  This paper proposes a transformer-based learned image compression system. It
is capable of achieving variable-rate compression with a single model while
supporting the region-of-interest (ROI) functionality. Inspired by prompt
tuning, we introduce prompt generation networks to condition the
transformer-based autoencoder of compression. Our prompt generation networks
generate content-adaptive tokens according to the input image, an ROI mask, and
a rate parameter. The separation of the ROI mask and the rate parameter allows
an intuitive way to achieve variable-rate and ROI coding simultaneously.
Extensive experiments validate the effectiveness of our proposed method and
confirm its superiority over the other competing methods.
