Compression Optimality of Asymmetric Numeral Systems Article Swipe
Josef Pieprzyk
,
Jarek Duda
,
Marcin Pawłowski
,
Seyit Camtepe
,
Arash Mahboubi
,
Paweł Morawiecki
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.02228
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.02228
Compression also known as entropy coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming for instance) renews an interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from a beautiful mathematical structure, it is very efficient and offers compression with a very low residual redundancy. ANS works well for any symbol source statistics. Besides, ANS has become a preferred compression algorithm in the IT industry. However, designing ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression rate. The paper investigates compression optimality of ANS. It shows that ANS is optimal (i.e. the entropies of encoding and source are equal) for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to determine ANS compression rate precisely. We present two algorithms for finding ANS instances with high compression rates. The first explores state probability approximations in order to choose ANS instances with better compression rates. The second algorithm is a probabilistic one. It finds ANS instances, whose compression rate can be made as close to the best rate as required. This is done at the expense of the number $θ$ of internal random ``coin'' tosses. The algorithm complexity is ${\cal O}(θL^3)$, where $L$ is the number of ANS states. The complexity can be reduced to ${\cal O}(θL\log{L})$ if we use a fast matrix inversion. If the algorithm is implemented on quantum computer, its complexity becomes ${\cal O}(θ(\log{L})^3)$.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2209.02228
- https://arxiv.org/pdf/2209.02228
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294961424
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4294961424Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2209.02228Digital Object Identifier
- Title
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Compression Optimality of Asymmetric Numeral SystemsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-06Full publication date if available
- Authors
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Josef Pieprzyk, Jarek Duda, Marcin Pawłowski, Seyit Camtepe, Arash Mahboubi, Paweł MorawieckiList of authors in order
- Landing page
-
https://arxiv.org/abs/2209.02228Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2209.02228Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2209.02228Direct OA link when available
- Concepts
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Numeral system, Compression (physics), Computer science, Arithmetic, Mathematics, Materials science, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.structure, | 58 |
| abstract_inverted_index.Compression | 0 |
| abstract_inverted_index.O}(θL^3)$, | 244 |
| abstract_inverted_index.compression | 33, 65, 86, 110, 115, 120, 168, 181, 197, 211 |
| abstract_inverted_index.implemented | 272 |
| abstract_inverted_index.probability | 144, 187 |
| abstract_inverted_index.redundancy. | 71 |
| abstract_inverted_index.statistics. | 79 |
| abstract_inverted_index.applications | 19 |
| abstract_inverted_index.distribution | 145 |
| abstract_inverted_index.investigates | 119 |
| abstract_inverted_index.mathematical | 57 |
| abstract_inverted_index.Consequently, | 105 |
| abstract_inverted_index.probabilistic | 204 |
| abstract_inverted_index.approximations | 188 |
| abstract_inverted_index.probabilities. | 161 |
| abstract_inverted_index.O}(θL\log{L})$ | 260 |
| abstract_inverted_index.teleconferencing | 22 |
| abstract_inverted_index.O}(θ(\log{L})^3)$. | 280 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.11322277 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |