TinyLIC-High efficiency lossy image compression method
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916129720500224 |
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| author | Ma, Gaocheng Chai, Yinfeng Jiang, Tianhao Lu, Ming Chen, Tong |
| author_facet | Ma, Gaocheng Chai, Yinfeng Jiang, Tianhao Lu, Ming Chen, Tong |
| contents | Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led to the emergence of learned image compression methods that offer significant improvements in coding efficiency compared to traditional codecs. These learned compression techniques have shown noticeable gains and even outperformed traditional schemes |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11164 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TinyLIC-High efficiency lossy image compression method Ma, Gaocheng Chai, Yinfeng Jiang, Tianhao Lu, Ming Chen, Tong Image and Video Processing Image compression has been the subject of extensive research for several decades, resulting in the development of well-known standards such as JPEG, JPEG2000, and H.264/AVC. However, recent advancements in deep learning have led to the emergence of learned image compression methods that offer significant improvements in coding efficiency compared to traditional codecs. These learned compression techniques have shown noticeable gains and even outperformed traditional schemes |
| title | TinyLIC-High efficiency lossy image compression method |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2402.11164 |