Double bit range estimation with eight estimators for CABAC in VVC Article Swipe
This work describes the modification of Context‐based Adaptive Binary Arithmetic Coding (CABAC) using the double bit range estimation in the VVC engine and the consideration of range updates by using eight hypothetical probability estimators. The focus is on the selected adaptation rates performed in these proposed estimators, which are chosen based on memory consideration and coding efficiency. An investigation of arithmetic coding engines with multi‐hypothesis probability estimates and their consideration of contextual modeling of entropy coding at the level of transform coefficients. The proposed scheme enables a quantitative representation of probabilistic predictions linearly and describes the scalability potential for higher accuracy. In addition, this work discusses the hardware implementation, which is based on simple operations such as bitwise operations and subinterval updates. The experimental results validate the effectiveness of the proposed approach specified in VTM framework. The improved results show that it provides more significant gains in terms of RA and LD, which is better than the AI configuration.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/ipr2.12547
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12547
- OA Status
- gold
- Cited By
- 3
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282826806
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4282826806Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/ipr2.12547Digital Object Identifier
- Title
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Double bit range estimation with eight estimators for CABAC in VVCWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-14Full publication date if available
- Authors
-
Ka‐Hou Chan, Sio‐Kei ImList of authors in order
- Landing page
-
https://doi.org/10.1049/ipr2.12547Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12547Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12547Direct OA link when available
- Concepts
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Estimator, Computer science, Range (aeronautics), Estimation, Bit (key), Arithmetic, Algorithm, Statistics, Mathematics, Computer security, Engineering, Aerospace engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.modeling | 73 |
| abstract_inverted_index.proposed | 46, 84, 131 |
| abstract_inverted_index.provides | 143 |
| abstract_inverted_index.selected | 40 |
| abstract_inverted_index.updates. | 122 |
| abstract_inverted_index.validate | 126 |
| abstract_inverted_index.accuracy. | 101 |
| abstract_inverted_index.addition, | 103 |
| abstract_inverted_index.describes | 3, 95 |
| abstract_inverted_index.discusses | 106 |
| abstract_inverted_index.estimates | 67 |
| abstract_inverted_index.performed | 43 |
| abstract_inverted_index.potential | 98 |
| abstract_inverted_index.specified | 133 |
| abstract_inverted_index.transform | 81 |
| abstract_inverted_index.Arithmetic | 10 |
| abstract_inverted_index.adaptation | 41 |
| abstract_inverted_index.arithmetic | 61 |
| abstract_inverted_index.contextual | 72 |
| abstract_inverted_index.estimation | 18 |
| abstract_inverted_index.framework. | 136 |
| abstract_inverted_index.operations | 115, 119 |
| abstract_inverted_index.efficiency. | 57 |
| abstract_inverted_index.estimators, | 47 |
| abstract_inverted_index.estimators. | 34 |
| abstract_inverted_index.predictions | 92 |
| abstract_inverted_index.probability | 33, 66 |
| abstract_inverted_index.scalability | 97 |
| abstract_inverted_index.significant | 145 |
| abstract_inverted_index.subinterval | 121 |
| abstract_inverted_index.experimental | 124 |
| abstract_inverted_index.hypothetical | 32 |
| abstract_inverted_index.modification | 5 |
| abstract_inverted_index.quantitative | 88 |
| abstract_inverted_index.coefficients. | 82 |
| abstract_inverted_index.consideration | 25, 54, 70 |
| abstract_inverted_index.effectiveness | 128 |
| abstract_inverted_index.investigation | 59 |
| abstract_inverted_index.probabilistic | 91 |
| abstract_inverted_index.configuration. | 159 |
| abstract_inverted_index.representation | 89 |
| abstract_inverted_index.Context‐based | 7 |
| abstract_inverted_index.implementation, | 109 |
| abstract_inverted_index.multi‐hypothesis | 65 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5061396090 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I49835588 |
| citation_normalized_percentile.value | 0.59780237 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |