STAC-USC / FRSVR-PCQA

Full-reference point cloud quality assessment using support vector regression

Implementation of Full-reference point cloud quality assessment using support vector regression from [1,2].

How to use

Dataset download

This repository does not provide a dataset. When you test a sourcecode with the BASICS (Broad Quality Assessment of Static Point Clouds in a Compression Scenario) dataset [3], the point clouds can be downloaded from the URL: https://zenodo.org/records/10533088.

Calculation of 5 types of FR metrics

5 types of full-reference metrics must be calculated before training and evaluation as follows:

Run ScoreCalculator_BASICStraining.m 
Run ScoreCalculator_BASICSTest.m

After the calculation, "Scores_BASICStraining.csv" and "Scores_BASICStest.csv" are output in ScoreData folder (These csv files have already been prepared by authors in this repository).

Training and test by support vector regression

After calculating the scores, training a support vector regression model and the evaluation are carried out by the following code.

Run TrainingSVRModel.m

Reference

[1] R. Watanabe, S. N. Sridhara, H. Hong, E. Pavez, K. Nonaka, T. Kobayashi and A. Ortega, "Full-reference point cloud quality assessment using support vector regression," Elsevier Signal Processing: Image Communication (Under review).

[2] R. Watanabe, S. N. Sridhara, H. Hong, E. Pavez and A. Ortega, "ICIP 2023 Challenge: Full-Reference and Non-Reference Point Cloud Quality Assessment Methods with Support Vector Regression," 2023 IEEE International Conference on Image Processing Challenges and Workshops (ICIPCW), 2023, pp. 3654-3658.

[3] A. Ak, E. Zerman, M. Quach, A. Chetouani, A. Smolic, G. Valenzise, and P. L. Callet, “Basics: Broad quality assessment of static point clouds in a compression scenario,” IEEE Transactions on Multimedia, pp. 1–13, 2024.