Open AccessFuzzy logic inference system‐based hybrid quality prediction model for wireless 4kUHD H.265‐coded video streaming
Author(s) -
Alreshoodi Mohammed,
AdeyemiEjeye Anthony Olufemi,
Woods John,
Walker Stuart D.
Publication year - 2015
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/iet-net.2015.0018
Subject(s) - computer science , quality of service , video quality , real time computing , wireless network , fuzzy logic , artificial intelligence , wireless , artificial neural network , machine learning , data mining , computer network , telecommunications , metric (unit) , operations management , economics
Networked visual applications such video streaming have grown exponentially in recent years, yet are known to be sensitive to network impairments. However, available measurement techniques that adopt a full reference model are impractical in real‐time streaming because they require the original video sequence available at the receivers side. The primary aim of this study is to present a hybrid no‐reference prediction model for the perceptual quality of 4kUHD H.265‐coded video in the wireless domain. The contributions of this paper are two‐fold: first, an investigation of the impact of quality of service (QoS) parameters on 4kUHD H.265‐coded video transmission in an experimental environment; second, objective model based on fuzzy logic inference system is developed to predict the visual quality by mapping QoS parameters to the measured quality of experience. The model is evaluated in contrast to random neural networks. The results show that good prediction accuracy was obtained from the proposed hybrid prediction model. This study will help in the development of a reference‐free video quality prediction model and QoS control methods for 4kUHD video streaming.