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Related Experiment Video

Updated: Jun 17, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

No-reference video quality measurement with support vector regression.

Huicheng Lian¹

¹School of Computer Engineering and Science, Shanghai University, P. Box 147, No. 149, Yanchang Road, Shanghai, 200072, China. lianhc@shu.edu.cn

International Journal of Neural Systems

|December 30, 2009

Summary

This summary is machine-generated.

This study introduces a new method for no-reference video quality assessment. Combining diverse video quality features with support vector regression significantly improves prediction accuracy compared to other models.

Related Experiment Videos

Last Updated: Jun 17, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

Computer Vision
Signal Processing
Machine Learning

Background:

Objective video quality assessment is crucial for understanding user experience.
No-reference (NR) video quality measurement presents significant challenges due to the absence of original pristine videos for comparison.

Purpose of the Study:

To propose a novel and effective approach for no-reference video quality measurement.
To evaluate the performance of the proposed method against established regression models.

Main Methods:

Extraction of various features to quantify video quality parameters.
Training a support vector regression (SVR) model using extracted features.
Comparative analysis of SVR against six other regression models for prediction accuracy.

Main Results:

The proposed method, combining specific video quality features with SVR, demonstrated superior performance.
Support vector regression significantly outperformed the other six evaluated regression models.
The approach achieved high accuracy in predicting unseen video quality samples.

Conclusions:

The integration of selected video quality features with support vector regression offers a robust solution for no-reference video quality assessment.
This method provides a significant advancement over existing techniques in the field.
The findings highlight the potential of machine learning for objective video quality prediction.