Study on the Assessment of the Quality of Experience of Streaming Video

TL;DR

This study investigates how objective factors influence subjective QoE estimation for streaming video, proposing regression-based VQA models with high accuracy using the SQoE-III database.

Contribution

It introduces new VQA models based on regression and gradient boosting, achieving high correlation with subjective QoE and applicable with or without reference videos.

Findings

VQA models reach SRCC up to 0.9647.

Standard and handcrafted features correlate significantly with QoE.

Gradient Boosting Regressor shows promise for further enhancement.

Abstract

Dynamic adaptive streaming over HTTP provides the work of most multimedia services, however, the nature of this technology further complicates the assessment of the QoE (Quality of Experience). In this paper, the influence of various objective factors on the subjective estimation of the QoE of streaming video is studied. The paper presents standard and handcrafted features, shows their correlation and p-Value of significance. VQA (Video Quality Assessment) models based on regression and gradient boosting with SRCC reaching up to 0.9647 on the validation subsample are proposed. The proposed regression models are adapted for applied applications (both with and without a reference video); the Gradient Boosting Regressor model is perspective for further improvement of the quality estimation model. We take SQoE-III database, so far the largest and most realistic of its kind. The VQA (video quality assessment) models are available at https://github.com/AleksandrIvchenko/QoE-assesment