Prediction and Modeling for No-Reference Video Quality Assessment based on Machine Learning

The increase in popularity of video streaming, the improvement of bandwidth corresponding to 5G networks and the transmission of higher amounts of data derived from advanced video formats such as Ultra High Definition (UHD) with 4K and 8K resolutions make the user demand a high perceptual quality of the contents consumed. For that reason, it is necessary to create advanced models for predicting video quality based on the video features and the encoding settings in environments where the reference is not present. The use of machine learning (ML) techniques for data analysis based on patterns extracted on the features of audio-visual content, improves the generation of prediction models for accurately predicting quality. This paper presents a novel model for assessing video quality based on the analysis of encoding video settings of the transmitted contents and the image intrinsic characteristics for objectively estimating the Mean Opinion Score (MOS) in correlation with the subjective results. The use of data mining techniques for combining a collection of parameters associated to the video transmitted improves the performance of traditional quality evaluation, as demonstrated with the database analyzed for this purpose.