Robust Distributed Compression with Learned Heegard-Berger Scheme
arxiv(2024)
摘要
We consider lossy compression of an information source when decoder-only side
information may be absent. This setup, also referred to as the Heegard-Berger
or Kaspi problem, is a special case of robust distributed source coding.
Building upon previous works on neural network-based distributed compressors
developed for the decoder-only side information (Wyner-Ziv) case, we propose
learning-based schemes that are amenable to the availability of side
information. We find that our learned compressors mimic the achievability part
of the Heegard-Berger theorem and yield interpretable results operating close
to information-theoretic bounds. Depending on the availability of the side
information, our neural compressors recover characteristics of the
point-to-point (i.e., with no side information) and the Wyner-Ziv coding
strategies that include binning in the source space, although no structure
exploiting knowledge of the source and side information was imposed into the
design.
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