Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression

TL;DR

This paper introduces a low-cost, perceptually aligned quality model for 3D point cloud compression that improves rate control and correlates well with human perception, outperforming existing metrics.

Contribution

It proposes a novel linear perceptual quality model for 3D point clouds that is computationally efficient and enhances rate-distortion optimization in V-PCC standard.

Findings

Model correlates well with subjective quality scores

Outperforms state-of-the-art full reference measures

Improves perceptual quality at same bit rate

Abstract

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and video, no such one exists for 3D point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization parameters and whose coefficients can easily be computed from two features extracted from the original 3D point cloud. Subjective quality tests with 400 compressed 3D point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearsons linear correlation coefficient. Moreover, we show that for the same target bit rate, ratedistortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.