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Found 13 papers, 4 papers with code
Rate-Distortion-Perception Controllable Joint Source-Channel Coding for High-Fidelity Generative Communications
Based on this framework, we introduce a distortion-perception controllable transmission (DPCT) model, which addresses the variation in the perception-distortion trade-off.
DiffCom: Channel Received Signal is a Natural Condition to Guide Diffusion Posterior Sampling
Unlike traditional systems that rely on deterministic decoders optimized solely for distortion metrics, our DiffCom leverages raw channel-received signal as a fine-grained condition to guide stochastic posterior sampling.
Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency
In this article, we reveal the dual-functionality of deep generative models that reshapes both data compression for efficiency and transmission error concealment for resiliency.
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years.
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials
We first focus on those two paradigms of NeurJSCC by identifying their common and different components in building end-to-end communication systems.
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain.
Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding
Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain.
WITT: A Wireless Image Transmission Transformer for Semantic Communications
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT).
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding
Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable communications.
Wireless Deep Video Semantic Transmission
In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels.
Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission
However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM).
Distributed Image Transmission using Deep Joint Source-Channel Coding
In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node.
Nonlinear Transform Source-Channel Coding for Semantic Communications
In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding.
