We study the effects of quantization and additive white Gaussian noise (AWGN) in transmitting latent representations of images over a noisy communication channel. The latent representations are obtained using autoencoders (AEs). We analyze image reconstruction and classification performance for different channel noise powers, latent vector sizes, and number of quantization bits used for the latent variables as well as AEs' parameters. The results show that the digital transmission of latent representations using conventional AEs alone is extremely vulnerable to channel noise and quantization effects. We then propose a combination of basic AE and a denoising autoencoder (DAE) to denoise the corrupted latent vectors at the receiver. This approach demonstrates robustness against channel noise and quantization effects and enables a significant improvement in image reconstruction and classification performance particularly in adverse scenarios with high noise powers and significant quantization effects.
- communication channels
- data compression
- deep learning
- denoising autoencoders
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering