Neural Distributed Compressor Discovers Binning

TL;DR

This paper introduces a neural network-based lossy compression method for the Wyner-Ziv problem, demonstrating that binning and optimal index combination naturally emerge from data-driven learning without explicit source structure knowledge.

Contribution

It presents a novel neural compression scheme that discovers binning principles and optimal index combination in distributed source coding through end-to-end training.

Findings

Neural network-based compression recovers binning behavior.

The scheme approaches theoretical optimality in source coding.

Binning emerges naturally from data-driven learning without explicit design.

Abstract

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case of distributed source coding. To this day, practical approaches for the Wyner-Ziv problem have neither been fully developed nor heavily investigated. We propose a data-driven method based on machine learning that leverages the universal function approximation capability of artificial neural networks. We find that our neural network-based compression scheme, based on variational vector quantization, recovers some principles of the optimum theoretical solution of the Wyner-Ziv setup, such as binning in the source space as well as optimal combination of the quantization index and side information, for exemplary sources. These behaviors emerge although no structure exploiting knowledge of the source distributions was imposed. Binning is a widely used tool in information theoretic proofs and methods, and to our knowledge, this is the first time it has been explicitly observed to emerge from data-driven learning.