Leveraging Overfitting for Low-Complexity and Modality-Agnostic Joint Source-Channel Coding

Fuente: arXiv
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Autori principali: Wu, Haotian, Li, Gen, Dragotti, Pier Luigi, Gündüz, Deniz
Natura: Preprint
Pubblicazione: 2025
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author Wu, Haotian
Li, Gen
Dragotti, Pier Luigi
Gündüz, Deniz
author_facet Wu, Haotian
Li, Gen
Dragotti, Pier Luigi
Gündüz, Deniz
contents This paper introduces Implicit-JSCC, a novel overfitted joint source-channel coding paradigm that directly optimizes channel symbols and a lightweight neural decoder for each source. This instance-specific strategy eliminates the need for training datasets or pre-trained models, enabling a storage-free, modality-agnostic solution. As a low-complexity alternative, Implicit-JSCC achieves efficient image transmission with around 1000x lower decoding complexity, using as few as 607 model parameters and 641 multiplications per pixel. This overfitted design inherently addresses source generalizability and achieves state-of-the-art results in the high SNR regimes, underscoring its promise for future communication systems, especially streaming scenarios where one-time offline encoding supports multiple online decoding.
format Preprint
id arxiv_https___arxiv_org_abs_2512_20981
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Leveraging Overfitting for Low-Complexity and Modality-Agnostic Joint Source-Channel Coding
Wu, Haotian
Li, Gen
Dragotti, Pier Luigi
Gündüz, Deniz
Image and Video Processing
Information Theory
68P30, 94A08
I.4.2; E.4
This paper introduces Implicit-JSCC, a novel overfitted joint source-channel coding paradigm that directly optimizes channel symbols and a lightweight neural decoder for each source. This instance-specific strategy eliminates the need for training datasets or pre-trained models, enabling a storage-free, modality-agnostic solution. As a low-complexity alternative, Implicit-JSCC achieves efficient image transmission with around 1000x lower decoding complexity, using as few as 607 model parameters and 641 multiplications per pixel. This overfitted design inherently addresses source generalizability and achieves state-of-the-art results in the high SNR regimes, underscoring its promise for future communication systems, especially streaming scenarios where one-time offline encoding supports multiple online decoding.
title Leveraging Overfitting for Low-Complexity and Modality-Agnostic Joint Source-Channel Coding
topic Image and Video Processing
Information Theory
68P30, 94A08
I.4.2; E.4
url https://arxiv.org/abs/2512.20981