Survey on video image reconstruction method based on generative model
Traditional video compression technology based on pixel correlation has limited performance improvement space, semantic compression has become the new direction of video compression coding, and video image reconstruction is the key link of semantic compression coding.First, the video image reconstru...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022178/ |
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| _version_ | 1841539965268262912 |
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| author | Yanwen WANG Weimin LEI Wei ZHANG Huan MENG Xinyi CHEN Wenhui YE Qingyang JING |
| author_facet | Yanwen WANG Weimin LEI Wei ZHANG Huan MENG Xinyi CHEN Wenhui YE Qingyang JING |
| author_sort | Yanwen WANG |
| collection | DOAJ |
| description | Traditional video compression technology based on pixel correlation has limited performance improvement space, semantic compression has become the new direction of video compression coding, and video image reconstruction is the key link of semantic compression coding.First, the video image reconstruction methods for traditional coding optimization were introduced, including how to use deep learning to improve prediction accuracy and enhance reconstruction quality with super-resolution techniques.Second, the video image reconstruction methods based on variational auto-encoders, generative adversarial networks, autoregressive models and transformer models were discussed emphatically.Then, the models were classified according to different semantic representations of images.The advantages, disadvantages, and applicable scenarios of various methods were compared.Finally, the existing problems of video image reconstruction were summarized, and the further research directions were prospected. |
| format | Article |
| id | doaj-art-3cc3c121ad1f4b7483c8119478053e37 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-3cc3c121ad1f4b7483c8119478053e372025-01-14T06:28:51ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-09-014319420859391913Survey on video image reconstruction method based on generative modelYanwen WANGWeimin LEIWei ZHANGHuan MENGXinyi CHENWenhui YEQingyang JINGTraditional video compression technology based on pixel correlation has limited performance improvement space, semantic compression has become the new direction of video compression coding, and video image reconstruction is the key link of semantic compression coding.First, the video image reconstruction methods for traditional coding optimization were introduced, including how to use deep learning to improve prediction accuracy and enhance reconstruction quality with super-resolution techniques.Second, the video image reconstruction methods based on variational auto-encoders, generative adversarial networks, autoregressive models and transformer models were discussed emphatically.Then, the models were classified according to different semantic representations of images.The advantages, disadvantages, and applicable scenarios of various methods were compared.Finally, the existing problems of video image reconstruction were summarized, and the further research directions were prospected.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022178/video compression codingimage reconstructiongenerative adversarial networkvariational auto-encoderTransformer model |
| spellingShingle | Yanwen WANG Weimin LEI Wei ZHANG Huan MENG Xinyi CHEN Wenhui YE Qingyang JING Survey on video image reconstruction method based on generative model Tongxin xuebao video compression coding image reconstruction generative adversarial network variational auto-encoder Transformer model |
| title | Survey on video image reconstruction method based on generative model |
| title_full | Survey on video image reconstruction method based on generative model |
| title_fullStr | Survey on video image reconstruction method based on generative model |
| title_full_unstemmed | Survey on video image reconstruction method based on generative model |
| title_short | Survey on video image reconstruction method based on generative model |
| title_sort | survey on video image reconstruction method based on generative model |
| topic | video compression coding image reconstruction generative adversarial network variational auto-encoder Transformer model |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022178/ |
| work_keys_str_mv | AT yanwenwang surveyonvideoimagereconstructionmethodbasedongenerativemodel AT weiminlei surveyonvideoimagereconstructionmethodbasedongenerativemodel AT weizhang surveyonvideoimagereconstructionmethodbasedongenerativemodel AT huanmeng surveyonvideoimagereconstructionmethodbasedongenerativemodel AT xinyichen surveyonvideoimagereconstructionmethodbasedongenerativemodel AT wenhuiye surveyonvideoimagereconstructionmethodbasedongenerativemodel AT qingyangjing surveyonvideoimagereconstructionmethodbasedongenerativemodel |