Lossless Data Compression with Error Detection using Cantor Set
Nithin Nagaraj

TL;DR
This paper enhances lossless data compression using GLS-coding by integrating error detection through Cantor sets, enabling detection of small errors and improving robustness in noisy environments.
Contribution
It introduces a novel method of embedding error detection into GLS-coding by utilizing Cantor sets with controllable fractal dimensions.
Findings
Repetition codes are shown to lie on Cantor sets with fractal dimension 1/n.
Embedding the initial value on a Cantor set enables error detection.
Error detection capability can be tuned based on channel noise levels.
Abstract
In 2009, a lossless compression algorithm based on 1D chaotic maps known as Generalized Lur\"{o}th Series (or GLS) has been proposed. This algorithm (GLS-coding) encodes the input message as a symbolic sequence on an appropriate 1D chaotic map (GLS) and the compressed file is obtained as the initial value by iterating backwards on the map. For ergodic sources, it was shown that GLS-coding achieves the best possible lossless compression (in the noiseless setting) bounded by Shannon entropy. However, in the presence of noise, even small errors in the compressed file leads to catastrophic decoding errors owing to sensitive dependence on initial values. In this paper, we first show that Repetition codes (every symbol is repeated times, where is a positive odd integer), the oldest and the most basic error correction and detection codes in literature, actually lie on a…
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
TopicsChaos-based Image/Signal Encryption · Mathematical Dynamics and Fractals · Algorithms and Data Compression
