Dynamic point cloud texture video compression using the edge position difference oriented motion model

Ashek Ahmmed, Manoranjan Paul, Mark Pickering

Research output: Other contribution to conferenceAbstractpeer-review

2 Citations (Scopus)

Abstract

Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs edge position difference oriented motion model. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 3.15% over the V-PCC HEVC reference encoder.
Original languageEnglish
Pages335
Number of pages1
DOIs
Publication statusE-pub ahead of print - 10 May 2021
Event2021 Data Compression Conference, DCC 2021 - Snowbird, United States
Duration: 23 Mar 202126 Mar 2021

Conference

Conference2021 Data Compression Conference, DCC 2021
Country/TerritoryUnited States
CitySnowbird
Period23/03/2126/03/21

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