Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing

• URL: http://arxiv.org/abs/2405.11894v2
• Date: Mon, 17 Jun 2024 02:23:01 GMT
• Title: Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing
• Authors: Takahiro Shindo, Yui Tatsumi, Taiju Watanabe, Hiroshi Watanabe,
• Abstract summary: 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. The effectiveness of the proposed method is validated through comparisons with traditional methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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