Automated Lossless Data Compression via Dynamic Fractal Encoding and Adaptive Context Modeling (ALDFAC-ACM)

**Abstract:** This paper introduces Automated Lossless Data Compression via Dynamic Fractal Encoding and Adaptive Context Modeling (ALDFAC-ACM), a novel data compression framework designed to achieve superior compression ratios compared to existing lossless techniques, particularly for multimedia datasets. ALDFAC-ACM synergistically merges dynamic fractal encoding, leveraging self-similarity patterns within data, with an adaptive context modeling (ACM) system that dynamically adjusts its compression parameters based on the statistical properties of the input data. This approach significantly reduces redundancy and allows for highly efficient data representation. The system is readily commercializable and exhibits potential across a broad landscape, particularly in archival storage, digital asset management, and high-throughput data transmission.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-13
DOI
https://doi.org/10.5281/zenodo.17336203
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated Lossless Data Compression via Dynamic Fractal Encoding and Adaptive Context Modeling (ALDFAC-ACM)

Zenodo (CERN European Organization for Nuclear Research)
Algorithms and Data Compression
article

Automated Lossless Data Compression via Dynamic Fractal Encoding and Adaptive Context Modeling (ALDFAC-ACM)

article en

Abstract

**Abstract:** This paper introduces Automated Lossless Data Compression via Dynamic Fractal Encoding and Adaptive Context Modeling (ALDFAC-ACM), a novel data compression framework designed to achieve superior compression ratios compared to existing lossless techniques, particularly for multimedia datasets. ALDFAC-ACM synergistically merges dynamic fractal encoding, leveraging self-similarity patterns within data, with an adaptive context modeling (ACM) system that dynamically adjusts its compression parameters based on the statistical properties of the input data. This approach significantly reduces redundancy and allows for highly efficient data representation. The system is readily commercializable and exhibits potential across a broad landscape, particularly in archival storage, digital asset management, and high-throughput data transmission.

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Openalex Percentile: Top 24%
Algorithms and Data Compression
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