Quality Analysis of the Coding Bitrate Tradeoff Between Geometry and Attributes for Colored Point Clouds

TL;DR

This study investigates how different bitrate allocations between geometry and color attributes affect the quality of reconstructed point clouds, using various encoding standards and quality metrics.

Contribution

It provides a comprehensive analysis of bitrate tradeoffs between geometry and attributes in point cloud compression, highlighting that higher attribute bitrate generally improves quality.

Findings

Higher attribute bitrate allocation yields better quality.

Different encoding methods show similar tradeoff effects.

Objective metrics confirm the benefit of increased attribute bitrate.

Abstract

Typically, point cloud encoders allocate a similar bitrate for geometry and attributes (usually RGB color components) information coding. This paper reports a quality study considering different coding bitrate tradeoff between geometry and attributes. A set of five point clouds, representing different characteristics and types of content was encoded with the MPEG standard Geometry Point Cloud Compression (G-PCC), using octree to encode geometry information, and both the Region Adaptive Hierarchical Transform and the Prediction Lifting transform for attributes. Furthermore, the JPEG Pleno Point Cloud Verification Model was also tested. Five different attributes/geometry bitrate tradeoffs were considered, notably 70%/30%, 60%/40%, 50%/50%, 40%/60%, 30%/70%. Three point cloud objective metrics were selected to assess the quality of the reconstructed point clouds, notably the PSNR YUV, the Point Cloud Quality Metric, and GraphSIM. Furthermore, for each encoder, the Bjonteegaard Deltas were computed for each tradeoff, using the 50%/50% tradeoff as a reference. The reported results indicate that using a higher bitrate allocation for attribute encoding usually yields slightly better results.