Distributed Compression in the Era of Machine Learning: A Review of Recent Advances
CoRR(2024)
摘要
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.
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关键词
Distributed source coding,Wyner–Ziv coding,lossy compression,binning,neural networks,rate–distortion theory,learning
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