This paper presents a Transformer-based image compression system that allows
for a variable image quality objective according to the user's preference.
Optimizing a learned codec for different quality objectives leads to
reconstructed images with varying visual characteristics. Our method provides
the user with the flexibility to choose a trade-off between two image quality
objectives using a single, shared model. Motivated by the success of
prompt-tuning techniques, we introduce prompt tokens to condition our
Transformer-based autoencoder. These prompt tokens are generated adaptively
based on the user's preference and input image through learning a prompt
generation network. Extensive experiments on commonly used quality metrics
demonstrate the effectiveness of our method in adapting the encoding and/or
decoding processes to a variable quality objective. While offering the
additional flexibility, our proposed method performs comparably to the
single-objective methods in terms of rate-distortion performance.

这篇论文介绍了一种基于 Transformer 的图像压缩系统，根据用户的偏好提供可变的图像质量目标，通过引入提示符令牌，使得自适应生成，学习和适应编码和 / 或解码过程的方法，在可变的质量目标下提高了压缩效果。