Yui Tatsumi, Ziyue Zeng, Hiroshi Watanabe
The paper introduces two methods for scalable image compression, enhancing efficiency and flexibility for both human and machine vision, achieving significant performance improvements over previous models.
This research focuses on improving how images are compressed so they can be used effectively by both people and machines, like image recognition software. The authors propose two new methods that make image compression more efficient and adaptable to different needs. These methods allow choosing between how complex the compression process is and how well it performs, which can be useful for various applications. The study shows that one of the methods, PR-ICMH, significantly outperforms previous techniques in terms of compression efficiency.
