Transformer-Based Variable-Rate Image Compression with Region-of-Interest Control

Chia-Hao Kao, Ying-Chieh Weng, Yi-Hsin Chen, Wei-Chen Chiu, Wen-Hsiao Peng

Abstract: 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.