Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display

TL;DR

This paper presents a new subjective quality database for point clouds viewed with 6DoF HMDs, analyzes the effects of geometry and texture distortions, and proposes two objective quality assessment models for immersive environments.

Contribution

It introduces the SIAT-PCQD database, the first comprehensive subjective point cloud quality dataset for 6DoF HMDs, and proposes two novel projection-based objective quality metrics.

Findings

The database contains 340 distorted point clouds with diverse compression parameters.

Geometry and texture distortions significantly affect perceived quality.

The proposed models outperform existing metrics in immersive point cloud quality assessment.

Abstract

In this paper, we focus on subjective and objective Point Cloud Quality Assessment (PCQA) in an immersive environment and study the effect of geometry and texture attributes in compression distortion. Using a Head-Mounted Display (HMD) with six degrees of freedom, we establish a subjective PCQA database, named SIAT Point Cloud Quality Database (SIAT-PCQD). Our database consists of 340 distorted point clouds compressed by the MPEG point cloud encoder with the combination of 20 sequences and 17 pairs of geometry and texture quantization parameters. The impact of distorted geometry and texture attributes is further discussed in this paper. Then, we propose two projection-based objective quality evaluation methods, i.e., a weighted view projection based model and a patch projection based model. Our subjective database and findings can be used in point cloud processing, transmission, and coding, especially for virtual reality applications. The subjective dataset has been released in the public repository.