Towards building large scale live media streaming framework for a U-city

Ryu, Eun‐Seok

doi:10.1007/s11042-007-0166-z

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…The simplicity and scalability for implementing different configuration devices made it easy to adapt the proposed framework to other genres of video. In addition, it can support privacypreserving solutions [46] effectively by adapting efficient encryption techniques [47], [48]; it can analyze human activity recognition [49]; and can be adapted to three screen TV solutions [50]- [53]. The proposed method can also be adopted in ATSC 3.0 and can use over-the-top (OTT) services to provide a personalized interactive application.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

Mujtaba¹,

Malik²,

Ryu³

2022

Preprint

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This paper proposes a novel lightweight thumbnail container-based summarization (LTC-SUM) framework for full featurelength videos. This framework generates a personalized keyshot summary for concurrent users by using the computational resource of the end-user device. State-of-the-art methods that acquire and process entire video data to generate video summaries are highly computationally intensive. In this regard, the proposed LTC-SUM method uses lightweight thumbnails to handle the complex process of detecting events. This significantly reduces computational complexity and improves communication and storage efficiency by resolving computational and privacy bottlenecks in resource-constrained end-user devices. These improvements were achieved by designing a lightweight 2D CNN model to extract features from thumbnails, which helped select and retrieve only a handful of specific segments.Extensive quantitative experiments on a set of full 18 feature-length videos (approximately 32.9 h in duration) showed that the proposed method is significantly computationally efficient than state-of-the-art methods on the same end-user device configurations. Joint qualitative assessments of the results of 56 participants showed that participants gave higher ratings to the summaries generated using the proposed method. To the best of our knowledge, this is the first attempt in designing a fully client-driven personalized keyshot video summarization framework using thumbnail containers for feature-length videos.

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Section: Discussion and Limitationsmentioning

confidence: 99%

LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

Mujtaba¹,

Malik²,

Ryu³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thanks to its simplicity and scalability in implementing multiple device configurations [35], it can be easily adapted to other animated image formats, such as WebP, recommendation methods [36], [37], and other streaming protocols. In addition, by reducing the computational load of the servers, the proposed approach can act as a privacy protection solution by utilizing effective encryption methods [38]- [40] in three-screen TV solutions [41], [42]. Various client-based GIF generation realtime application scenarios for smartphones or set-top boxes can be considered.…”

Section: Discussionmentioning

confidence: 99%

LTC-GIF: Attracting More Clicks on Feature-length Sports Videos

Mujtaba¹,

Choi²,

Ryu³

2022

Preprint

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This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from full-length sports videos. In addition, instead of processing the entire video, small video segments are processed to generate artistic media. This makes the proposed approach more computationally efficient compared to the baseline approaches that create artistic media using the entire video. The proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data used during artistic media generation. When extensive experiments were conducted on the Nvidia Jetson TX2, the computational complexity of the proposed method was 3.57 times lower than that of the SoA method. In the qualitative assessment, GIFs generated using the proposed method received 1.02 higher overall ratings compared to the SoA method. To the best of our knowledge, this is the first technique that uses LTC to generate artistic media while providing lightweight and high-performance services even on resource constrained devices.

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“…Because of its simplicity and scalability for implementing various configuration devices [21], the proposed approach can be easily adapted to other animated images (WebP), recommendation techniques [25,43], and streaming protocols such as dynamic adaptive streaming over HTTP (DASH). In addition to reducing the required server computational resources, the proposed method can serve as a privacy-preserving solution using efficient encryption techniques [28,30] that can be integrated into other client-driven solutions [26,27]; moreover, it can be adapted in three-screen TV solutions [33,34].…”

Section: Discussionmentioning

confidence: 99%

Client-driven animated GIF generation framework using an acoustic feature

Mujtaba

Lee

Kim

et al. 2021

Multimed Tools Appl

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This paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device. The method analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch. Further, it processes a small video segment to generate the GIF instead of processing the entire video. This makes the proposed method computationally efficient, unlike baseline approaches that use entire videos to create GIFs. The proposed method retrieves and uses the audio file and video segment so that communication and storage efficiencies are improved in the GIF generation process. Experiments on a set of 16 videos show that the proposed approach is 3.76 times more computationally efficient than a baseline method on an Nvidia Jetson TX2. Additionally, in a qualitative evaluation, the GIFs generated using the proposed method received higher overall ratings compared to those generated by the baseline method. To the best of our knowledge, this is the first technique that uses an acoustic feature in the GIF generation process.

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Towards building large scale live media streaming framework for a U-city

Cited by 8 publications

References 10 publications

LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

LTC-GIF: Attracting More Clicks on Feature-length Sports Videos

Client-driven animated GIF generation framework using an acoustic feature

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