DeepRS: Deep-learning Based Network-Adaptive FEC for Real-Time Video Communications

01/22/2020

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This work proposes an innovative approach to handle packet loss in real-time video streaming scenarios in a more sophisticated way – Predicting packet loss pattern on time field by deep learning model.

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DeepRS: Deep-learning Based Network-Adaptive FEC for Real-Time Video Communications

Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Perceived Audiovisual Quality Modelling based on Decison Trees, Genetic Programming and Neural Networks

Real-Time Violence Detection Using CNN-LSTM

The Effect of Erasure Coding on the Burstiness of Packet Loss

tPLCnet: Real-time Deep Packet Loss Concealment in the Time Domain Using a Short Temporal Context

On Deep Speech Packet Loss Concealment: A Mini-Survey

Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection

DeepRS: Deep-learning Based Network-Adaptive FEC for Real-Time Video Communications

Related Research

Improving Content-Aware Video Streaming in Congested Networks with In-Network Computing

Perceived Audiovisual Quality Modelling based on Decison Trees, Genetic Programming and Neural Networks

Real-Time Violence Detection Using CNN-LSTM

The Effect of Erasure Coding on the Burstiness of Packet Loss

tPLCnet: Real-time Deep Packet Loss Concealment in the Time Domain Using a Short Temporal Context

On Deep Speech Packet Loss Concealment: A Mini-Survey

Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection