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.