BernoulliZip: a Compression Algorithm for Bernoulli Processes and Erdos-Renyi Graphs
Amirmohammad Farzaneh, Mihai-Alin Badiu, Justin P. Coon
TL;DR
BernoulliZip is a new, efficient compression algorithm designed for Bernoulli processes and Erdos-Renyi graphs, focusing on near-optimal prefix coding by first encoding the number of ones and then the sequence.
Contribution
The paper introduces BernoulliZip, a novel compression scheme that efficiently encodes Bernoulli trials and applies it to Erdos-Renyi graph compression.
Findings
BernoulliZip achieves near-optimal compression performance.
The method efficiently encodes the number of ones before the sequence.
Application to Erdos-Renyi graphs demonstrates versatility.
Abstract
A novel compression scheme for compressing the outcome of independent Bernoulli trials is introduced and analysed. The resulting algorithm, BernoulliZip, is a fast and near-optimal method to produce prefix codes for a Bernoulli process. BernoulliZip's main principle is to first represent the number of 1s in the sequence and then specify the sequence. The application of BernoulliZip on compressing Erdos-Renyi graphs is explored.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAlgorithms and Data Compression · Cellular Automata and Applications · Computability, Logic, AI Algorithms