arXiv (All)
open access
High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models
computer-vision-and-pattern-recognitionimage-and-video-processinginformation-theorymachine-learningmultimedia
We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver. Specifically, we are interested in the perception-distortion trade-off in the practical
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Mean Spatial Frequency Decoupling for Learning-Based Uplink-to-Downlink Covariance Conversion in FDD Massive MIMO#1155531
- Neural network based image coding quality prediction#448978
- Noise-Induced Restoration of Entropy Concavity Near Gaussian Equilibrium#1386365
- Hybrid neural denoising for resource-efficient near- and sub-threshold radio triggering of extensive air showers#635850
- Hybrid neural denoising for resource-efficient near- and sub-threshold radio triggering of extensive air showers#633501
Record · ID 1413470
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive