Quality of Experience Measurement for Video Streaming Based On Adaptive Neural Fuzzy Inference System

Rana Fareed Ghani,Amal Sufiuh Ajrash

doi:10.24996/ijs.2019.60.7.21

Abstract

Technological development in recent years leads to increase the access speed in the networks that allow a huge number of users watching videos online. Video streaming is one of the most popular applications in networking systems. Quality of Experience (QoE) measurement for transmitted video streaming may deal with data transmission problems such as packet loss and delay. This may affect video quality and leads to time consuming. We have developed an objective video quality measurement algorithm that uses different features, which affect video quality. The proposed algorithm has been estimated the subjective video quality with suitable accuracy. In this work, a video QoE estimation metric for video streaming services is presented where the proposed metric does not require information on the original video. This work predicts QoE of videos by extracting features. Two types of features have been used, pixel-based features and network-based features. These features have been used to train an Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the video QoE.

Highlights

The increasing of access speed on the internet led to a large amount of online video on the internet and an increasing number of Consumers have own multimedia devices that allow users to watch online video anytime, anywhere and it tries to introduce these services to the person who uses a particular product with specific resolutions
The provider must ensure that customers have received appropriate quality at ‎whole times. ‎The variety of methods for measuring video quality aims to understand and ensuring a good user experience: The research in the last years, Quality of Experience (QoE) term appeared and act as representative to user satisfaction about the quality of the displayed content and matching the computed quality to people's opinion like, it depends on the virtual experience of the human virtual system (HVS) with the video content [1]
It is important to differentiate between Quality of services (QoS) term and Quality of Experience (QoE)term because the first one refers to the performance of IP-based networks and services and the other depend on the degree of the end user delight to the application, service, or system [1,2]

Summary

INTRODUCTION

The increasing of access speed on the internet led to a large amount of online video on the internet and an increasing number of Consumers have own multimedia devices that allow users to watch online video anytime, anywhere and it tries to introduce these services to the person who uses a particular product with specific resolutions. It is important to differentiate between Quality of services (QoS) term and Quality of Experience (QoE)term because the first one refers to the performance of IP-based networks and services and the other depend on the degree of the end user delight to the application, service, or system [1,2]. QoS-based metrics models have the ability to distribute at any point in the network, but It does not have an idea on the user impression of the quality of the video being played. The server side is responsible for collect ‎network QoS parameter and other information Save this information in a big database and use it in a heuristic rule model to predict user score, this process called fuzzy clustering analysis ‎and they will generate service QoE score that will feedback to clients. In the first part training phase, they calculate the sensitivity of the low quality coded videos from some features such as blockiness, blurriness, edge, and continuity, etc. rank these features using the Principal Component Analysis (PCA) method

The Proposal System

Classification and Clustering

CONCLUSIONS