Learning-based methods have effectively promoted the community of image
compression. Meanwhile, variational autoencoder (VAE) based variable-rate
approaches have recently gained much attention to avoid the usage of a set of
different networks for various compression rates. Despite the remarkable
performance that has been achieved, these approaches would be readily corrupted
once multiple compression/decompression operations are executed, resulting in
the fact that image quality would be tremendously dropped and strong artifacts
would appear. Thus, we try to tackle the issue of high-fidelity fine
variable-rate image compression and propose the Invertible Activation
Transformation (IAT) module. We implement the IAT in a mathematical invertible
manner on a single rate Invertible Neural Network (INN) based model and the
quality level (QLevel) would be fed into the IAT to generate scaling and bias
tensors. IAT and QLevel together give the image compression model the ability
of fine variable-rate control while better maintaining the image fidelity.
Extensive experiments demonstrate that the single rate image compression model
equipped with our IAT module has the ability to achieve variable-rate control
without any compromise. And our IAT-embedded model obtains comparable
rate-distortion performance with recent learning-based image compression
methods. Furthermore, our method outperforms the state-of-the-art variable-rate
image compression method by a large margin, especially after multiple
re-encodings.

本文通过引入 Invertible Activation Transformation (IAT) 模块来解决图片在多次压缩／解压缩操作下质量下降的问题，实现了更好的维护图片保真度的同时，拥有细粒度的压缩率控制并取得了和目前最好的类似学习方法比较时具有可比较的的速率失真性能。