Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression

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 derive a dimensionless quantity to represent the overall quality degradation. Then, we derive the relationships of overall distortion and bit rate with the quantization parameters. Finally, we formulate the bit rate constrained point cloud compression as a constrained minimization problem using the derived polynomial models and deduce the solution via an iterative numerical method. Experimental results show that the proposed algorithm can achieve optimal decoded point cloud quality at various target bit rates, and substantially outperform the video-rate-distortion model based point cloud compression scheme.

arXiv commentAccepted to IEEE Transactions on Circuits and Systems for Video Technology

References in corpus (6)

• An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
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• Graph-based compression of dynamic 3D point cloud sequences
• Dynamic Adaptive Point Cloud Streaming
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• Rate-Distortion Optimized Skip Coding of Region Adaptive Hierarchical Transform Coefficients for MPEG G-PCC
• Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows