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.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technolog