A parametric model for perceptual video quality estimation

Joskowicz, José; Ardao, J. Carlos López

doi:10.1007/s11235-010-9352-9

Cited by 20 publications

(10 citation statements)

References 19 publications

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“…The metrics used for the evaluation of the visual quality is the Mean Opinion Score (MOS) or the difference mean opinion score (DMOS). We suggested to use the parametric model [21] for estimation of the perceptual visual quality, that is, MOS on the user device by using

\begin{matrix} MOS = v_{3} (1 - \frac{1}{1 + {(B w / v_{4})}^{v_{5}}}), \end{matrix}

where MOS is the multimedia visual quality on a user's device in the range of (1–5), Bw is the available bandwidth, and v 3 , v 4 , and v 5 are the model parameters that are dependent on the codec, frame rate, and video screen size.…”

Section: The Proposed Network and Visual Quality Aware N-screen Comentioning

confidence: 99%

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

Ullah

Sarwar

Lee

2014

The Scientific World Journal

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We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user's N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user's N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues.

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\begin{matrix} MOS = v_{3} (1 - \frac{1}{1 + {(B w / v_{4})}^{v_{5}}}), \end{matrix}

Section: The Proposed Network and Visual Quality Aware N-screen Comentioning

confidence: 99%

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

Ullah

Sarwar

Lee

2014

The Scientific World Journal

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“…There are many QoE utility functions proposed in the literature to reflect the user QoE [32], [33], [34]. In this paper we used the QoE utility function proposed in [35] because it is widely used in literature.…”

Section: Qoe Utility Function (Quf)mentioning

confidence: 99%

Variable-threshold buffer based adaptation for DASH mobile Video Streaming

Abuteir

Fladenmuller

Fourmaux

et al. 2017

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)

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Dynamic Adaptive Streaming over HTTP (DASH) was introduced to enable high video quality streaming over HTTP. DASH depends on the adaptation logic at the client to choose which video bitrate to stream from the content server for each chunk. For clients receiving video over a cellular network, the cellular first hop tends to be the bandwidth bottleneck and can exhibit significant swings in available bandwidth. In this paper we develop and evaluate a Dynamic Adaptation for mobile Video Streaming (DAVS), a technique that can be used within DASH adaptation to handle the significant bandwidth variability experienced by cellular mobile clients. In our scheme the main innovation is that the client chooses a bitrate based on whether the playout buffer occupancy (BO) falls below or above a dynamic threshold. In addition, the scheme attempts to minimize bitrate switching by again delaying a change of video bitrate selection by a window of time -that is also dynamically determined. We evaluate the performance of DAVS over real traces collected from a mobile network operator. DAVS shows better performance over different video streaming metrics. Furthermore, It increases the QoE by a range 15% -55% compared to benchmark algorithms.

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“…Kusuma et al [46] developed the Reduced reference model for the estimation of perceptual visual quality. No reference parametric packet layer models have been proposed or studied in the past [47][48][49]. ITU-T G.1070 [8] is the recommendation based on the PLM of QoE estimation for videophone multimedia streaming services.…”

Section: Background and Related Workmentioning

confidence: 99%

QoS and QoE Aware N-Screen Multicast Service

Sarwar¹,

Ullah²,

Lee

2016

Journal of Sensors

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The paper focuses on ensuring the quality-of-service (QoS) and quality-of-experience (QoE) requirements of users having heterogeneous devices in a multicast session. QoS parameters such as bit rate, delays, and packet losses are good indicators for optimizing network services but fall short in characterizing user perception (QoE). In N-Screen service, the users have different devices with heterogeneous attributes like screen size, resolution, and access network interface, and the users have different QoE on N-Screen devices with the same QoS parameters. We formulate the objective function of the N-Screen multicast grouping to ensure the minimum user’s QoE with smaller bandwidth requirement. We propose a dynamic user reassignment scheme to maintain and satisfy the QoE by adapting the user’s membership to the varying network conditions. The proposed schemes combine the available bandwidth and multimedia visual quality to ensure the QoS and QoE. In the network architecture, we introduce the functions of the QoS and QoE aware multicast group management and the estimation schemes for the QoS and QoE parameters. The simulation results show that the proposed multicast service ensures the network QoS and guarantees the QoE of users in the varying network conditions.

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A parametric model for perceptual video quality estimation

Cited by 20 publications

References 19 publications

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

Variable-threshold buffer based adaptation for DASH mobile Video Streaming

QoS and QoE Aware N-Screen Multicast Service

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