Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression
Pan Gao, Shengzhou Luo, and Manoranjan Paul

TL;DR
This paper introduces a rate-distortion model for point cloud compression that optimally selects quantization parameters under bit rate constraints, improving quality over existing methods.
Contribution
It proposes a unified model for geometry and color distortion, and formulates a constrained optimization approach for quantization parameter selection in point cloud compression.
Findings
Achieves optimal point cloud quality at various bit rates.
Outperforms existing video-rate-distortion based compression schemes.
Provides a practical iterative solution for parameter optimization.
Abstract
As being one of the main representation formats of 3D real world and well-suited for virtual reality and augmented reality applications, point clouds have gained a lot of popularity. In order to reduce the huge amount of data, a considerable amount of research on point cloud compression has been done. However, given a target bit rate, how to properly choose the color and geometry quantization parameters for compressing point clouds is still an open issue. In this paper, we propose a rate-distortion model based quantization parameter selection scheme for bit rate constrained point cloud compression. Firstly, to overcome the measurement uncertainty in evaluating the distortion of the point clouds, we propose a unified model to combine the geometry distortion and color distortion. In this model, we take into account the correlation between geometry and color variables of point clouds and…
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