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.
翻译:本文件介绍了向2022年1月发布的 " JPEG Pleno Point Cloud Codeting提案呼吁 " 提交的基于深学习的点云几何码和基于深学习的点云共同几何码和基于深学习的点云共同几何码和彩色码。提议的代码以基于深学习的PCPC Pleno Point Cloud Clude Codement的最新发展为基础,并提供了 " 征集提案 " 所针对的一些关键功能。提议的几何码提供了比MPEG G-PCC标准高的压缩效率,优于或优于为JPEG提案呼吁设定的V-PC Intra标准;然而,由于需要克服质量饱和效应,联合几何法和彩色码的情况并非如此。