Generative Visual Compression: A Review
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909094805241856 |
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| author | Chen, Bolin Yin, Shanzhi Chen, Peilin Wang, Shiqi Ye, Yan |
| author_facet | Chen, Bolin Yin, Shanzhi Chen, Peilin Wang, Shiqi Ye, Yan |
| contents | Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs. This paper provides a thorough review on the recent advances of generative visual compression, illustrating great potentials and promising applications in ultra-low bitrate communication, user-specified reconstruction/filtering, and intelligent machine analysis. In particular, we review the visual data compression methodologies with deep generative models, and summarize how compact representation and high-fidelity reconstruction could be actualized via generative techniques. In addition, we generalize related generative compression technologies for machine vision and intelligent analytics. Finally, we discuss the fundamental challenges on generative visual compression techniques and envision their future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Visual Compression: A Review Chen, Bolin Yin, Shanzhi Chen, Peilin Wang, Shiqi Ye, Yan Computer Vision and Pattern Recognition Image and Video Processing Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs. This paper provides a thorough review on the recent advances of generative visual compression, illustrating great potentials and promising applications in ultra-low bitrate communication, user-specified reconstruction/filtering, and intelligent machine analysis. In particular, we review the visual data compression methodologies with deep generative models, and summarize how compact representation and high-fidelity reconstruction could be actualized via generative techniques. In addition, we generalize related generative compression technologies for machine vision and intelligent analytics. Finally, we discuss the fundamental challenges on generative visual compression techniques and envision their future research directions. |
| title | Generative Visual Compression: A Review |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2402.02140 |