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.
@article{arxiv.1809.10452,
title = {Context-adaptive Entropy Model for End-to-end Optimized Image Compression},
author = {Jooyoung Lee and Seunghyun Cho and Seung-Kwon Beack},
journal= {arXiv preprint arXiv:1809.10452},
year = {2019}
}
Comments
Published as a conference paper at ICLR 2019. The test code, evaluation results and reconstructed images are publicly available at https://github.com/JooyoungLeeETRI/CA_Entropy_Model