Low-Complexity Vector Source Coding for Discrete Long Sequences with Unknown Distributions

TL;DR

This paper introduces a low-complexity source coding method for long discrete sequences from unknown distributions, leveraging spatial structure and Golomb coding to approach entropy rates efficiently.

Contribution

It presents a novel encoding scheme that efficiently compresses data from unknown distributions without high computational complexity.

Findings

Achieves near-entropy compression rates

Uses Golomb coding for run-length encoding

Maintains low computational complexity

Abstract

In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress data by sacrificing computational efficiency or by assuming the data follows a known distribution. We take advantage of the structure that arises within the spatial representation and utilize it to encode run-lengths within this representation using Golomb coding. Through theoretical analysis, we show that our scheme yields an overall bit rate that nears entropy without a computationally complex encoding algorithm and verify these results through numerical experiments.