A Variational Auto-Encoder Approach for Image Transmission in Wireless Channel

10/08/2020
by   Amir Hossein Estiri, et al.
0

Recent advancements in information technology and the widespread use of the Internet have led to easier access to data worldwide. As a result, transmitting data through noisy channels is inevitable. Reducing the size of data and protecting it during transmission from corruption due to channel noises are two classical problems in communication and information theory. Recently, inspired by deep neural networks' success in different tasks, many works have been done to address these two problems using deep learning techniques. In this paper, we investigate the performance of variational auto-encoders and compare the results with standard auto-encoders. Our findings suggest that variational auto-encoders are more robust to channel degradation than auto-encoders. Furthermore, we have tried to excel in the human perceptual quality of reconstructed images by using perception-based error metrics as our network's loss function. To this end, we use the structural similarity index (SSIM) as a perception-based metric to optimize the proposed neural network. Our experiments demonstrate that the SSIM metric visually improves the quality of the reconstructed images at the receiver.

READ FULL TEXT

page 1

page 6

research
05/30/2019

Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators

In this paper, we propose a novel structure for a cross-modal data assoc...
research
03/18/2019

Deep Learning for the Degraded Broadcast Channel

Machine learning has shown promising results for communications system p...
research
04/30/2019

The Level Weighted Structural Similarity Loss: A Step Away from the MSE

The Mean Square Error (MSE) has shown its strength when applied in deep ...
research
02/14/2018

Similarity measures for vocal-based drum sample retrieval using deep convolutional auto-encoders

The expressive nature of the voice provides a powerful medium for commun...
research
04/06/2018

Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Auto-Encoders

In this work, we research and evaluate the usage of convolutional variat...
research
10/30/2018

Generating new pictures in complex datasets with a simple neural network

We introduce a version of a variational auto-encoder (VAE), which can ge...
research
12/29/2020

TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

In this paper, we propose a framework called TrustMAE to address the pro...

Please sign up or login with your details

Forgot password? Click here to reset