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.
@article{arxiv.2405.11894,
title = {Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing},
author = {Takahiro Shindo and Yui Tatsumi and Taiju Watanabe and Hiroshi Watanabe},
journal= {arXiv preprint arXiv:2405.11894},
year = {2024}
}