Symmetry and Asymmetry in Data Compression
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 48
Special Issue Editors
Interests: computer graphics; geometric modeling; computer geometry; CAD/CAM systems; geometric design with geometric features; geometric design with geometric constraints; multimedia; virtual reality
Interests: artificial intelligence; machine learning; computer vision; medical image analysis and 3D construction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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.
- 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.
- From an algorithm perspective, the distinction between symmetric and asymmetric data compression algorithms implies differences in the design and use of the two categories.
- Time-varying data are sometimes characterised by a somewhat symmetric behaviour over time, which can improve their compression.
- 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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- data compression
- symmetry detection
- algorithm analysis
- exact and approximate symmetry
- lossless and lossy data compression
- symmetrical and asymmetrical data compression algorithms
- spatial, time-varying and non-geometric data
- feature-based data compression
- error control
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
