Deep learning-based joint source-channel coding (JSCC) is emerging as a promising technology for effective image transmission. However, most existing approaches focus on transmitting clear images, overlooking real-world challenges such as motion blur caused by camera shaking or fast-moving objects. Motion blur often degrades image quality, making transmission and reconstruction more challenging. Event cameras, which asynchronously record pixel intensity changes with extremely low latency, have shown great potential for motion deblurring tasks. However, the efficient transmission of the abundant data generated by event cameras remains a significant challenge. In this work, we propose a novel JSCC framework for the joint transmission of blurry images and events, aimed at achieving high-quality reconstructions under limited channel bandwidth. This approach is designed as a deblurring task-oriented JSCC system. Since RGB cameras and event cameras capture the same scene through different modalities, their outputs contain both shared and domain-specific information. To avoid repeatedly transmitting the shared information, we extract and transmit their shared information and domain-specific information, respectively. At the receiver, the received signals are processed by a deblurring decoder to generate clear images. Additionally, we introduce a multi-stage training strategy to train the proposed model. Simulation results demonstrate that our method significantly outperforms existing JSCC-based image transmission schemes, addressing motion blur effectively.

Despite the remarkable success of semantic image transmission, existing approaches face the challenge of distortion accumulation. Specifically, as a received image is further forwarded to other devices, reconstruction distortion will accumulate, leading to decreased system stability. In this paper, we propose an idempotent semantic communication system for image transmission to enhance stability. We systematically analyze the factors contributing to this accumulation effect and propose several strategies to mitigate it. First, we design the system using a right-invertible neural network to achieve idempotence, ensuring the decoder functions as the right inverse of the encoder. Second, we introduce a feature discretization mechanism to further reduce distortion accumulation, leveraging the benefits of digitalization over analog transmission. Finally, we employ a recursive training strategy, which incorporates the reconstructed images into the training process to significantly improve overall stability. Empirical results demonstrate that our proposed strategies effectively enhance system stability, minimizing quality degradation and enhancing output consistency across multiple transmissions.

Recent studies in joint source-channel coding (JSCC) have fostered a fresh paradigm in end-to-end semantic communication. Despite notable performance achievements, present initiatives in building semantic communication systems primarily hinge on the transmission of continuous channel symbols, thus presenting challenges in compatibility with established digital systems. In this paper, we introduce a novel approach to address this challenge by developing a multi-order digital joint coding-modulation (MDJCM) scheme for semantic communications. Initially, we construct a digital semantic communication system by integrating a multi-order modulation/demodulation module into a nonlinear transform source-channel coding (NTSCC) framework. Recognizing the non-differentiable nature of modulation/demodulation, we propose a novel substitution training strategy. Herein, we treat modulation/demodulation as a constrained quantization process and introduce scaling operations alongside manually crafted noise to approximate this process. As a result, employing this approximation in training semantic communication systems can be deployed in practical modulation/demodulation scenarios with superior performance. Additionally, we demonstrate the equivalence by analyzing the involved probability distribution. Moreover, to further upgrade the performance, we develop a hierarchical dimension-reduction strategy to provide a gradual information extraction process. Extensive experimental evaluations demonstrate the superiority of our proposed method over existing digital and non-digital JSCC techniques.

Deep learning-based joint source-channel coding (JSCC) is emerging as a potential technology to meet the demand for effective data transmission, particularly for image transmission. Nevertheless, most existing advancements only consider analog transmission, where the channel symbols are continuous, making them incompatible with practical digital communication systems. In this work, we address this by involving the modulation process and consider mapping the continuous channel symbols into discrete space. Recognizing the non-uniform distribution of the output channel symbols in existing methods, we propose two effective methods to improve the performance. Firstly, we introduce a uniform modulation scheme, where the distance between two constellations is adjustable to match the non-uniform nature of the distribution. In addition, we further design a non-uniform modulation scheme according to the output distribution. To this end, we first generate the constellations by performing feature clustering on an analog image transmission system, then the generated constellations are employed to modulate the continuous channel symbols. For both schemes, we fine-tune the digital system to alleviate the performance loss caused by modulation. Here, the straight-through estimator (STE) is considered to overcome the non-differentiable nature. Our experimental results demonstrate that the proposed schemes significantly outperform existing digital image transmission systems.