Model-Based Joint Bit Allocation Between Geometry and Color for Video-Based 3D Point Cloud Compression

In video-based 3D point cloud compression, the quality of the reconstructed 3D point cloud depends on both the geometry, and color distortions. Finding an optimal allocation of the total bitrate between the geometry coder, and the color coder is a challenging task due to the large number of possible solutions. To solve this bit allocation problem, we first propose analytical distortion, and rate models for the geometry, and color information. Using these models, we formulate the joint bit allocation problem as a constrained convex optimization problem, and solve it with an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.66$\%$ of its time complexity.

I. Introduction

With the rapid development of 3D scanning techniques [1], point clouds are now readily available and popular [2]. There are already many 3D point cloud applications in the fields of 3D modeling [3] [4], automatic driving [5], 3D printing [6], augmented reality [7], etc. Compared with traditional 2D images and videos, a 3D point cloud describes the 3D scene or object with the geometry information and the corresponding attributes (e.g., color, reflectance) [8]. To represent a 3D scene accurately, millions of points must be captured and processed. This huge data volume poses a severe challenge for efficient storage and transmission. In the past few years, major progress in both static and dynamic point cloud compression has been made [9] (see Section II).