Data-driven information compression based on machine learning

V.O. Lesyk, A.Yu. Doroshenko

Abstract


The latest data compression methods and systems have come a long way, from the emergence of new artificial intelligence techniques to the widespread use of methods of various types and purposes, which has flooded the research landscape and complicated the process of selecting and identifying candidate models for effective application, further improvement, and research. This article proposes an empirical approach to comparing trained compression methods and models within specific frameworks, highlighting potential application areas, the best models at the current stage of development, and the characteristics, advantages, and disadvantages of current models. An assessment of the general applicability of trained compression and the theoretical limits of entropy is conducted. A series of comparisons is performed, taking into account various subject areas of model application; a set of criteria for comparing and evaluating the implementation effectiveness of each of the considered methods is described; key problem areas that need to be assessed prior to implementing the models into technological processes and as well as their use from the perspective of ordinary users of software applications or data compression packages. An additional assessment of potential technologies for the further development and improvement of learned compression methods is provided, along with an identification of systems and technologies that are losing their potential for progress. This analysis allows researchers to gain a general understanding of the current state of research on the application of machine learning to various compression problems, assess the state of technological development, and—based on the limitations and prob lem areas identified—select the appropriate direction for further research or for scientific literature discovery.

Problems in programming 2026; 3: 102-110


Keywords


machine learning; learned data compression; data compression with neural networks; hybrid com pression methods; artificial intelligence

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DOI: https://doi.org/10.15407/pp2026.03.102

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