Distributed Compression in the Era of Machine Learning: A Review of Recent Advances
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909105023614976 |
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| author | Ozyilkan, Ezgi Erkip, Elza |
| author_facet | Ozyilkan, Ezgi Erkip, Elza |
| contents | Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. While information-theoretic foundations of distributed compression are well investigated, the impact of theory in practice-oriented applications to this day has been somewhat limited. As the field of data compression is undergoing a transformation with the emergence of learning-based techniques, machine learning is becoming an important tool to reap the long-promised benefits of distributed compression. In this paper, we review the recent contributions in the broad area of learned distributed compression techniques for abstract sources and images. In particular, we discuss approaches that provide interpretable results operating close to information-theoretic bounds. We also highlight unresolved research challenges, aiming to inspire fresh interest and advancements in the field of learned distributed compression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed Compression in the Era of Machine Learning: A Review of Recent Advances Ozyilkan, Ezgi Erkip, Elza Information Theory Signal Processing Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. While information-theoretic foundations of distributed compression are well investigated, the impact of theory in practice-oriented applications to this day has been somewhat limited. As the field of data compression is undergoing a transformation with the emergence of learning-based techniques, machine learning is becoming an important tool to reap the long-promised benefits of distributed compression. In this paper, we review the recent contributions in the broad area of learned distributed compression techniques for abstract sources and images. In particular, we discuss approaches that provide interpretable results operating close to information-theoretic bounds. We also highlight unresolved research challenges, aiming to inspire fresh interest and advancements in the field of learned distributed compression. |
| title | Distributed Compression in the Era of Machine Learning: A Review of Recent Advances |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2402.07997 |