No-Reference Video Quality Measurement With Support Vector Regression

Abstract

A novel approach for no-reference video quality measurement is proposed in this paper. Firstly, various feature extraction methods are used to quantify the quality of videos. Then, a support vector regression model is trained and adopted to predict unseen samples. Six different regression models are compared with the support vector regression model. The experimental results indicate that the combination of different video quality features with a support vector regression model can outperform other methods for no-reference video quality measurement significantly.

Counting people with support vector regression

“…Support vector regression (SVR) is one kind of support vector machine (SVM) on function approximation [7] [9]. The basic training principle behind SVRs is finding the optimal linear hyper plane such that the expected mean square error for unseen test samples is minimized.…”

Automatic diagnosis of submersible motor pump conditions in offshore oil exploration

“…The support vector machine (SVM) [15] classification architecture has been extensively used during the last decade in many distinct domains, also in machine fault diagnosis [16], and is currently considered one of the most powerful methods in machine learning for solving binary classification problems. SVMs also have been successfully used for regression tasks [17]. We experimentally compared SVM classifiers with Multi-layer Perceptron (MLP) [6] artificial neural network classifiers and found that SVMs achieved a consistent higher performance criterion, although the MLP being computationally much more expensive during training.…”

Continuous Eeg Signal Analysis for Asynchronous Bci Application

“…58,59 For example, the min-max modular SVM is adopted for the prediction of multiple subcellular locations of proteins. 58 No-reference video quality measurement is performed with support vector regression.…”

“…58 No-reference video quality measurement is performed with support vector regression. 59 Since it can balance accuracy and generalization simultaneously, it is used for classification in this study. [58][59][60] In most previous works, the segment of EEG signals was usually too long to apply in real-time analysis.…”

“…59 Since it can balance accuracy and generalization simultaneously, it is used for classification in this study. [58][59][60] In most previous works, the segment of EEG signals was usually too long to apply in real-time analysis. The EEG signals, however, are not restricted to a predefined segment in asynchronous BCI works.…”