273 citations · 360 across the 4 of their papers we have counts for
4 papers
End-to-End Learnable Multi-Scale Feature Compression for VCM
Yeongwoong Kim, Hyewon Jeong, Janghyun Yu +4
The proliferation of deep learning-based machine vision applications has given rise to a new type of compression, so called video coding for machine (VCM). VCM differs from traditi…
Selective compression learning of latent representations for variable-rate image compression
Jooyoung Lee, Seyoon Jeong, Munchurl Kim
Recently, many neural network-based image compression methods have shown promising results superior to the existing tool-based conventional codecs. However, most of them are often…
An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization
Jooyoung Lee, Seunghyun Cho, Munchurl Kim
Recently, learned image compression methods have been actively studied. Among them, entropy-minimization based approaches have achieved superior results compared to conventional im…
Context-adaptive Entropy Model for End-to-end Optimized Image Compression
Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack
We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free context…