Efficient CU and PU Decision Based on Neural Network and Gray Level Co-Occurrence Matrix for Intra Prediction of Screen Content Coding Article Swipe
Chao Huang
,
Zongju Peng
,
Fen Chen
,
Qiuping Jiang
,
Gangyi Jiang
,
Qingqing Hu
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/access.2018.2866081
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/access.2018.2866081
Screen content coding (SCC) significantly improves the screen content compression efficiency over the high efficient video coding but at the cost of extremely huge computational complexity. The flexible quad-tree coding unit (CU) partitioning structure and the newly-introduced SCC intra modes are largely responsible for the high computational complexity. In order to meet the challenge of huge computational complexity of SCC, we propose an efficient CU and prediction unit (PU) decision based on neural network (NN) and gray level co-occurrence matrix (GLCM) for SCC intra prediction. The proposed efficient SCC intra prediction algorithm contains three stages, including NN-based CU classification model, efficient PU mode decision based on classification (EPMD), and efficient CU size decision based on GLCM and spatiotemporal correlations (ECSD). Consequently, the computational complexity of SCC intra prediction can be drastically reduced by replacing the brute-force search with EPMD and ECSD to decide the optimal combination of CU size and PU mode. In addition, an online updating method of weighted factor is introduced to cope with different characteristics of test sequences. In order to achieve a good tradeoff between complexity reduction and rate distortion (RD) performance, extensive experiments are conducted to select the optimal threshold for ECSD. The experimental results show that in comparison with the original SCC reference software, the proposed algorithm can reduce 49.33% intra coding time with 1.36% BDBR increase and 0.13 dB BDPSNR decrease. Meanwhile, the proposed algorithm outperforms eight state-of-the-art algorithms in terms of computational complexity reduction and RD performance.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2018.2866081
- OA Status
- gold
- Cited By
- 25
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2889495570
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2889495570Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2018.2866081Digital Object Identifier
- Title
-
Efficient CU and PU Decision Based on Neural Network and Gray Level Co-Occurrence Matrix for Intra Prediction of Screen Content CodingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
-
Chao Huang, Zongju Peng, Fen Chen, Qiuping Jiang, Gangyi Jiang, Qingqing HuList of authors in order
- Landing page
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https://doi.org/10.1109/access.2018.2866081Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2018.2866081Direct OA link when available
- Concepts
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Computational complexity theory, Computer science, Coding (social sciences), Algorithm, Artificial neural network, Reduction (mathematics), Algorithmic efficiency, Artificial intelligence, Data mining, Mathematics, Statistics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
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2025: 1, 2024: 2, 2022: 7, 2021: 7, 2020: 4Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 26, 85, 197 |
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| abstract_inverted_index.huge | 23, 55 |
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| abstract_inverted_index.ECSD. | 196 |
| abstract_inverted_index.based | 70, 104, 113 |
| abstract_inverted_index.eight | 233 |
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| abstract_inverted_index.(EPMD), | 107 |
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