User-Driven Multimedia Adaptation Framework for Context-aware Learning Content Service

Rueangprathum, Atchara; Limsiroratana, Somchai; Witosurapot, Suntorn

doi:10.12720/jait.7.3.182-185

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…By viewing this consideration as a basic problem of multi-criteria decision making (MCDM) system, it looks promising that an optimal set of required solutions can be resulted. Our focus is on the Analytical Hierarchical Process (AHP) method [10], [11] since it is convenient to use and allows both qualitative and quantitative factors to be computed nicely together.…”

Section: Ahp Methodsmentioning

confidence: 99%

AHP-Based Resource Utilization Scheme at the Network Edge with Ad Hoc Network Gateway

Waedorkor¹,

Witosurapot²

2018

IJFCC

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A sensitive problem in active learning classroom affecting the performance of Internet video streams is caused from the network degradation during the times of overloaded traffics. In this paper, we investigate how such a problem can be solved by a mean of edge computing system running at a gateway device so that virtualized services for transcoded video streams can be offered at the local ad-hoc network. In addition, we explore the use of multi-criteria decision making mechanism with AHP method for suggesting sensible network resources that can yield possible video qualities. Based on the result obtained by empirical network traces on a video clip transfer, our proposed solution selects lower levels of network utilizations than those of DASH server for similar video resolutions and frame-rates. Hence, it is beneficial for empowering the teacher to deploy flexible network policies in times of congestion at ease so that the residue of network resource can be given to some other essential tasks.

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Section: Ahp Methodsmentioning

confidence: 99%

AHP-Based Resource Utilization Scheme at the Network Edge with Ad Hoc Network Gateway

Waedorkor¹,

Witosurapot²

2018

IJFCC

Self Cite

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“…Moreover, as mobile users are increasingly becoming quality-aware, the framework integrates novel mechanisms for decreasing the video quality in a controlled way, with the aim to support a good learner quality of experience (QoE) even in resource-constrained situations. In [19], the authors propose an application-layer and middleware-based solutions that increase network reliability and flexibility and pro- In [20], The authors present the architecture of an adaptive multimedia learning service, where their engine enables users to identify the best combination of adaptive features of visual and audio content.…”

Section: Full-reference Image Quality Assessmentmentioning

confidence: 99%

“…Their use in many real-time streaming scenarios could be unpractical, especially when the original image is not present [18][19][20] They provide an end-to-end quality assessment framework that could guarantee a high level of QoS They assessed a limited number of features [21][22][23] They train a regression model to predict the image quality score using multiple features in order to identify the remaining useful images' features when adapting the content No-Reference IQA [29][30][31] A universal 512-D face feature representation is provided to measure the quality of a given face Even though these techniques achieve [26,[34][35][36] The authors used the FR-IQA methods to annotate and train their CNN models.…”

Section: No-reference Image Quality Assessmentmentioning

confidence: 99%

An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time

Chami¹,

Jaoude²,

Chbeir³

2021

Preprint

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In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

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A supervised and unsupervised image quality assessment framework in real-time

Chami

Jaoude

Chbeir

2022

Internet of Multimedia Things (IoMT)

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User-Driven Multimedia Adaptation Framework for Context-aware Learning Content Service

Cited by 4 publications

References 4 publications

AHP-Based Resource Utilization Scheme at the Network Edge with Ad Hoc Network Gateway

AHP-Based Resource Utilization Scheme at the Network Edge with Ad Hoc Network Gateway

An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time

A supervised and unsupervised image quality assessment framework in real-time

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