Context-adaptive Entropy Model for End-to-end Optimized Image Compression

Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack

Abstract:

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 contexts, distinguished based upon whether additional bit
allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an
enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index. The test code is publicly available at https://github.com/JooyoungLeeETRI/CA_Entropy_Model.

Keywords: image compression, deep learning, entropy model

TL;DR: Context-adaptive entropy model for use in end-to-end optimized image compression, which significantly improves compression performance

Code: [ JooyoungLeeETRI/CA_Entropy_Model](https://github.com/JooyoungLeeETRI/CA_Entropy_Model) + [ 1 community implementation](https://paperswithcode.com/paper/?openreview=HyxKIiAqYQ)