Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing
This work addresses the need for better image quality in scalable coding systems used for applications requiring both human viewing and machine recognition, though it appears incremental by adding existing post-processing techniques.
The paper tackles the problem of enhancing decoded image quality for human vision in scalable image coding for both humans and machines by integrating post-processing, resulting in improved compression performance as validated by comparisons with traditional methods.
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.