scholarly journals Reducing System Load of Effective Video Using a Network Model

2021 ◽  
Vol 11 (20) ◽  
pp. 9665
Author(s):  
Soo-Young Cho ◽  
Dae-Yeol Kim ◽  
Su-Yeong Oh ◽  
Chae-Bong Sohn
Keyword(s):  
Video Transmission ◽  
Super Resolution ◽  
Field Of View ◽  
Video Frame ◽  
Generative Adversarial Network ◽  
Frame Interpolation ◽  
System Load ◽  
Face To Face ◽  
Adversarial Network ◽  
Streaming Services

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.

Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network

2021 ◽  
Vol 58 (8) ◽  
pp. 0810005
Author(s):  
查体博 Zha Tibo ◽  
罗林 Luo Lin ◽  
杨凯 Yang Kai ◽  
张渝 Zhang Yu ◽  
李金龙 Li Jinlong
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Generative Adversarial Network ◽  
Image Reconstruction Algorithm ◽  
Adversarial Network

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

Direct Unsupervised Super-Resolution using Generative Adversarial Network (DUS-GAN) for Real-World Data

2021 ◽  
pp. 1-1
Author(s):  
Kalpesh Prajapati ◽  
Vishal Chudasama ◽  
Heena Patel ◽  
Kishor Upla ◽  
Kiran Raja ◽  
...  
Keyword(s):  
Real World ◽  
Super Resolution ◽  
Real World Data ◽  
Generative Adversarial Network ◽  
World Data ◽  
Adversarial Network

Infrared imagery super-resolution by using a generative adversarial network

2021 ◽  
Author(s):  
Trong-An Bui ◽  
Pei-Jun Lee ◽  
Kuan-Min Lee ◽  
Walter Wang ◽  
Shiual-Hal Shiu
Keyword(s):  
Super Resolution ◽  
Infrared Imagery ◽  
Generative Adversarial Network ◽  
Adversarial Network
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