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Support Vector Regression-based Reduced-Reference Perceptual Quality Model for Compressed Point Clouds.

Honglei Su1, Qi Liu2, Hui Yuan3

  • 1College of Electronics and Information, Qingdao University, Qingdao 266071, China.

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|November 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces PCQAML, a novel reduced-reference quality metric for video-based point cloud compression (V-PCC). PCQAML effectively predicts perceptual quality using selected features, outperforming existing metrics in accuracy and efficiency.

Keywords:
LASSO regressionPoint cloud compressionfeature selectionperceptual quality metricsupport vector regression

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Multimedia Compression

Background:

  • Video-based Point Cloud Compression (V-PCC) is an MPEG standard for compressing 3D data.
  • Existing quality metrics often require the original point cloud, limiting real-time applications.
  • Reduced-reference (RR) metrics are needed when the original data is unavailable.

Purpose of the Study:

  • To develop a novel reduced-reference quality metric for V-PCC.
  • To address challenges in feature selection and perceptual quality mapping for distorted point clouds.
  • To provide an accurate and efficient quality assessment tool for V-PCC.

Main Methods:

  • Proposed a comprehensive feature set including compression, geometry, normal, curvature, and luminance.
  • Utilized the Least Absolute Shrinkage and Selection Operator (LASSO) for effective feature selection.
  • Mapped selected features to Mean Opinion Scores (MOS) in a nonlinear space.

Main Results:

  • The proposed metric, PCQAML, demonstrated superior performance on benchmark datasets (WPC2.0, M-PCCD).
  • PCQAML outperformed state-of-the-art full-reference and reduced-reference metrics.
  • Achieved high correlation coefficients (Pearson, Spearman, Kendall) and low RMSE, indicating strong predictive accuracy.

Conclusions:

  • PCQAML offers a flexible and accurate solution for reduced-reference quality assessment in V-PCC.
  • The method effectively characterizes visual quality using selected features.
  • PCQAML is a promising tool for real-time V-PCC applications requiring perceptual quality evaluation.