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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Bibliographic Details
Published in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1 - 6
Main Authors: Wang, Jun, Yao, Shengshi, Wang, Sixian, Si, Zhongwei, Wang, Fengyu, Liu, Zhenyu, Dai, Jincheng
Format: Conference Proceeding
Language:English
Published: IEEE 24.03.2025
Subjects:
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
ISSN:1558-2612
Online Access:Get full text
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Summary: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.
ISSN:1558-2612
DOI:10.1109/WCNC61545.2025.10978538