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This content will become publicly available on May 23, 2023

Title: Point Cloud Attribute Compression Via Chroma Subsampling
We introduce chroma subsampling for 3D point cloud attribute compression by proposing a novel technique to sample points irregularly placed in 3D space. While most current video compression standards use chroma subsampling, these chroma subsampling methods cannot be directly applied to 3D point clouds, given their irregularity and sparsity. In this work, we develop a framework to incorporate chroma subsampling into geometry-based point cloud encoders, such as region adaptive hierarchical transform (RAHT) and region adaptive graph Fourier transform (RAGFT). We propose different sampling patterns on a regular 3D grid to sample the points at different rates. We use a simple graph-based nearest neighbor interpolation technique to reconstruct the full resolution point cloud at the decoder end. Experimental results demonstrate that our proposed method provides significant coding gains with negligible impact on the reconstruction quality. For some sequences, we observe a bitrate reduction of 10-15% under the Bjontegaard metric. More generally, perceptual masking makes it possible to achieve larger bitrate reductions without visible changes in quality.
Authors:
; ; ; ;
Award ID(s):
1956190
Publication Date:
NSF-PAR ID:
10340128
Journal Name:
2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Page Range or eLocation-ID:
2579 to 2583
Sponsoring Org:
National Science Foundation
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