Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909225104441344 |
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| author | Shindo, Takahiro Tatsumi, Yui Watanabe, Taiju Watanabe, Hiroshi |
| author_facet | Shindo, Takahiro Tatsumi, Yui Watanabe, Taiju Watanabe, Hiroshi |
| contents | Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonly used in popular image compression schemes into scalable image coding method for humans and machines. In this paper, we propose a method to enhance the quality of decoded images for humans by integrating post-processing into scalable coding scheme. Experimental results show that the post-processing improves compression performance. Furthermore, the effectiveness of the proposed method is validated through comparisons with traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing Shindo, Takahiro Tatsumi, Yui Watanabe, Taiju Watanabe, Hiroshi Computer Vision and Pattern Recognition Image and Video Processing Scalable image coding for both humans and machines is a technique that has gained a lot of attention recently. This technology enables the hierarchical decoding of images for human vision and image recognition models. It is a highly effective method when images need to serve both purposes. However, no research has yet incorporated the post-processing commonly used in popular image compression schemes into scalable image coding method for humans and machines. In this paper, we propose a method to enhance the quality of decoded images for humans by integrating post-processing into scalable coding scheme. Experimental results show that the post-processing improves compression performance. Furthermore, the effectiveness of the proposed method is validated through comparisons with traditional methods. |
| title | Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2405.11894 |