Lossy data compression with random gates

TL;DR

This paper presents a lossy data compression method using random gates that approaches Shannon's bound and can be efficiently encoded with a linear-time algorithm inspired by statistical physics.

Contribution

It introduces a new compression protocol based on random gates, achieving near-optimal performance with efficient encoding.

Findings

Capacity converges exponentially to Shannon's bound with increasing variables per gate.

Random gates perform nearly as well as parity-check gates.

Encoding is achievable in linear time using Survey Inspired Decimation.

Abstract

We introduce a new protocol for a lossy data compression algorithm which is based on constraint satisfaction gates. We show that the theoretical capacity of algorithms built from standard parity-check gates converges exponentially fast to the Shannon's bound when the number of variables seen by each gate increases. We then generalize this approach by introducing random gates. They have theoretical performances nearly as good as parity checks, but they offer the great advantage that the encoding can be done in linear time using the Survey Inspired Decimation algorithm, a powerful algorithm for constraint satisfaction problems derived from statistical physics.