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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Rate-Reconfigurable Deep Point Cloud Compression With Perceptual Bit Allocation Optimization.

Yun Zhang, Lewen Fan, Zixi Guo

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 30, 2026

Summary

This summary is machine-generated.

This study introduces a novel Rate-Reconfigurable Deep Point Cloud Compression (RR-DPCC) method. It achieves arbitrary bit rate control and efficient joint compression of geometry and attributes using a single model, significantly reducing bit rates and processing time.

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Area of Science:

Computer Vision
Data Compression
Machine Learning

Background:

Conventional point cloud compression methods necessitate multiple models for varying bit rates.
Existing approaches often fail to adequately address the differing rate requirements of geometry and attribute data.
The need for efficient, adaptable, and perceptually optimized point cloud compression is critical.

Purpose of the Study:

To propose an end-to-end Rate-Reconfigurable Deep Point Cloud Compression (RR-DPCC) framework.
To enable arbitrary bit rate control using a single trained deep learning model.
To enhance compression efficiency by jointly encoding geometry and attribute data with perceptual optimization.

Main Methods:

Development of a Rate-Reconfigurable Deep Point Cloud Compression (RR-DPCC) framework incorporating on/off-line Perceptual Bit Allocation Optimization (PBAO-ON/OFF).
Introduction of a one-stream network for joint geometry and attribute encoding.
Implementation of a bitrate reconfigurable module and a rate allocation module for fine-grained control and optimized bit distribution.
Derivation of rate-distortion models (R-α/β and D-α/β) for accurate rate control and bit allocation.

Main Results:

The RR-DPCC achieves fine-grained bitrate control and allocation via a single trained model.
Significant bit rate reductions were observed: -6.56% (PBAO-ON) and -4.90% (PBAO-OFF) compared to V-PCC.
Further reductions of -18.68% (PBAO-ON) and -15.34% (PBAO-OFF) were achieved against Deep-JGAC.
Substantial reductions in encoding/decoding time were reported: up to 98.38% (PBAO-ON) and 53.75% (PBAO-OFF) versus V-PCC.

Conclusions:

The proposed RR-DPCC with PBAO-ON/OFF offers a unified solution for adaptable and efficient point cloud compression.
The method achieves superior rate-distortion performance and significant computational savings.
This approach advances the state-of-the-art in deep learning-based point cloud compression.