We propose an end-to-end trainable image compression framework with a multi-scale and context-adaptive entropy model, especially for low bitrate compression. Due to the success of autoregressive priors in probabilistic generative model, the complementary combination of autoregressive and hierarchical priors can estimate the distribution of each latent representation accurately. Based on this combination, we firstly propose a multi-scale masked convolutional network as our autoregressive model. Secondly, for the significant computational penalty of generative model, we focus on decoded representations covered by receptive field, and skip full zero latents in arithmetic codec. At last, according to the low-rate compression's constraint in CLIC-2019, we use a method to maximize MS-SSIM by allocating bitrate for each image.
@article{arxiv.1910.07844,
title = {Multi-scale and Context-adaptive Entropy Model for Image Compression},
author = {Jing Zhou and Sihan Wen and Akira Nakagawa and Kimihiko Kazui and Zhiming Tan},
journal= {arXiv preprint arXiv:1910.07844},
year = {2019}
}
Comments
accepted by CVPR workshop and Challenge on Learned Image Compression (CLIC) 2019