Reducing System Load of Effective Video Using a Network Model

Recently, as non-face-to-face work has become more common, the development of streaming services has become a significant issue. As these services are applied in increasingly diverse fields, various problems are caused by the overloading of systems when users try to transmit high-quality images. In...

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Bibliographic Details
Published in:Applied sciences 2021-10, Vol.11 (20), p.9665
Main Authors: Cho, Soo-Young, Kim, Dae-Yeol, Oh, Su-Yeong, Sohn, Chae-Bong
Format: Article
Language:English
Subjects:
Cameras
DAIN
Deep learning
FBF
front on the backward frame
Image quality
Image transmission
Interpolation
Overloading
segmentation
SST
Streaming
Streaming media
Streaming services
Teaching methods
Traffic congestion
Video transmission
Visual field
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Summary:Recently, as non-face-to-face work has become more common, the development of streaming services has become a significant issue. As these services are applied in increasingly diverse fields, various problems are caused by the overloading of systems when users try to transmit high-quality images. In this paper, SRGAN (Super Resolution Generative Adversarial Network) and DAIN (Depth-Aware Video Frame Interpolation) deep learning were used to reduce the overload that occurs during real-time video transmission. Images were divided into a FoV (Field of view) region and a non-FoV (Non-Field of view) region, and SRGAN was applied to the former, DAIN to the latter. Through this process, image quality was improved and system load was reduced.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11209665