Modelling user quality of experience from objective and subjective data sets using fuzzy logic

Abstract

One of the paramount research questions in the scientific community today is how to remotely assess user quality of experience (QoE) for a specific service. To this end, various user QoE assessment models have been developed; however, they are mostly based on the data gathered from controlled environment experimentation. The aim of this research was to model user QoE for the User Datagram Protocol-based video streaming service from the results of uncontrolled subjective tests. Specifically, using fuzzy logic, we have correlated the values of three objective network parameters (the packet loss rate and the number and duration of packet loss occurrences in one streaming session) with test subjects' subjective perception about perceived quality distortions. The dependencies between different values of the parameters and the subjects' perception of video quality were used to develop a no reference objective video quality assessment model for assessing user QoE. The key distinguishing feature of the developed model lays in the process of subjective evaluation, which was conducted with a panel of 602 test subjects who evaluated the quality of 1-h video in home environments. For this purpose, 72 different test sequences were prepared for rating. We showed that a strong positive linear relationship exists between the assessed QoE of the model and the Mean Opinion Scores of the subjects (a Pearson correlation coefficient equal to 0.8841).

Subjective Evaluation of User Quality of Experience for Omnidirectional Video Streaming

“…the videos which surround the viewing angle of a user. The paper represents a) the continuation of our previous work [2,3] where extensive subjective evaluation for video streaming service was conducted and the results were used for modelling the user perception based on a number of objective input parameters, and b) one of the outputs of the Quality of Experience for Virtual Reality Applications (QoE4VR) project [4].…”

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

“…Human perception is influenced by a myriad of factors, including individual preferences, cognitive biases, and contextual conditions, making it highly variable and difficult to model accurately. Additionally, different users may have diverse expectations and sensitivities, leading to variations in perceived quality [7,8]. Even subtle changes in video content, such as frame-rate adjustments or compression rates, can impact user perception [9,10].…”

“…The main advantage of these types of models, relying solely on objective parameters, is their efficiency and ease of implementation, as they do not require human involvement in the assessment process. However, they may fall short in capturing the subjective and perceptual aspects of user experience, thus failing to meet the essence of the QoE concept [7].…”

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

“…It was found that many qualitative, subjective factors that are difficult to measure significantly influence users' opinions about the service. These factors include the social context of service usage, internal user state, and their prior experiences, quality of content, application design, and numerous others [3]. Therefore, a new concept (QoE) was introduced, providing a holistic approach to telecommunication service quality analysis.…”