A case study on entropy-aware block-based linear transforms for lossless image compression
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 va...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-79038-2 |
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| author | Borut Žalik David Podgorelec Ivana Kolingerová Damjan Strnad Štefan Kohek |
| author_facet | Borut Žalik David Podgorelec Ivana Kolingerová Damjan Strnad Štefan Kohek |
| author_sort | Borut Žalik |
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| description | 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 $$\Phi$$ Φ is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within $$\Phi$$ Φ , 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 $$\Phi$$ Φ reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in $$\Phi$$ Φ 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. |
| format | Article |
| id | doaj-art-0c8867507d9c4b8d99b7400cbd7cfaaa |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-0c8867507d9c4b8d99b7400cbd7cfaaa2025-08-20T02:08:22ZengNature PortfolioScientific Reports2045-23222024-11-0114111510.1038/s41598-024-79038-2A case study on entropy-aware block-based linear transforms for lossless image compressionBorut Žalik0David Podgorelec1Ivana Kolingerová2Damjan Strnad3Štefan Kohek4Faculty of Electrical Engineering and Computer Science, University of MariborFaculty of Electrical Engineering and Computer Science, University of Maribor Department of Computer Science and Engineering, University of West BohemiaFaculty of Electrical Engineering and Computer Science, University of MariborFaculty of Electrical Engineering and Computer Science, University of MariborAbstract 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 $$\Phi$$ Φ is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within $$\Phi$$ Φ , 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 $$\Phi$$ Φ reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in $$\Phi$$ Φ 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.https://doi.org/10.1038/s41598-024-79038-2Computer scienceInformation entropyPredictionInverse distance transformString transformations |
| spellingShingle | Borut Žalik David Podgorelec Ivana Kolingerová Damjan Strnad Štefan Kohek A case study on entropy-aware block-based linear transforms for lossless image compression Scientific Reports Computer science Information entropy Prediction Inverse distance transform String transformations |
| title | A case study on entropy-aware block-based linear transforms for lossless image compression |
| title_full | A case study on entropy-aware block-based linear transforms for lossless image compression |
| title_fullStr | A case study on entropy-aware block-based linear transforms for lossless image compression |
| title_full_unstemmed | A case study on entropy-aware block-based linear transforms for lossless image compression |
| title_short | A case study on entropy-aware block-based linear transforms for lossless image compression |
| title_sort | case study on entropy aware block based linear transforms for lossless image compression |
| topic | Computer science Information entropy Prediction Inverse distance transform String transformations |
| url | https://doi.org/10.1038/s41598-024-79038-2 |
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