Near-Lossless Compression of Point Cloud Attribute Using Quantization Parameter Cascading and Rate-Distortion Optimization

Near-lossless compression of point clouds is suitable for the application scenarios with low distortion tolerance and certain requirements on the rate. Near-lossless attribute compression usually adopts a level-of-detail structure, where the dependencies between the layers make it possible to improve the rate-distortion (R-D) performance by using different quantization parameters for different layers. In this work, a theoretical analysis of the dependencies between adjacent layers is carried out, based on which the dependent Distortion-Quantization and Rate-Quantization models are established for point cloud attribute compression. Then an algorithm for quantization parameter cascading based on R-D optimization is proposed and implemented for near-lossless compression of point cloud attributes. The experimental results show that the proposed method has a superior performance gain compared to state-of-the-art for the Hausdorff R-D performance. At the same time, the proposed method improves subjective quality and is well adapted to various categories of point clouds.

I. Introduction

In recent years, with the rapid development of 3-dimensional (3D) acquisition technologies, the demand for highly detailed 3D data is increasing to facilitate more realistic visualization of 3D objects in virtual and augmented reality, medical imaging, automatic driving, digital cities, and robotics to name a few [1]. However, the large amount of data contained in a point cloud poses storage and transmission challenges. Typically, a point cloud contains millions of points, in which not only geometry coordinates but also a vector of attributes, such as color, reflectance, and normals, is associated with each point. Therefore, efficient point cloud compression (PCC) technologies have attracted significant attention in both research and industry.