Bayesian-theory-based Fast CU Size and Mode Decision Algorithm for 3D-HEVC Depth Video Inter-coding Article Swipe
Fen Chen
,
Sheng Liu
,
Zongju Peng
,
Qingqing Hu
,
Gangyi Jiang
,
Mei Yu
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3837/tiis.2018.04.018
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3837/tiis.2018.04.018
Multi-view video plus depth (MVD) is a mainstream format of 3D scene representation in free viewpoint video systems.The advanced 3D extension of the high efficiency video coding (3D-HEVC) standard introduces new prediction tools to improve the coding performance of depth video.However, the depth video in 3D-HEVC is time consuming.To reduce the complexity of the depth video inter coding, we propose a fast coding unit (CU) size and mode decision algorithm.First, an off-line trained Bayesian model is built which the feature vector contains the depth levels of the corresponding spatial, temporal, and inter-component (texture-depth) neighboring largest CUs (LCUs).Then, the model is used to predict the depth level of the current LCU, and terminate the CU recursive splitting process.Finally, the CU mode search process is early terminated by making use of the mode correlation of spatial, inter-component (texture-depth), and inter-view neighboring CUs.Compared to the 3D-HEVC reference software HTM-10.0, the proposed algorithm reduces the encoding time of depth video and the total encoding time by 65.03% and 41.04% on average, respectively, with negligible quality degradation of the synthesized virtual view.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3837/tiis.2018.04.018
- http://itiis.org/digital-library/manuscript/file/21737/TIISVol12No4-18.pdf
- OA Status
- bronze
- Cited By
- 3
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2799719159
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2799719159Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3837/tiis.2018.04.018Digital Object Identifier
- Title
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Bayesian-theory-based Fast CU Size and Mode Decision Algorithm for 3D-HEVC Depth Video Inter-codingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-04-30Full publication date if available
- Authors
-
Fen Chen, Sheng Liu, Zongju Peng, Qingqing Hu, Gangyi Jiang, Mei YuList of authors in order
- Landing page
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https://doi.org/10.3837/tiis.2018.04.018Publisher landing page
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https://itiis.org/digital-library/manuscript/file/21737/TIISVol12No4-18.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://itiis.org/digital-library/manuscript/file/21737/TIISVol12No4-18.pdfDirect OA link when available
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Computer science, Coding (social sciences), Algorithm, Bayesian probability, Mode (computer interface), Artificial intelligence, Statistics, Human–computer interaction, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2022: 2, 2021: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 9, 21, 38, 52, 85, 106, 128, 132, 153, 172 |
| abstract_inverted_index.on | 165 |
| abstract_inverted_index.to | 33, 101, 140 |
| abstract_inverted_index.we | 58 |
| abstract_inverted_index.CUs | 95 |
| abstract_inverted_index.and | 66, 90, 110, 136, 156, 163 |
| abstract_inverted_index.new | 30 |
| abstract_inverted_index.the | 22, 35, 41, 50, 53, 78, 82, 86, 97, 103, 107, 112, 117, 129, 141, 146, 150, 157, 173 |
| abstract_inverted_index.use | 127 |
| abstract_inverted_index.(CU) | 64 |
| abstract_inverted_index.LCU, | 109 |
| abstract_inverted_index.fast | 61 |
| abstract_inverted_index.free | 14 |
| abstract_inverted_index.high | 23 |
| abstract_inverted_index.mode | 67, 119, 130 |
| abstract_inverted_index.plus | 2 |
| abstract_inverted_index.size | 65 |
| abstract_inverted_index.time | 47, 152, 160 |
| abstract_inverted_index.unit | 63 |
| abstract_inverted_index.used | 100 |
| abstract_inverted_index.with | 168 |
| abstract_inverted_index.(MVD) | 4 |
| abstract_inverted_index.built | 76 |
| abstract_inverted_index.depth | 3, 39, 42, 54, 83, 104, 154 |
| abstract_inverted_index.early | 123 |
| abstract_inverted_index.inter | 56 |
| abstract_inverted_index.level | 105 |
| abstract_inverted_index.model | 74, 98 |
| abstract_inverted_index.scene | 11 |
| abstract_inverted_index.tools | 32 |
| abstract_inverted_index.total | 158 |
| abstract_inverted_index.video | 1, 16, 25, 43, 55, 155 |
| abstract_inverted_index.view. | 176 |
| abstract_inverted_index.which | 77 |
| abstract_inverted_index.41.04% | 164 |
| abstract_inverted_index.65.03% | 162 |
| abstract_inverted_index.coding | 26, 36, 62 |
| abstract_inverted_index.format | 8 |
| abstract_inverted_index.levels | 84 |
| abstract_inverted_index.making | 126 |
| abstract_inverted_index.reduce | 49 |
| abstract_inverted_index.search | 120 |
| abstract_inverted_index.vector | 80 |
| abstract_inverted_index.3D-HEVC | 45, 142 |
| abstract_inverted_index.coding, | 57 |
| abstract_inverted_index.current | 108 |
| abstract_inverted_index.feature | 79 |
| abstract_inverted_index.improve | 34 |
| abstract_inverted_index.largest | 94 |
| abstract_inverted_index.predict | 102 |
| abstract_inverted_index.process | 121 |
| abstract_inverted_index.propose | 59 |
| abstract_inverted_index.quality | 170 |
| abstract_inverted_index.reduces | 149 |
| abstract_inverted_index.trained | 72 |
| abstract_inverted_index.virtual | 175 |
| abstract_inverted_index.Bayesian | 73 |
| abstract_inverted_index.advanced | 18 |
| abstract_inverted_index.average, | 166 |
| abstract_inverted_index.contains | 81 |
| abstract_inverted_index.decision | 68 |
| abstract_inverted_index.encoding | 151, 159 |
| abstract_inverted_index.off-line | 71 |
| abstract_inverted_index.proposed | 147 |
| abstract_inverted_index.software | 144 |
| abstract_inverted_index.spatial, | 88, 133 |
| abstract_inverted_index.standard | 28 |
| abstract_inverted_index.(3D-HEVC) | 27 |
| abstract_inverted_index.HTM-10.0, | 145 |
| abstract_inverted_index.algorithm | 148 |
| abstract_inverted_index.extension | 20 |
| abstract_inverted_index.recursive | 114 |
| abstract_inverted_index.reference | 143 |
| abstract_inverted_index.splitting | 115 |
| abstract_inverted_index.temporal, | 89 |
| abstract_inverted_index.terminate | 111 |
| abstract_inverted_index.viewpoint | 15 |
| abstract_inverted_index.Multi-view | 0 |
| abstract_inverted_index.complexity | 51 |
| abstract_inverted_index.efficiency | 24 |
| abstract_inverted_index.inter-view | 137 |
| abstract_inverted_index.introduces | 29 |
| abstract_inverted_index.mainstream | 7 |
| abstract_inverted_index.negligible | 169 |
| abstract_inverted_index.prediction | 31 |
| abstract_inverted_index.terminated | 124 |
| abstract_inverted_index.correlation | 131 |
| abstract_inverted_index.degradation | 171 |
| abstract_inverted_index.neighboring | 93, 138 |
| abstract_inverted_index.performance | 37 |
| abstract_inverted_index.synthesized | 174 |
| abstract_inverted_index.systems.The | 17 |
| abstract_inverted_index.(LCUs).Then, | 96 |
| abstract_inverted_index.CUs.Compared | 139 |
| abstract_inverted_index.consuming.To | 48 |
| abstract_inverted_index.corresponding | 87 |
| abstract_inverted_index.respectively, | 167 |
| abstract_inverted_index.representation | 12 |
| abstract_inverted_index.video.However, | 40 |
| abstract_inverted_index.(texture-depth) | 92 |
| abstract_inverted_index.inter-component | 91, 134 |
| abstract_inverted_index.(texture-depth), | 135 |
| abstract_inverted_index.algorithm.First, | 69 |
| abstract_inverted_index.process.Finally, | 116 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5043258671 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I109935558 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.42437751 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |