solve this problem, this paper proposes a fast V-PCC method to decrease the coding complexity. Taking into account the coding characteristic of V-PCC, the geometry and attribute maps are first classified into occupied and unoccupied blocks. Moreover, we analyze Coding Unit (CU) splitting for geometry and attribute map. Finally, we propose fast V-PCC algorithms based on early termination algorithm and transformer model, in which the early termination method is proposed for low complexity blocks in the geometry and attribute map, and the transformer model-based fast method is designed to predict the optimal CU splitting modes for the occupied block of the attribute map. The proposed algorithms are implemented with typical DPC sequences on the Test Model Category 2 (TMC2). The experimental results imply that the average time of the proposed method can significantly reduce 56.39% and 55.10% in the geometry and attribute map, respectively, with negligible Bjontegaard-Delta bitrate (BD-rate) compared with the anchor method.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10438525/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Video-based Point Cloud Compression (V-PCC) was proposed by the Moving Picture Experts Group (MPEG) to standardize Point Cloud Compression (PCC). The main idea of V-PCC is to project the Dynamic Point Cloud (DPC) into auxiliary information, occupancy, geometry, and attribute videos for encoding utilizing High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC), etc. Compared with the previous PCC algorithms, V-PCC has achieved a significant improvement in compression efficiency. However, it is accompanied by substantial computational complexity. To solve this problem, this paper proposes a fast V-PCC method to decrease the coding complexity. Taking into account the coding characteristic of V-PCC, the geometry and attribute maps are first classified into occupied and unoccupied blocks. Moreover, we analyze Coding Unit (CU) splitting for geometry and attribute map. Finally, we propose fast V-PCC algorithms based on early termination algorithm and transformer model, in which the early termination method is proposed for low complexity blocks in the geometry and attribute map, and the transformer model-based fast method is designed to predict the optimal CU splitting modes for the occupied block of the attribute map. The proposed algorithms are implemented with typical DPC sequences on the Test Model Category 2 (TMC2). The experimental results imply that the average time of the proposed method can significantly reduce 56.39% and 55.10% in the geometry and attribute map, respectively, with negligible Bjontegaard-Delta bitrate (BD-rate) compared with the anchor method.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.