Data Analysis Approach on Video Quality of Experience (QOE) Prediction Using Enrqoe Machine Learning

Name of Author : P. Archana & Dr Subhash Kulkarni

Abstract: In the last decades, most models have been developed to predict Video Quality of Experience, yet usage of these models up to now faces significant problems particularly for performing regression tasks. With regards to, furthermore, few papers on quantifying MOS and investigating the relationship between MOS and other quality of service parameters were developed. Thus, we address this concern by presenting a novel ENRQOE data analysis depend on the video streaming Quality of Experience data. In specifically, our analytical model comprises regression and categorization for predict video Quality of Experience. We express the performance of ENRQOE with Live-Netflix Video Database along with state-of-the-art machine learning algorithms that were highly enhanced by utilizing the Quality of Experience representation from ENRQOE. The outcomes predict that our proposed method outperforms other existing approaches in term of SROCC, LCC in Quality of Experience of prediction

Keywords : Video Quality, (QOE), Data Analysis

DOI :