5,853 research outputs found
On feedback in network source coding
We consider source coding over networks with
unlimited feedback from the sinks to the sources. We first show
examples of networks where the rate region with feedback is
a strict superset of that without feedback. Next, we find an
achievable region for multiterminal lossy source coding with
feedback. Finally, we evaluate this region for the case when one
of the sources is fully known at the decoder and use the result
to show that this region is a strict superset of the best known
achievable region for the problem without feedback
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
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