Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks
Cross-component linear model (CCLM) prediction in H.266/versatile video coding (VVC) can improve the compression efficiency.There exists high correlation between luma and chroma components while the correlation is difficult to be modeled explicitly.An algorithm for neural network based cross-compone...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022031/ |
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| _version_ | 1841539989941256192 |
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| author | Junyan HUO Danni WANG Yanzhuo MA Shuai WAN Fuzheng YANG |
| author_facet | Junyan HUO Danni WANG Yanzhuo MA Shuai WAN Fuzheng YANG |
| author_sort | Junyan HUO |
| collection | DOAJ |
| description | Cross-component linear model (CCLM) prediction in H.266/versatile video coding (VVC) can improve the compression efficiency.There exists high correlation between luma and chroma components while the correlation is difficult to be modeled explicitly.An algorithm for neural network based cross-component prediction (NNCCP) was proposed where reference pixels with high correlation were selected according to the luma difference between the reference pixels and the pixel to be predicted.Based on the high-correlated reference pixels and the luma difference, the predicted chroma was obtained based on lightweight fully connected networks.Experimental results demonstrate that the proposed algorithm can achieve 0.27%, 1.54%, and 1.84% bitrate savings for luma and chroma components, compared with the VVC test model 10.0 (VTM10.0).Besides, a unified network can be employed to blocks with different sizes and different quantization parameters. |
| format | Article |
| id | doaj-art-b7ed30e48f454602bb478498ca51cb50 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b7ed30e48f454602bb478498ca51cb502025-01-14T06:29:33ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-02-014314315559394463Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networksJunyan HUODanni WANGYanzhuo MAShuai WANFuzheng YANGCross-component linear model (CCLM) prediction in H.266/versatile video coding (VVC) can improve the compression efficiency.There exists high correlation between luma and chroma components while the correlation is difficult to be modeled explicitly.An algorithm for neural network based cross-component prediction (NNCCP) was proposed where reference pixels with high correlation were selected according to the luma difference between the reference pixels and the pixel to be predicted.Based on the high-correlated reference pixels and the luma difference, the predicted chroma was obtained based on lightweight fully connected networks.Experimental results demonstrate that the proposed algorithm can achieve 0.27%, 1.54%, and 1.84% bitrate savings for luma and chroma components, compared with the VVC test model 10.0 (VTM10.0).Besides, a unified network can be employed to blocks with different sizes and different quantization parameters.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022031/H.266/VVCchroma intra predictioncross component predictionneural network |
| spellingShingle | Junyan HUO Danni WANG Yanzhuo MA Shuai WAN Fuzheng YANG Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks Tongxin xuebao H.266/VVC chroma intra prediction cross component prediction neural network |
| title | Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks |
| title_full | Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks |
| title_fullStr | Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks |
| title_full_unstemmed | Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks |
| title_short | Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks |
| title_sort | efficient cross component prediction for h 266 vvc based on lightweight fully connected networks |
| topic | H.266/VVC chroma intra prediction cross component prediction neural network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022031/ |
| work_keys_str_mv | AT junyanhuo efficientcrosscomponentpredictionforh266vvcbasedonlightweightfullyconnectednetworks AT danniwang efficientcrosscomponentpredictionforh266vvcbasedonlightweightfullyconnectednetworks AT yanzhuoma efficientcrosscomponentpredictionforh266vvcbasedonlightweightfullyconnectednetworks AT shuaiwan efficientcrosscomponentpredictionforh266vvcbasedonlightweightfullyconnectednetworks AT fuzhengyang efficientcrosscomponentpredictionforh266vvcbasedonlightweightfullyconnectednetworks |