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Dynamic Point Cloud Texture Video Compression using the Edge Position Difference Oriented Motion Model

Citation Author(s):
Ashek Ahmmed, Manoranjan Paul, and Mark Pickering
Submitted by:
Ashek Ahmmed
Last updated:
4 March 2021 - 6:14pm
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Ashek Ahmmed
Categories:

Abstract 

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. One approach to compress point clouds is the video based point cloud compression (V-PCC) technique which projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using the coding tools offered by modern video coding standard like HEVC. However, the properties of projected texture video frames are different from traditional video frames, hence 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.

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DCC_2021_Presentation.pdf

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