作者
Yueqi Xie, Ka Leong Cheng, Qifeng Chen
发表日期
2021/8/8
研讨会论文
ACM International Conference on Multimedia (ACM MM)
简介
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 …
引用总数
学术搜索中的文章
Y Xie, KL Cheng, Q Chen - Proceedings of the 29th ACM international conference …, 2021