27 citations · 41 across the 12 of their papers we have counts for
5 papers · 1 filter
Generative Joint Source-Channel Coding for Semantic Image Transmission
Ecenaz Erdemir, Tze-Yang Tung, Pier Luigi Dragotti +1
Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission…
DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski +1
Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem. Very promising…
DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding
Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski +1
Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques. Very promising results in end-to-end image qual…
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission
Tze-Yang Tung, Deniz Gündüz
We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map…
SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity
Tze-Yang Tung, Deniz Gündüz
A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved…