Although deep learning based image compression methods have achieved
promising progress these days, the performance of these methods still cannot
match the latest compression standard Versatile Video Coding (VVC). Most of the
recent developments focus on designing a more accurate and flexible entropy
model that can better parameterize the distributions of the latent features.
However, few efforts are devoted to structuring a better transformation between
the image space and the latent feature space. In this paper, instead of
employing previous autoencoder style networks to build this transformation, we
propose an enhanced Invertible Encoding Network with invertible neural networks
(INNs) to largely mitigate the information loss problem for better compression.
Experimental results on the Kodak, CLIC, and Tecnick datasets show that our
method outperforms the existing learned image compression methods and
compression standards, including VVC (VTM 12.1), especially for high-resolution
images. Our source code is available at this https URL

本文提出了一种基于可逆神经网络（INNs）的 Invertible Encoding Network 方法来构建更好的图像空间和潜在特征空间之间的转换，并在 Kodak、CLIC 和 Tecnick 数据集上证明了该方法在高分辨率图像方面优于现有的学习图像压缩方法和压缩标准（包括 VVC (VTM 12.1)）的实验结果。