Preprint
Linear Progressive Coding for Semantic Communication using Deep Neural Networks
ArXiv.org
Cornell University
09/27/2023
DOI: 10.48550/arxiv.2309.15959
Abstract
We propose a general method for semantic representation of images and other
data using progressive coding. Semantic coding allows for specific pieces of
information to be selectively encoded into a set of measurements that can be
highly compressed compared to the size of the original raw data. We consider a
hierarchical method of coding where a partial amount of semantic information is
first encoded a into a coarse representation of the data, which is then refined
by additional encodings that add additional semantic information. Such
hierarchical coding is especially well-suited for semantic communication i.e.
transferring semantic information over noisy channels. Our proposed method can
be considered as a generalization of both progressive image compression and
source coding for semantic communication. We present results from experiments
on the MNIST and CIFAR-10 datasets that show that progressive semantic coding
can provide timely previews of semantic information with a small number of
initial measurements while achieving overall accuracy and efficiency comparable
to non-progressive methods.
Details
- Title: Subtitle
- Linear Progressive Coding for Semantic Communication using Deep Neural Networks
- Creators
- Eva RiherdRaghu MudumbaiWeiyu Xu
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2309.15959
- ISSN
- 2331-8422
- Publisher
- Cornell University
- Language
- English
- Date posted
- 09/27/2023
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984473780502771
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