Multi-resolution intra-predictive coding of 3D point cloud attributes

TL;DR

This paper introduces a multi-resolution intra-predictive coding method for 3D point cloud attributes that leverages localized transforms and intra-prediction to improve compression efficiency, achieving up to 0.5dB gains.

Contribution

It presents a novel intra-prediction strategy integrated with RAGFT for better compression of 3D point clouds, exploiting correlations across resolutions.

Findings

Achieves up to 0.5dB improvement over RAHT-based methods.

Demonstrates effectiveness of intra-prediction in multi-resolution transforms.

Validates approach on 8i dataset with positive results.

Abstract

We propose an intra frame predictive strategy for compression of 3D point cloud attributes. Our approach is integrated with the region adaptive graph Fourier transform (RAGFT), a multi-resolution transform formed by a composition of localized block transforms, which produces a set of low pass (approximation) and high pass (detail) coefficients at multiple resolutions. Since the transform operations are spatially localized, RAGFT coefficients at a given resolution may still be correlated. To exploit this phenomenon, we propose an intra-prediction strategy, in which decoded approximation coefficients are used to predict uncoded detail coefficients. The prediction residuals are then quantized and entropy coded. For the 8i dataset, we obtain gains up to 0.5db as compared to intra predicted point cloud compresion based on the region adaptive Haar transform (RAHT).