Adaptive bitstream switching of scalable video

Lotfallah, Osama; Auwera, Geert Van Der; Reisslein, Martin

doi:10.1016/j.image.2007.06.002

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…[34][35][36][37][38]), we present a client-side controller. The advantages of client-side intelligence are reduced complexity of decision making (instead of the server that computes/applies individual strategies based on end-toend bandwidth for each client, the clients themselves calculate their own strategy) and the possibility for users to personalize the streaming strategies (i.e.…”

Section: Resultsmentioning

confidence: 99%

Client intelligence for adaptive streaming solutions

Jarnikov

Özçelebi

2011

Signal Processing: Image Communication

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Section: Resultsmentioning

confidence: 99%

Client intelligence for adaptive streaming solutions

Jarnikov

Özçelebi

2011

Signal Processing: Image Communication

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On the performance of scalable video coding for VBR TV channels transport in multiple resolutions and qualities

Avramova

Vleeschauwer²,

Debevere

et al. 2010

Multimed Tools Appl

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Video broadcast operators target a variety of receiving devices of different resolutions and processing capabilities. In such a heterogeneous TV network, the transport resource consumption is likely to increase. In this paper we estimate the required transport capacity for a broadcast TV network taking into account parameters as currently proposed in standardization bodies. We target constant video quality, hence the TV channel has variable bit rate (VBR). We consider a multicast-based transport system where only the required versions of a TV channel are transported; this leads to fluctuation of the consumed transport capacity over time. The main focus in this paper is on the comparison of a simulcast and a scalable video coding (SVC) transport scheme in several realistic examples with different encoding modes, including spatial and/or quality scalability. To estimate the required transport capacity for simulcast and SVC, we use a comprehensive toolkit based on a Gaussian approximation of the capacity demand. In order to obtain realistic input values for our calculation tools, we characterize the fluctuations of the bit rate associated with a TV channel by encoding a representative set of video clips. Based on the considered realistic examples, we explore under what conditions either the simulcast or the SVC transport scheme is more efficient

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Classification of Quality Defects using Multivariate Control Chart with Ensemble Machine Learning Model

Demircioğlu Diren,

Boran

2024

jista

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Multivariate control charts enable to monitor processes affected by more than one variable. But, when the process is out of control, it cannot detect which variable is causing it. It is an important requirement to know which variables in the process need corrective actions. In this study, a machine learning-based model is proposed to predict the variable/s that make the process out of control. For this purpose, ensemble algorithms, which are known to have higher prediction performance than single algorithms, were preferred. Because it is aimed to determine the variable(s) that cause the process to be out of control in the most accurate way. It is thought that a classification model in which ensemble algorithms are used together can increase the prediction accuracy. The model, which has not been encountered before in a quality control problem, was applied to a real problem and 98.06% classification accuracy was achieved. Another benefit is that it can predict the variable/variables that make the process uncontrolled without the need for multivariate control charts.

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Adaptive bitstream switching of scalable video

Cited by 3 publications

References 39 publications

Client intelligence for adaptive streaming solutions

Client intelligence for adaptive streaming solutions

On the performance of scalable video coding for VBR TV channels transport in multiple resolutions and qualities

Classification of Quality Defects using Multivariate Control Chart with Ensemble Machine Learning Model

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