| Title: | State-of-the-art trends in data compression : COMPROMISE case study |
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| Authors: | ID Podgorelec, David (Author)
ID Strnad, Damjan (Author)
ID Kolingerová, Ivana (Author)
ID Žalik, Borut (Author) |
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| Files: |  entropy-26-01032.pdf (1,13 MB)
MD5: 2AC802FB229DAB9AE76682A10165E43F
 https://www.mdpi.com/1099-4300/26/12/1032
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight. In this article, we try to critically evaluate the most prominent of these trends and to explore their parallels, complementarities, and differences. Digital data restoration mimics the human ability to omit memorising information that is satisfactorily retrievable from the context. Feature-based data compression introduces a two-level data representation with higher-level semantic features and with residuals that correct the feature-restored (predicted) data. The integration of the advantages of individual domain-specific data compression methods into a general approach is also challenging. To the best of our knowledge, a method that addresses all these trends does not exist yet. Our methodology, COMPROMISE, has been developed exactly to make as many solutions to these challenges as possible inter-operable. It incorporates features and digital restoration. Furthermore, it is largely domain-independent (general), asymmetric, and universal. The latter refers to the ability to compress data in a common framework in a lossy, lossless, and near-lossless mode. COMPROMISE may also be considered an umbrella that links many existing domain-dependent and independent methods, supports hybrid lossless–lossy techniques, and encourages the development of new data compression algorithms |
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| Keywords: | data compression, data resoration, universal algorithm, feature, residual |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 18.09.2024 |
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| Article acceptance date: | 27.11.2024 |
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| Publication date: | 29.11.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | 26 str. |
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| Numbering: | Vol. 26, iss. 12, art. no. 1032 |
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| PID: | 20.500.12556/DKUM-91776  |
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| UDC: | 004.6 |
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| ISSN on article: | 1099-4300 |
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| COBISS.SI-ID: | 217954307 |
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| DOI: | 10.3390/e26121032 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 04.02.2025 |
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| Views: | 177 |
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| Downloads: | 19 |
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| Metadata: |    |
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| Categories: | Misc.
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