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State-of-the-Art Trends in Data Compression: COMPROMISE Case Study.

David Podgorelec1, Damjan Strnad1, Ivana Kolingerová2

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia.

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Summary
This summary is machine-generated.

Data compression research is evolving beyond traditional methods to meet new computing challenges. A new universal framework, COMPROMISE, integrates digital restoration and feature-based compression for versatile data handling.

Keywords:
data compressiondata restorationfeatureresidualuniversal algorithm

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Area of Science:

  • Computer Science
  • Information Theory

Background:

  • Traditional data compression algorithms (e.g., JPEG, MP3) achieved high compression ratios but are now challenged by new computing paradigms.
  • Emerging trends include digital data restoration and feature-based compression, requiring new approaches beyond domain-specific methods.

Purpose of the Study:

  • To critically evaluate prominent new trends in data compression.
  • To explore the parallels, complementarities, and differences among these trends.
  • To introduce a novel methodology that addresses these challenges and integrates various approaches.

Main Methods:

  • The study critically evaluates emerging data compression trends like digital data restoration and feature-based compression.
  • A new methodology, COMPROMISE, is developed to integrate these trends and existing methods.
  • COMPROMISE is designed to be domain-independent, asymmetric, and universal, supporting lossy, lossless, and near-lossless compression.

Main Results:

  • Existing data compression methods face limitations with modern computing paradigms like cloud and edge computing.
  • Digital data restoration and feature-based compression offer new avenues for data compression.
  • A unified, domain-independent method addressing these trends is currently lacking.

Conclusions:

  • The COMPROMISE methodology offers an interoperable framework for diverse data compression challenges.
  • It integrates digital restoration and feature-based compression, promoting a general, universal approach.
  • COMPROMISE aims to link existing methods, support hybrid techniques, and foster future algorithm development.