Kavli Affiliate: Wei Gao
| First 5 Authors: Juncheng Long, Honglei Su, Qi Liu, Hui Yuan, Wei Gao
| Summary:
No-reference bitstream-layer point cloud quality assessment (PCQA) can be
deployed without full decoding at any network node to achieve real-time quality
monitoring. In this work, we develop the first PCQA model dedicated to
Trisoup-Lifting encoded 3D point clouds by analyzing bitstreams without full
decoding. Specifically, we investigate the relationship among texture bitrate
per point (TBPP), texture complexity (TC) and texture quantization parameter
(TQP) while geometry encoding is lossless. Subsequently, we estimate TC by
utilizing TQP and TBPP. Then, we establish a texture distortion evaluation
model based on TC, TBPP and TQP. Ultimately, by integrating this texture
distortion model with a geometry attenuation factor, a function of
trisoupNodeSizeLog2 (tNSL), we acquire a comprehensive NR bitstream-layer PCQA
model named streamPCQ-TL. In addition, this work establishes a database named
WPC6.0, the first and largest PCQA database dedicated to Trisoup-Lifting
encoding mode, encompassing 400 distorted point clouds with both 4 geometric
multiplied by 5 texture distortion levels. Experiment results on M-PCCD,
ICIP2020 and the proposed WPC6.0 database suggest that the proposed
streamPCQ-TL model exhibits robust and notable performance in contrast to
existing advanced PCQA metrics, particularly in terms of computational cost.
The dataset and source code will be publicly released at
href{https://github.com/qdushl/Waterloo-Point-Cloud-Database-6.0}{textit{https://github.com/qdushl/Waterloo-Point-Cloud-Database-6.0}}
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