Linear Progressive Coding for Semantic Communication using Deep Neural Networks

Eva Riherd; Raghu Mudumbai; Weiyu Xu

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arXiv · 2309.15959

Linear Progressive Coding for Semantic Communication using Deep Neural Networks

Eva Riherd·Raghu Mudumbai·Weiyu Xu

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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.

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BibTeX RIS

Eva Riherd, Raghu Mudumbai, Weiyu Xu. 2023-09-27. Linear Progressive Coding for Semantic Communication using Deep Neural Networks. https://arxiv.org/abs/2309.15959

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