Low-Complexity Vector Source Coding for Discrete Long Sequences with Unknown Distributions
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866910669277757440 |
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| author | Woldemariam, Leah Liu, Hang Scaglione, Anna |
| author_facet | Woldemariam, Leah Liu, Hang Scaglione, Anna |
| contents | In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress data by sacrificing computational efficiency or by assuming the data follows a known distribution. We take advantage of the structure that arises within the spatial representation and utilize it to encode run-lengths within this representation using Golomb coding. Through theoretical analysis, we show that our scheme yields an overall bit rate that nears entropy without a computationally complex encoding algorithm and verify these results through numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_05633 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Low-Complexity Vector Source Coding for Discrete Long Sequences with Unknown Distributions Woldemariam, Leah Liu, Hang Scaglione, Anna Information Theory In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress data by sacrificing computational efficiency or by assuming the data follows a known distribution. We take advantage of the structure that arises within the spatial representation and utilize it to encode run-lengths within this representation using Golomb coding. Through theoretical analysis, we show that our scheme yields an overall bit rate that nears entropy without a computationally complex encoding algorithm and verify these results through numerical experiments. |
| title | Low-Complexity Vector Source Coding for Discrete Long Sequences with Unknown Distributions |
| topic | Information Theory |
| url | https://arxiv.org/abs/2309.05633 |