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Review and Preview01:10

Review and Preview

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Updated: Jun 6, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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No-Reference Objective Quality Metrics for 3D Point Clouds: A Review.

Simone Porcu1,2, Claudio Marche1,2, Alessandro Floris1,2

  • 1Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

No-reference point cloud quality assessment (NR PCQA) metrics are crucial for evaluating 3D data quality without the original file. These methods accurately estimate quality from distorted point clouds (PCs), vital for applications like streaming.

Keywords:
3Dmodel-based metricno-reference metricobjective quality evaluationpoint cloudprojection-based metricquality of experience

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Area of Science:

  • Computer Vision
  • Multimedia Technology
  • Digital Signal Processing

Background:

  • Three-dimensional (3D) applications are driving immersive multimedia, with point clouds (PCs) essential for 3D environments.
  • Large data sizes of PCs pose challenges, and compression introduces quality degradations affecting accuracy.
  • Point cloud quality assessment (PCQA) is critical due to the trade-off between data size and quality.

Purpose of the Study:

  • To review state-of-the-art no-reference (NR) objective quality metrics for point clouds (PCs).
  • To highlight the importance of NR PCQA for evaluating compressed or generated PCs without original data.

Main Methods:

  • Focus on NR PCQA metrics that analyze feature information from distorted PCs.
  • Review techniques that estimate quality solely from the degraded point cloud data.

Main Results:

  • NR PCQA metrics can accurately estimate the quality of generated and compressed PCs.
  • These metrics rely on feature information extracted directly from the distorted point cloud.

Conclusions:

  • NR PCQA is vital for real-world applications where original data is unavailable, such as in 3D streaming.
  • Objective NR metrics offer a practical solution for assessing point cloud quality efficiently.