2017/03/06 by Imen Triki, Quanyan Zhu, Triki, Imen +7
Computer Science · #FOS: Computer and information sciences #Image and Video Quality Assessment #Multimedia (cs.MM) #Video Coding and Compression Technologies #Visual Attention and Saliency Detection
paper · pdf · doi:10.48550/arxiv.1703.01986
openalex publication_date 2017/03/06 · openalex created_date 2017/03/16 · openalex updated_date 2026/07/28
The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization harder. This paper aims at taking a step further in order to address this limitation and meet users profiles. To do so, we propose a closed-loop control framework based on the users(subjective) feedbacks to learn the QoE function and optimize it at the same time. Our simulation results show that our system converges to a steady state, where the resulting QoE function noticeably improves the users feedbacks.