# On Lossless Causal Compression of Periodic Signals

## TL;DR

This paper introduces a lossless causal compression method for periodic signals that uses mixing with another periodic signal, enabling reconstruction under specific non-resonance conditions, with applications in communication and digital photography.

## Contribution

The paper proposes a novel lossless causal compression scheme for periodic signals based on mixing with another periodic signal, highlighting its applicability in communication and imaging systems.

## Key findings

- Compression relies on non-resonance conditions between periods.
- The scheme is inherently suitable for round-robin communication networks.
- Applicable to digital photography with active pixel sensors.

## Abstract

We present and study a scheme for lossless causal compression of periodic real-valued signals. In particular, our technique compresses a vector-valued signal to a scalar-valued signal by mixing it with another periodic signal. The conditions for being able to reconstruct the original signal then amount to certain non-resonances between the periods of the two signals. The proposed compression scheme turns out to implicitly be inherent to communication networks with round-robin scheduling and digital photography with active pixel sensors.