Rate adaption of deep image compression in a single model will become one of the decisive factors competing with the classical image compression codecs. However, until now, there is no perfect solution that neither increases the computation nor affects the compression performance. In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model. Unlike the previous solutions that encode progressively or change the internal unit of the network, G-VAE only adds a pair of gain units at the output of encoder and the input of decoder. It is so concise that G-VAE could be applied to almost all the image compression methods and achieve continuously variable rate with negligible additional parameters and computation. We also propose a new deep image compression framework, which outperforms all the published results on Kodak datasets in PSNR and MS-SSIM metrics. Experimental results show that adding a pair of gain units will not affect the performance of the basic models while endowing them with continuously variable rate.

本研究提出了一种基于变分自编码器的不对称增益可变率可学习图像压缩框架（AG-VAE），该框架利用一对增益单元实现了一种连续可变率适应性方法，通过指数插值实现连续率自适应，同时利用不对称高斯熵模型对熵进行更精确的估计。该方法表现优秀，与最先进的学习图像压缩方法相媲美，同时胜过了经典图像编解码器，实验证明增益模块和不对称高斯熵模型的优越性。

具有连续速率调整的非对称增益深度图像压缩