Video Quality Modelling—Comparison of the Classical and Machine Learning Techniques

Abstract

The classical objective methods of assessing video quality used so far, apart from their advantages, such as low costs, also have disadvantages. The need to eliminate these defects results in the search for better and better solutions. This article proposes a video quality assessment method based on machine learning using a linear regression model. A set of objective quality assessment metrics was used to train the model. The results obtained show that the prediction of video quality based on a machine learning model gives better results than the objective assessment based on individual metrics. The proposed model showed a strong correlation with the subjective user assessments but also a good fit of the regression function to the empirical data. It is an extension and improvement of the efficiency of the classical methods of objective quality assessment that have been used so far. The solution presented here will allow for a more accurate prediction of the video quality perceived by viewers based on an assessment carried out using a much cheaper, objective method.

Keywords

• subjective video quality assessment
• objective quality evaluation
• quality metrics
• quality of perception (QoP)
• quality of experience (QoE)
• quality model