Diffusion-Aided Joint Source Channel Coding for High Realism Wireless Image Transmission

作者：Mingyu Yang, Bowen Liu, Boyang Wang, Hun-Seok Kim · 发表于：IEEE Transactions on Machine Learning in Communications and Networking · 年份：2025 · DOI：10.1109/tmlcn.2025.3628535 · 被引用次数：19 · 研究领域：Advanced Data Compression Techniques、Advanced Image Processing Techniques、Wireless Signal Modulation Classification

Deep learning-based joint source-channel coding (deep JSCC) has been demonstrated to be an effective approach for wireless image transmission. However, many current approaches utilize an autoencoder framework to optimize conventional metrics such as Mean Squared Error (MSE) and Structural Similarity Index (SSIM), which are inadequate for preserving the perceptual quality of reconstructed images. Such an issue is more prominent under stringent bandwidth constraints or low signal-to-noise ratio (SNR) conditions. To tackle this challenge, we propose DiffJSCC, a novel framework that leverages the prior knowledge of the pre-trained Stable Diffusion model to produce high-realism images via the conditional diffusion denoising process. First, our DiffJSCC employs an autoencoder structure similar to prior deep JSCC works to generate an initial image reconstruction from the noisy channel symbols. This preliminary reconstruction serves as an intermediate step where robust multimodal spatial and textual features are extracted. In the following diffusion step, DiffJSCC uses the derived multimodal features, together with channel state information such as the signal-to-noise ratio (SNR) and channel gain, to guide the diffusion denoising process through a novel control module. To maintain the balance between realism and fidelity, an optional intermediate guidance approach using the initial image reconstruction is implemented. Extensive experiments on diverse datasets reveal that our method s...