论文标题
IT/IST/iPleiria响应jpeg Pleno Point Cloud编码上提案的呼吁
IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding
论文作者
论文摘要
本文档描述了基于深度学习的点云几何编解码器以及基于深度学习的点云关节几何和颜色编解码器，并提交了2022年1月发布的关于JPEG PLENO POINT云编码的提案的呼吁。拟议的编解码器是基于深度学习的PC几何编码的最新发展，并通过基于深度学习的PC几何形式编码，并通过呼叫来供应供应供应供应。所提出的几何编解码器提供了一种压缩效率，它的表现优于MPEG G-PCC标准和胜过表现，或者与V-PCC Intra Intra Interra标准竞争，用于提案测试集的JPEG呼吁；但是，由于需要克服的质量饱和效应，关节几何和颜色编解码器不会发生同样的情况。
This document describes a deep learning-based point cloud geometry codec and a deep learning-based point cloud joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022. The proposed codecs are based on recent developments in deep learning-based PC geometry coding and offer some of the key functionalities targeted by the Call for Proposals. The proposed geometry codec offers a compression efficiency that outperforms the MPEG G-PCC standard and outperforms or is competitive with the V-PCC Intra standard for the JPEG Call for Proposals test set; however, the same does not happen for the joint geometry and colour codec due to a quality saturation effect that needs to be overcome.
