The impact of quality of service parameters to the subjective and objective video quality assessment

Bondžulić, Boban; Petrović, Vladimir; Stanojević, Darko; Pavlović, Bojan

doi:10.2298/sjee1801097s

Cited by 5 publications

(13 citation statements)

References 8 publications

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“…SSIM and PSNR mainly differ on their degree of sensitivity to image degradations. PSNR is one of the most commonly used objective measures but it often has been critiqued for providing results that are not fully consistent with subjective quality assessments [1]. However, its ease of implementation and interpretation make it valuable [24].…”

Section: Related Studiesmentioning

confidence: 99%

“…A video assessment method based on delay, jitter, PLR and bandwidth found QoS/QoE was closely related to video quality degradation [26]. A method that processed delay, jitter, packet loss rate and bandwidth to determine four types of video quality degradations was reported [1]. They analysed the impact of different levels of video degradation.…”

Section: Related Studiesmentioning

confidence: 99%

“…The resulting mismatch in the frame sequence numbers results in inaccuracies when comparing the original and received frames to establish video quality [24,28]. Another limitation of current objective QoE methods is that they typically rely on peak signal to noise ratio (PSNR), structural similarity index (SSIM) or video quality metric (VQM), which do not always provide consistent assessment [1,9,26,29]. Therefore, in our study, a further video quality measurement parameter called image distance (ID) was included.…”

Section: Related Studiesmentioning

confidence: 99%

“…The scoring was based on ITU-T Recommendation P.800 [25]. This opinion score allocates values from bad to excellent by mapping the quantitative MOS as excellent (5), good (4), fair (3), poor (2) and bad (1). A comparison of Figure 16 (from FIS3, objective test) and 17 (MOS, subjective test) show that they had a similar quality trend over time.…”

Section: Parametermentioning

confidence: 99%

“…A grouping is related to the traffic and can be characterized by packet transmission delay, jitter and loss. These traffic measures can be accommodated as part of a quality of service (QoS) assessment [1,2]. The second grouping indicates the effects of noise and associated distortion on the images making up a video.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Salama¹,

Saatchi²

2019

Technologies

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Video transmission over wireless computer networks is increasingly popular as new applications emerge and wireless networks become more widespread and reliable. An ability to quantify the quality of a video transmitted using a wireless computer network is important for determining network performance and its improvement. The process requires analysing the images making up the video from the point of view of noise and associated distortion as well as traffic parameters represented by packet delay, jitter and loss. In this study a modular fuzzy logic based system was developed to quantify the quality of video transmission over a wireless computer network. Peak signal to noise ratio, structural similarity index and image difference were used to represent the user's quality of experience (QoE) while packet delay, jitter and percentage packet loss ratio were used to represent traffic related quality of service (QoS). An overall measure of the video quality was obtained by combining QoE and QoS values. Systematic sampling was used to reduce the number of images processed and a novel scheme was devised whereby the images were partitioned to more sensitively localize distortions. To further validate the developed system, a subjective test involving 25 participants graded the quality of the received video. The image partitioning significantly improved the video quality evaluation. The subjective test results correlated with the developed fuzzy logic approach. The video quality assessment developed in this study was compared against a method that uses spatial efficient entropic differencing and consistent results were observed. The study indicated that the developed fuzzy logic approaches could accurately determine the quality of a wirelessly transmitted video.

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Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Salama¹,

Saatchi²

2019

Technologies

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A User Prioritisation Algorithm for Horizontal Handover in Dense WLANs

Aldhaibani

Raschellà

Mohi-Ud-Din

et al. 2021

Int J Wireless Inf Networks

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This paper proposes an algorithm that enhances horizontal handover (HO) in dense wireless local area networks (WLANs), which is implemented in a software-defined wireless networking (SDWN)-based architecture. The algorithm considers the concept of user prioritisation, classifying the WLAN stations (STAs) into two categories representing high and low priorities respectively, and always attempts to guarantee the best quality of experience (QoE) to the high priority users. The architecture that implements the algorithm leverages the flexibility, programmability, and centralised nature of SDWN to efficiently manage the HO process. Moreover, the paper presents a performance evaluation campaign that demonstrates significant achievements against a state-of-the-art solution in terms of the provided QoE, throughput and delay. Finally, we discuss the importance of considering user prioritisation in a HO algorithm for dense WLANs.

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