Binary hologram compression using context based Bayesian tree models with adaptive spatial segmentation
With holographic displays requiring giga- or terapixel resolutions, data compression is of utmost importance in making holography a viable technique in the near future. In addition, since the first-generation of holographic displays is expected to require binary holograms, associated compression alg...
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| Veröffentlicht in: | Optics express Jg. 30; H. 14; S. 25597 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
04.07.2022
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| ISSN: | 1094-4087, 1094-4087 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | With holographic displays requiring giga- or terapixel resolutions, data compression is of utmost importance in making holography a viable technique in the near future. In addition, since the first-generation of holographic displays is expected to require binary holograms, associated compression algorithms are expected to be able to handle this binary format. In this work, the suitability of a context based Bayesian tree model is proposed as an extension to adaptive binary arithmetic coding to facilitate the efficient lossless compression of binary holograms. In addition, we propose a quadtree-based adaptive spatial segmentation strategy, as the scale dependent, quasi-stationary behavior of a hologram limits the applicability of the advocated modelling approach straightforwardly on the full hologram. On average, the proposed compression strategy produces files that are around 12% smaller than JBIG2, the reference binary image codec. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1094-4087 1094-4087 |
| DOI: | 10.1364/OE.457828 |