Compression of Plenoptic Point Cloud Attributes Using 6-D Point Clouds and 6-D Transforms - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Multimedia Année : 2023

Compression of Plenoptic Point Cloud Attributes Using 6-D Point Clouds and 6-D Transforms

Résumé

In this paper, we introduce a novel 6-D representation of plenoptic point clouds, enabling joint, non-separable transform coding of plenoptic signals defined along both spatial and angular (viewpoint) dimensions. This 6-D representation, which is built in a global coordinate system, can be used in both multi-camera studio capture and video fly-by capture scenarios, with various viewpoint (camera) arrangements and densities. We show that both the Region-Adaptive Hierarchical Transform (RAHT) and the Graph Fourier Transform (GFT) can be extended to the proposed 6-D representation to enable the non-separable transform coding. Our method is applicable to plenoptic data with either dense or sparse sets of viewpoints, and to complete or incomplete plenoptic data, while the stateof-the-art RAHT-KLT method, which is separable in spatial and angular dimensions, is applicable only to complete plenoptic data. The "complete" plenoptic data refers to data that has, for each spatial point, one colour for every viewpoint (ignoring any occlusions), while "incomplete" data has colours only for the visible surface points at each viewpoint. We demonstrate that the proposed 6-D RAHT and 6-D GFT compression methods are able to outperform the state-of-the-art RAHT-KLT method on 3-D objects with various levels of surface specularity, and captured with different camera arrangements and different degrees of viewpoint sparsity.
Fichier principal
Vignette du fichier
5DGFT-finalversion.pdf (7.76 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03432597 , version 1 (17-11-2021)

Licence

Paternité

Identifiants

Citer

Maja Krivokuća, Ehsan Miandji, Christine Guillemot, Philip A Chou. Compression of Plenoptic Point Cloud Attributes Using 6-D Point Clouds and 6-D Transforms. IEEE Transactions on Multimedia, 2023, 25, pp.593-607. ⟨10.1109/TMM.2021.3129341⟩. ⟨hal-03432597⟩
73 Consultations
93 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More