Task-Scalable Image Semantic Communication via Conditional Affine Transforms and Pixel-Wise Quality Control
Deep autoencoder-based joint source-channel coding (JSCC) has gained significant attention for end-to-end image semantic communication systems. However, existing methods typically optimize a uniform bandwidth-distortion trade-off over the entire image, potentially leading to the loss of crucial deta...
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| Vydáno v: | IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1 - 6 |
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24.03.2025
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| ISSN: | 1558-2612 |
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| Abstract | Deep autoencoder-based joint source-channel coding (JSCC) has gained significant attention for end-to-end image semantic communication systems. However, existing methods typically optimize a uniform bandwidth-distortion trade-off over the entire image, potentially leading to the loss of crucial details and inconsistent content for tasks with diverse regions of interest. In this paper, we propose a flexible fine-grained bandwidth allocation method for deep JSCC that enables highly efficient, task-scalable image transmission across various semantic communication scenarios using a single codec. Our method optimizes the bandwidth-distortion trade-off by constraining image distortion through a 2D pixel-wise quality map. Guided by the pixel-wise quality map, we introduce a novel conditional affine transformation that generates dedicated semantic feature maps tailored to specific tasks. Additionally, we introduce a semantic guidance network to automatically generate task-aware quality maps via backpropagation without additional retraining. This approach leverages a pretrained variable-length neural JSCC codec and adjusts the transmission quality on a fine-grained level, eliminating the need to train separate models for different tasks. Experimental results demonstrate the effectiveness of our bandwidth allocation method, enhancing task-specific performance in various goal-oriented image communication scenarios without additional training. |
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| AbstractList | Deep autoencoder-based joint source-channel coding (JSCC) has gained significant attention for end-to-end image semantic communication systems. However, existing methods typically optimize a uniform bandwidth-distortion trade-off over the entire image, potentially leading to the loss of crucial details and inconsistent content for tasks with diverse regions of interest. In this paper, we propose a flexible fine-grained bandwidth allocation method for deep JSCC that enables highly efficient, task-scalable image transmission across various semantic communication scenarios using a single codec. Our method optimizes the bandwidth-distortion trade-off by constraining image distortion through a 2D pixel-wise quality map. Guided by the pixel-wise quality map, we introduce a novel conditional affine transformation that generates dedicated semantic feature maps tailored to specific tasks. Additionally, we introduce a semantic guidance network to automatically generate task-aware quality maps via backpropagation without additional retraining. This approach leverages a pretrained variable-length neural JSCC codec and adjusts the transmission quality on a fine-grained level, eliminating the need to train separate models for different tasks. Experimental results demonstrate the effectiveness of our bandwidth allocation method, enhancing task-specific performance in various goal-oriented image communication scenarios without additional training. |
| Author | Wang, Sixian Yao, Shengshi Wang, Fengyu Si, Zhongwei Wang, Jun Liu, Zhenyu Dai, Jincheng |
| Author_xml | – sequence: 1 givenname: Jun surname: Wang fullname: Wang, Jun organization: Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China – sequence: 2 givenname: Shengshi surname: Yao fullname: Yao, Shengshi organization: Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China – sequence: 3 givenname: Sixian surname: Wang fullname: Wang, Sixian organization: Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China – sequence: 4 givenname: Zhongwei surname: Si fullname: Si, Zhongwei email: sizhongwei@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China – sequence: 5 givenname: Fengyu surname: Wang fullname: Wang, Fengyu organization: Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China – sequence: 6 givenname: Zhenyu surname: Liu fullname: Liu, Zhenyu organization: Institute for Communication Systems, University of Surrey,United Kingdom – sequence: 7 givenname: Jincheng surname: Dai fullname: Dai, Jincheng email: daijincheng@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China |
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| Snippet | Deep autoencoder-based joint source-channel coding (JSCC) has gained significant attention for end-to-end image semantic communication systems. However,... |
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| SubjectTerms | Backpropagation Channel allocation Codecs Image coding Image communication Image quality Image semantic communications joint source and channel coding Quality control Semantic communication task-scalable Training Transforms |
| Title | Task-Scalable Image Semantic Communication via Conditional Affine Transforms and Pixel-Wise Quality Control |
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