基于神经网络的V-PCC属性帧后处理滤波

IF 0.6 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Keiichiro TAKADA, Yasuaki TOKUMO, Tomohiro IKAI, Takeshi CHUJOH
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引用次数: 0

摘要

基于视频的点云压缩(V-PCC)利用视频压缩技术对密集的点云进行高效编码,以相对较小的计算负担提供最先进的压缩性能。V-PCC将三维点云数据转换成三种二维帧,即占用帧、几何帧和属性帧,并通过视频压缩进行编码。另一方面,由于视频压缩,这些帧的质量可能会下降。本文提出了一种基于自适应神经网络的属性帧后处理滤波器,以缓解属性帧的退化问题。在此基础上,研究了一种基于占用帧的训练方法。实验结果表明,Y、U和V的平均bd速率增益分别为3.0%、29.3%和22.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Network-Based Post-Processing Filter on V-PCC Attribute Frames
Video-based point cloud compression (V-PCC) utilizes video compression technology to efficiently encode dense point clouds providing state-of-the-art compression performance with a relatively small computation burden. V-PCC converts 3-dimensional point cloud data into three types of 2-dimensional frames, i.e., occupancy, geometry, and attribute frames, and encodes them via video compression. On the other hand, the quality of these frames may be degraded due to video compression. This paper proposes an adaptive neural network-based post-processing filter on attribute frames to alleviate the degradation problem. Furthermore, a novel training method using occupancy frames is studied. The experimental results show average BD-rate gains of 3.0%, 29.3% and 22.2% for Y, U and V respectively.
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来源期刊
IEICE Transactions on Information and Systems
IEICE Transactions on Information and Systems 工程技术-计算机:软件工程
CiteScore
1.80
自引率
0.00%
发文量
238
审稿时长
5.0 months
期刊介绍: Published by The Institute of Electronics, Information and Communication Engineers Subject Area: Mathematics Physics Biology, Life Sciences and Basic Medicine General Medicine, Social Medicine, and Nursing Sciences Clinical Medicine Engineering in General Nanosciences and Materials Sciences Mechanical Engineering Electrical and Electronic Engineering Information Sciences Economics, Business & Management Psychology, Education.
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