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No-Reference Bitstream-Based Perceptual Quality Assessment of Octree-Lifting Encoded 3D Point Clouds.
IEEE Transactions on Visualization and Computer Graphics
|August 15, 2024
Summary
We developed a novel no-reference point cloud quality assessment (PCQA) model for Octree-Lifting coded data. This streamPCQ-OL model accurately predicts perceived quality using bitstream information, outperforming existing methods.
Area of Science:
- Computer Vision
- Signal Processing
- Multimedia Engineering
Background:
- No-reference point cloud quality assessment (PCQA) is crucial for network monitoring.
- Existing methods often lack accuracy for compressed point cloud data.
Purpose of the Study:
- To develop a no-reference PCQA model for Octree-Lifting coded point clouds using bitstream data.
- To enhance perceived quality assessment by considering texture characteristics and quantization parameters.
Main Methods:
- Explored the correlation between subjective quality and texture quantization parameter (TQP).
- Incorporated texture complexity (TC) estimation using TQP and Y_std (average standard deviation of Y-component).
- Developed a texture distortion model integrated with position quantization scale (PQS) for the streamPCQ-OL model.
Main Results:
- The proposed streamPCQ-OL model demonstrates superior performance compared to existing PCQA methods.
- Accurate prediction of perceived quality for Octree-Lifting coded point clouds.
Conclusions:
- The streamPCQ-OL model offers an effective solution for no-reference PCQA based on bitstreams.
- Public release of dataset and source code facilitates further research and application.
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