Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression | IEEE Journals & Magazine | IEEE Xplore

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...Show More

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.
Page(s): 2424 - 2438
Date of Publication: 21 November 2022

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I. Introduction

Due to the advancement in 3D scene capturing and rendering, point clouds have been a popular representation in emerging applications, such as metaverse, 3D telepresence, gaming, and virtual/augmented reality [1], [2], [3]. Recently, compression of point clouds has gained a significant attention from both academia and industry [4], [5] to enable the transmission of the captured dynamic 3D scenes or objects to a remote location. However, almost all the previous efforts on point cloud compression are focused on addressing the problem of how to reduce the bit rate given a reconstruction quality level (i.e., quantization parameter, QP), or improve the reconstruction quality at the same bit rate. Given a bit rate constraint in the bandwidth limited channel, finding the optimal QPs that can achieve maximal reconstruction quality is still an open issue in point cloud compression.

References

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