Transformer-based Variable-rate Image Compression with Region-of-interest Control

• URL: http://arxiv.org/abs/2305.10807v3
• Date: Tue, 1 Aug 2023 10:12:42 GMT
• Title: Transformer-based Variable-rate Image Compression with Region-of-interest Control
• Authors: Chia-Hao Kao, Ying-Chieh Weng, Yi-Hsin Chen, Wei-Chen Chiu, Wen-Hsiao Peng
• Abstract summary: 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 functionality.
• Score: 24.794581811606445
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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