JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications
📰 ArXiv cs.AI
Learn how JSCGC improves wireless generative communications by jointly optimizing source, channel, and generation coding for more realistic image reconstructions
Action Steps
- Apply JSCGC to wireless image transmission using generative models
- Configure the JSCGC framework to optimize source, channel, and generation coding jointly
- Test the performance of JSCGC using metrics that capture human visual perception
- Compare the results with traditional separation-based coding and JSCC methods
- Implement JSCGC in a wireless communication system to evaluate its practicality
Who Needs to Know This
Researchers and engineers working on wireless communication systems and generative models can benefit from this approach to improve image reconstruction quality
Key Insight
💡 JSCGC can capture complex human visual perception and produce more realistic image reconstructions than traditional methods
Share This
💡 JSCGC: a new approach to wireless generative communications that jointly optimizes source, channel, and generation coding for better image reconstructions #JSCGC #WirelessComms #GenerativeModels
Key Takeaways
Learn how JSCGC improves wireless generative communications by jointly optimizing source, channel, and generation coding for more realistic image reconstructions
Full Article
Title: JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications
Abstract:
arXiv:2606.12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory. However, relying on generic distortion metrics fails to capture complex human visual perception, often resulting in blurred or unrealistic reconstructions. In this paper, we propose Joint Source-Channel-Generation Coding (JSCGC), a generative communication
Abstract:
arXiv:2606.12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory. However, relying on generic distortion metrics fails to capture complex human visual perception, often resulting in blurred or unrealistic reconstructions. In this paper, we propose Joint Source-Channel-Generation Coding (JSCGC), a generative communication
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