Reducing System Load of Effective Video Using a Network Model

Applied Sciences

Su-Yeong Oh¹  Chae-Bong Sohn¹  Soo-Young Cho¹  Dae-Yeol Kim¹ 

[1] Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea;

关键词: DAIN;    SST;    FBF;    front on the backward frame;    segmentation;   

DOI  :  10.3390/app11209665

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown