| Title: | A case study on entropy-aware block-based linear transforms for lossless image compression |
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| Authors: | ID Žalik, Borut (Author)
ID Podgorelec, David (Author)
ID Kolingerová, Ivana (Author)
ID Strnad, Damjan (Author)
ID Kohek, Štefan (Author) |
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| Files: |  s41598-024-79038-2.pdf (5,13 MB)
MD5: 51E87AFB067021C880E9B5D32E87D844
 https://www.nature.com/articles/s41598-024-79038-2#article-info
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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: | Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within , including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods. Thirty-two different greyscale raster images with varying resolutions and contents were used in the experiments. It was shown that reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in is then evaluated. It was confirmed that, despite this additional cost, the estimated size in bytes is smaller in comparison to the sizes achieved by the JPEG LS and CALIC predictors. |
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| Keywords: | computer science, information entropy, prediction, inverse distance transform, string transformations |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 27.07.2024 |
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| Article acceptance date: | 05.11.2024 |
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| Publication date: | 28.11.2024 |
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| Publisher: | Springer Nature |
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| Year of publishing: | 2024 |
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| Number of pages: | 15 str. |
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| Numbering: | let. 14 |
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| PID: | 20.500.12556/DKUM-91488  |
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| UDC: | 004.9 |
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| ISSN on article: | 2045-2322 |
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| COBISS.SI-ID: | 217965571 |
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| DOI: | 10.1038/s41598-024-79038-2 |
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| Copyright: | © The Author(s) 2024 |
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| Publication date in DKUM: | 07.01.2025 |
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| Views: | 186 |
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| Downloads: | 20 |
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| Metadata: |    |
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| Categories: | Misc.
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