arXiv:2610.25803v2 Announce Type: replace Abstract: Semantic error correction (SEC) uses the semantic knowledge of pretrained models to complement conventional channel decoding. By combining physical-layer decoding information with application-layer semantic predictions, cross-layer SEC (CL-SEC) has demonstrated substantial performance gains in text transmission; however, its applicability to image transmission remains largely unexplored. This paper proposes Image CL-SEC, which, to the best of our knowledge, is the first extension of the CL-SEC framework to image transmission. Image CL-SEC uses channel-decoding information together with contextual predictions from a pretrained visual model to resolve residual uncertainty after forward error correction (FEC) decoding. In our implementation, images are represented as discrete visual tokens. For each token position selected for re-evaluation, the probability distribution produced by the channel decoder is combined with the distribution predicted by the visual model to identify the most likely transmitted token. The resulting decision therefore reflects both channel evidence and visual context. Experiments across multiple channel conditions show that, at low and moderate signal-to-noise ratios (SNRs), Image CL-SEC improves pixel-level fidelity, perceptual similarity, and token-recovery accuracy relative to either channel decoding or visual-model inference alone. Supplementary experiments using different pretrained visual models further demonstrate that these gains are not specific to a single model.