Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
| Autor: | Chanh Minh Tran, Eiji Kamioka, Phan Xuan Tan, Tho Nguyen Duc |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
lcsh:T58.5-58.64
lcsh:Information technology Computer Networks and Communications Computer science media_common.quotation_subject cumulative QoE model 020206 networking & telecommunications degree of interest 02 engineering and technology memory effect Moment (mathematics) video-on-demand services Human–computer interaction Perception On demand 0202 electrical engineering electronic engineering information engineering Degree of interest 020201 artificial intelligence & image processing Quality of experience Video streaming Session (computer science) quality of experience (QoE) media_common |
| Zdroj: | Future Internet, Vol 11, Iss 8, p 171 (2019) Future Internet Volume 11 Issue 8 |
| ISSN: | 1999-5903 |
| DOI: | 10.3390/fi11080171 |
| Popis: | The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user&rsquo s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session. |
| Databáze: | OpenAIRE |
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