Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display
arXiv:2008.02501 · doi:10.1109/TCSVT.2021.3101484
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.
This work has been submitted to the IEEE for possible publication
References in corpus (3)
Cited by in corpus (7)
- BASICS: Broad quality Assessment of Static point clouds In Compression Scenarios
- Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds
- No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction
- Subjective and Objective Quality Assessment of Rendered Human Avatar Videos in Virtual Reality
- No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
- The Worse The Better: Content-Aware Viewpoint Generation Network for Projection-related Point Cloud Quality Assessment
- Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features