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Related Experiment Videos

Video quality prediction and classification using XGBoost under variable encoding and network conditions.

Jaroslav Frnda1,2, Jan Rozhon3, Miroslav Uhrina4

  • 1Department of Quantitative Methods and Economic Informatics, University of Zilina, Zilina, 01026, Slovakia. jaroslav.frnda@uniza.sk.

Scientific Reports
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework for video quality assessment (VQA) that maps objective metrics like SSIM and VMAF to subjective Mean Opinion Scores (MOS). It offers real-time user-perceived quality estimation, outperforming traditional methods for online services.

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Area of Science:

  • Computer Science
  • Signal Processing
  • Machine Learning

Background:

  • Objective video quality assessment (VQA) metrics like SSIM and VMAF lack standardized conversion to subjective Mean Opinion Scores (MOS).
  • Traditional subjective VQA tests are time-consuming, costly, and impractical for real-time online services like IPTV and streaming.
  • Packet loss is a primary driver of video quality degradation in real-world network conditions.

Purpose of the Study:

  • To develop a machine learning framework for accurate mapping of objective VQA metrics to subjective MOS.
  • To enable real-time estimation of user-perceived video quality for online platforms.
  • To propose a reduced-reference VQA classifier robust to packet loss.

Main Methods:

  • Utilized the XGBoost algorithm to train regression models for mapping SSIM and VMAF to MOS.
  • Trained models on a dataset of over 700 distorted video sequences with varying codecs (H.264, H.265), resolutions, bitrates, and packet loss rates.
  • Developed a reduced-reference classifier using packet loss rate for quality estimation without original sequence comparison.

Main Results:

  • SSIM-to-MOS and VMAF-to-MOS regression models achieved a Pearson correlation of 0.95 and an RMSE of 0.31.
  • The reduced-reference classifier achieved a weighted F1 score of 0.92, demonstrating effectiveness in packet loss environments.
  • The proposed machine learning framework outperformed Back Propagation Neural Network (BPNN) and other state-of-the-art methods.

Conclusions:

  • The developed framework provides a reliable and efficient method for real-time video quality assessment, bridging the gap between objective metrics and subjective perception.
  • The reduced-reference classifier is suitable for time-sensitive services like live streaming and IPTV, even with network impairments.
  • Publicly available source codes promote reproducibility and further research in Quality of Experience (QoE) assessment.