Image compression is a widely used technique to reduce the spatial redundancy
in images. Recently, learning based image compression has achieved significant
progress by using the powerful representation ability from neural networks.
However, the current state-of-the-art learning based image compression methods
suffer from the huge computational cost, which limits their capacity for
practical applications. In this paper, we propose a unified framework called
Efficient Deep Image Compression (EDIC) based on three new technologies,
including a channel attention module, a Gaussian mixture model and a
decoder-side enhancement module. Specifically, we design an auto-encoder style
network for learning based image compression. To improve the coding efficiency,
we exploit the channel relationship between latent representations by using the
channel attention module. Besides, the Gaussian mixture model is introduced for
the entropy model and improves the accuracy for bitrate estimation.
Furthermore, we introduce the decoder-side enhancement module to further
improve image compression performance. Our EDIC method can also be readily
incorporated with the Deep Video Compression (DVC) framework to further improve
the video compression performance. Simultaneously, our EDIC method boosts the
coding performance significantly while bringing slightly increased
computational cost. More importantly, experimental results demonstrate that the
proposed approach outperforms the current state-of-the-art image compression
methods and is up to more than 150 times faster in terms of decoding speed when
compared with Minnen's method. The proposed framework also successfully
improves the performance of the recent deep video compression system DVC. Our
code will be released at this https URL

该研究提出了一种基于三种新技术的统一框架，包括通道关注模块、高斯混合模型和解码器侧增强模块，命名为 EDIC，可用于基于学习的图像压缩，并说明该方法优于当前所有先进的图像压缩方法，同时将解码速度提高了超过 150 倍。