An autoencoder based approach to enable high fidelity video conferencing over low bandwidth networks

dc.contributor.author	Kulshrestha, S.
dc.contributor.author	Jain, A.
dc.contributor.author	Sahani, A.
dc.date.accessioned	2022-08-25T15:18:43Z
dc.date.available	2022-08-25T15:18:43Z
dc.date.issued	2022-08-25
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/3901
dc.description.abstract	Data lagging and distortion has been the issue for the majority of us since the usage of online video conferencing platforms has become a routine of our daily life. In this paper, we attempted to design a solution for this by specifically for the image part of the videos, by building a convolution neural network based autoencoder, which will compress the images being sent from one end to another in a batch of 5 frames, and stretch it back to its original size on the receiver end. We calculated the accuracy and loss obtained for the same for comparison purposes.	en_US
dc.language.iso	en_US	en_US
dc.subject	Autoencoders	en_US
dc.subject	Data compression	en_US
dc.subject	Image-Similarity-Measures	en_US
dc.subject	Keras	en_US
dc.subject	Machine learning	en_US
dc.subject	Neural networks	en_US
dc.subject	OpenCV	en_US
dc.subject	Python	en_US
dc.subject	Tensorflow	en_US
dc.title	An autoencoder based approach to enable high fidelity video conferencing over low bandwidth networks	en_US
dc.type	Article	en_US