20 citations · 46 across the 13 of their papers we have counts for
5 papers · 1 filter
Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee
Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines and progressive coding schemes.…
SLIM: Semantic-based Low-bitrate Image compression for Machines
Hyeonjin Lee, Jun-Hyuk Kim, Jong-Seok Lee
In recent years, the demand of image compression models for machine vision has increased dramatically. However, the training frameworks of image compression still focus on the visi…
Joint Global and Local Hierarchical Priors for Learned Image Compression
Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee
Recently, learned image compression methods have outperformed traditional hand-crafted ones including BPG. One of the keys to this success is learned entropy models that estimate t…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
SRZoo: An integrated repository for super-resolution using deep learning
Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee
Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However,…