Kavli Affiliate: Wei Gao
| First 5 Authors: Dongshuai Duan, 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 focus on the PCQA problem dedicated to Octree-RAHT
encoding mode. First, to address the issue that existing PCQA databases have a
small scale and limited distortion levels, we establish the WPC5.0 database
which is the first one dedicated to Octree-RAHT encoding mode with a scale of
400 distorted point clouds (PCs) including 4 geometric multiplied by 5 attitude
distortion levels. Then, we propose the first PCQA model dedicated to
Octree-RAHT encoding mode by parsing PC bitstreams without full decoding. The
model introduces texture bitrate (TBPP) to predict texture complexity (TC) and
further derives the texture distortion factor. In addition, the Geometric
Quantization Parameter (PQS) is used to estimate the geometric distortion
factor, which is then integrated into the model along with the texture
distortion factor to obtain the proposed PCQA model named streamPCQ-OR. The
proposed model has been compared with other advanced PCQA methods on the
WPC5.0, BASICS and M-PCCD databases, and experimental results show that our
model has excellent performance while having very low computational complexity,
providing a reliable choice for time-critical applications. To facilitate
subsequent research, the database and source code will be publicly released at
https://github.com/qdushl/Waterloo-Point-Cloud-Database-5.0.
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