Fuzzy logic inference system‐based hybrid quality prediction model for wireless 4kUHD H.265‐coded video streaming. Issue 6 (1st November 2015)

• Record Type: Journal Article
• Authors: Alreshoodi, Mohammed; Adeyemi‐Ejeye, Anthony Olufemi; Woods, John; Walker, Stuart D.
• Is Part Of: IET networks. Volume 4:Issue 6(2015)
• Page Start: 296
• Page End: 303
• Publication Date: 2015-11-01
• Subjects: image sequences -- video coding -- video streaming -- fuzzy logic -- fuzzy reasoning -- quality of service -- quality of experience -- neural nets; fuzzy logic inference system‐based hybrid quality prediction model -- wireless 4kUHD H.265‐coded video streaming -- measurement techniques -- full reference model -- video sequence -- hybrid no‐reference prediction model -- wireless domain -- quality of service -- QoS parameter mapping -- 4kUHD H.265‐coded video transmission -- visual quality prediction -- quality of experience -- random neural networks -- reference‐free video quality prediction model -- QoS control methods; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2015.0018
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : 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.