.png){kind=icon}
返回
Efficient Quadtree Search for HEVC Coding Units for V-PCC
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3118806.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Dynamic point clouds (DPC) are new media storage formats that allow end-users to watch objects/scenes in a three-dimensional (3D) sense. It can be displayed from different angles throughout time. However, the raw size of a point cloud is huge because there can be millions of points (each containing color triplet and location triplet information) in a point cloud, and there can be multiple point clouds in a DPC. Video-based point cloud compression (V-PCC) is developed to project a 3D point cloud to 2D images: attribute, geometry, and occupancy images. After padding, the 2D images are compressed using the well-established high-efficiency video coding (HEVC). In this study, we first employ an occupancy image to propose a blocky occupancy flag (BOF), to denote the occupancy information on a block basis''. For coding attribute and geometry images, we use a BOF to develop a fast coding unit (CU) algorithm for early termination of the CU search recursion. We also utilize the geometry images to calculate the 2D and 3D information of each pixel, for 2D/3D spatial homogeneity of the pixels to design fast CU decision. In addition, we proposed a modified rate-distortion optimization for different color components considering the picture order count (POC) structure in HEVC/V-PCC. Finally, we propose an HEVC input pixel modification method based on a BOF to reduce the unnecessary information to be coded for attribute images. Compared with the state-of-the-art fast V-PCC encoding method, the proposed work outperforms by up to 2.31% in Bjontegaard delta bit rates (BDBR) (with very slight loss by only up to 0.38%), and improves the time saving performances by up to 7.84% for two different testing datasets.
Keyword:
Video-based point cloud compression (V-PCC)
dynamic point cloud (DPC)
high efficiency video coding (HEVC)
fast coding unit (CU) decision algorithm
occupancy map
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏Fast Multi-Type Tree Partitioning for Versatile Video Coding Using a Lightweight Neural Network
PREAI
分享收藏Advanced 3D Motion Prediction for Video-Based Dynamic Point Cloud Compression基于视频的动态点云压缩的高级3D运动预测
PREAI
分享收藏
查看更多内容