Spiral Data Compression: Self-Similar Encoding for Audio, Image and Vector Systems

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Auteur principal: Garbar, Iryna
Format: Recurso digital
Publié: Zenodo 2025
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author Garbar, Iryna
author_facet Garbar, Iryna
contents <p>This preprint introduces Spiral Data Compression (SDC), a geometric encoding framework that distributes <br>information along logarithmic spiral coordinates. The method reduces redundancy, eliminates block artifacts, <br>improves reconstructability, and enhances noise resilience by leveraging self-similarity and exponential scaling. <br>Applications include audio codecs, image encoding, vector compression, high-dimensional data clustering, <br>sensor data processing, and AI preprocessing.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_17821104
institution Zenodo
language
publishDate 2025
publisher Zenodo
record_format zenodo
spellingShingle Spiral Data Compression: Self-Similar Encoding for Audio, Image and Vector Systems
Garbar, Iryna
data compression
spiral encoding
self-similarity
image encoding
audio compression
vector geometry
geometric encoding
noise robustness
<p>This preprint introduces Spiral Data Compression (SDC), a geometric encoding framework that distributes <br>information along logarithmic spiral coordinates. The method reduces redundancy, eliminates block artifacts, <br>improves reconstructability, and enhances noise resilience by leveraging self-similarity and exponential scaling. <br>Applications include audio codecs, image encoding, vector compression, high-dimensional data clustering, <br>sensor data processing, and AI preprocessing.</p>
title Spiral Data Compression: Self-Similar Encoding for Audio, Image and Vector Systems
topic data compression
spiral encoding
self-similarity
image encoding
audio compression
vector geometry
geometric encoding
noise robustness
url https://doi.org/10.5281/zenodo.17821104