27 February 2023 Rate-distortion optimized quantization for geometry-based point cloud compression
Tian Guo, Hui Yuan, Lu Wang, Tingting Wang
Author Affiliations +
Abstract

Limited by the network bandwidth, three-dimensional (3D) point cloud needs to be efficiently compressed before transmission. As one of the three attribute coding methods adopted in the geometry-based point cloud compression (G-PCC) standard developed by MPEG, predicting transform (PT) has received increasing attention. To further improve the coding efficiency of PT, we propose a rate-distortion optimized quantization (RDOQ) in which an additional option for quantization results is added by setting the quantized residuals to zero forcedly. Rate-distortion optimization is then used to determine whether the final quantized residual should be zero or nonzero. Experimental results show that average BD rates of −0.5 % , −3.0 % , and −3.0 % can be achieved for Luma, Chroma Cb, and Chroma Cr components, respectively, with negligible increment of time complexity.

© 2023 SPIE and IS&T
Tian Guo, Hui Yuan, Lu Wang, and Tingting Wang "Rate-distortion optimized quantization for geometry-based point cloud compression," Journal of Electronic Imaging 32(1), 013047 (27 February 2023). https://doi.org/10.1117/1.JEI.32.1.013047
Received: 22 December 2022; Accepted: 8 February 2023; Published: 27 February 2023
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Point clouds

Quantization

Distortion

Chromium

3D modeling

Video coding

Mathematical optimization

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