Symmetry and Asymmetry in Data Compression

Dear Colleagues,

Data compression represents one of the most traditional disciplines in computer science with the first, still used, solutions created in the late 1940s. At the end of the last millennium, it experienced a boom, coinciding with the invention of the Internet and the rise in digital multimedia. In recent years, however, new paradigms are re-emerging, built on the foundation of machine learning, cross-domain generalisation and knowledge transfer, and predictions based on higher semantic features rather than on the limited context of a few samples. In this Special Issue, authors are invited to submit high-quality research or review articles that address the impact of the topics below, as well as other topics related to the symmetry or asymmetry of the choice, design, analysis, and use of data compression algorithms in general, or in specific data domains.

1. From a data perspective, symmetry denotes the repetition (semantic feature) of data patterns in their original or transformed form. Research topics include, but are not limited to, symmetry detection, representation, classification and organisation with the goal of enhancing data compression.
2. From an algorithm perspective, the distinction between symmetric and asymmetric data compression algorithms implies differences in the design and use of the two categories.
3. Time-varying data are sometimes characterised by a somewhat symmetric behaviour over time, which can improve their compression.
4. The relationship between lossless and lossy compression on the one hand and exact and approximate symmetries on the other is also of research interest. A lossless algorithm achieves a clearly better compression when exact symmetries are identified in the data. Symmetrisation can thus be an important preprocessing step in data compression.

Dr. David Podgorelec
Prof. Dr. Jie Yang
Guest Editors

Manuscript Submission Information

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