Efficient and Robust Semantic Image Communication via Stable Cascade

Bilal Khalid; Jaroslaw E. Prilepsky; Pedro Freire; Sergei K. Turitsyn

REVIEW 5 major objections 6 minor 23 references

Efficient and Robust Semantic Image Communication via Stable Cascade

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read An image communication system sending just 0.29% of the image as a latent embedding reconstructs it with a fine-tuned diffusion stage, beating three baselines under channel noise.

desk verdict Plausible Stable-Cascade-based SIC with an honest ablation, but the missing sampling-step count and unnormalized SNR comparison make the headline speedups and quality gains unproven. read the letter →

arxiv 2507.17416 v1 pith:3K6GUKHD submitted 2025-07-23 eess.IV

Bilal Khalid , Pedro Freire , Sergei K. Turitsyn , Jaroslaw E. Prilepsky This is my paper

classification eess.IV

keywords semanticimagecommunicationdiffusionmodelsStableCascadelatentAWGNchannelcompressiongenerativenoiserobustness

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that semantic image communication can be simultaneously tiny, fast, and noise-robust: it transmits a latent image embedding of only 0.29% of the original image size and reconstructs the image with a diffusion model conditioned on that embedding. The system, built from Stable Cascade's VQGAN stage and a fine-tuned latent diffusion stage, is claimed to beat three benchmarks — a segmentation-conditioned diffusion communicator, a Stable Diffusion-based communicator, and JPEG2000 with LDPC coding — in perceptual reconstruction quality over an AWGN channel, with the largest margins at low SNR. It also claims large speedups (over 3x at 512x512 and over 16x at 1024x1024 versus the Stable Diffusion baseline) and low generation randomness, with the LPIPS standard deviation of repeated reconstructions at 0.003 for SNR of 10 dB and above. If these results hold, diffusion-based semantic image communication would shed its two practical blockers: slow inference and unreliable generation.

What carries the argument

The machinery is a compact image embedding $Z$ from the EfficientNet-V2 encoder, used as conditioning for a fine-tuned latent diffusion stage, with a VQGAN (a vector-quantized generative adversarial network) decoder reconstructing the image. Stage A is the VQGAN that maps the $1024\times1024$ image into latent space; stage B is a latent diffusion model trained with the objective $L = \mathbb{E}[\|\epsilon - \bar\epsilon(X_{\mathrm{VG},t}, t, \hat Z)\|_2^2]$, where $\hat Z = Z + \epsilon$ is the AWGN-corrupted embedding. Transmitting $\hat Z$ instead of starting diffusion from a noisy image or from text makes channel noise enter the system as a degraded conditioning signal, and fine-tuning on SNR values between 1 and 20 dB teaches the denoiser to compensate. This mechanism is what lets a [16,12,12] tensor (0.29% of a 512x512 RGB image) produce reconstructions that the paper reports as perceptually superior to the baselines.

What would settle it

Re-run the comparison with a fixed total bit budget: encode the source image with JPEG2000 at the same number of bits as the [16,12,12] embedding plus its required channel coding, transmit both over the same AWGN channel with equal per-symbol energy, and measure LPIPS and FID; if JPEG2000+LDPC or a coded version of the embedding matches or beats the proposed model on perceptual metrics, the claimed superiority under fair rate matching is refuted.

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Extended reading notes

Core claim

The paper's central claim is that a highly compressed image embedding, extracted by an EfficientNet-V2 encoder and transmitted alone over an AWGN channel, carries enough semantic information for a fine-tuned latent diffusion model to reconstruct the original image faithfully. At the receiver, the noisy embedding is used purely as conditioning for stage B of Stable Cascade; the diffusion process predicts noise and produces a VQGAN latent that the stage A decoder turns into pixels. The fine-tuning steps make the denoiser robust to channel impairments, and the paper reports that this design outperforms GESCO, Img2Img-SC, and JPEG2000+LDPC on LPIPS, FID, SSIM, and PSNR across SNR from 1 to 20 dB. On Cityscapes, the average gains over the next-best diffusion baseline are 43% in FID and 55% in LPIPS, with SSIM up 56% and PSNR up 23%; on unseen DIV2K data the model degrades but still produces semantically coherent reconstructions. The paper also reports reconstruction from a [16,12,12] embedding for 512x512 images (compression ratio 341) in 0.78 seconds and from a [16,24,24] embedding for 1024x1024 images in 1.72 seconds, and its ablation shows that the fine-tuning, not the pretrained Stable Cascade model, is what provides the noise robustness.

Load-bearing premise

The headline comparison assumes that ranking all methods at the same received-signal-to-noise ratio, without equalizing transmit power or counting the extra bits used by JPEG2000 and LDPC coding, is a fair way to compare reconstruction quality at equal bandwidth; if the comparison gave every method the same data budget or transmit power, the reported margins over the baselines could change.

Editorial extensions

If this is right

If the reported gains hold, diffusion-based semantic communication can send only a tiny conditioning embedding, making the channel-use cost of image delivery negligible compared with pixel, latent-map, or text-plus-image schemes.
The 0.78-second reconstruction for 512x512 images and 1.72 seconds for 1024x1024 on a single GPU bring diffusion-based image delivery close to interactive use, because the 3x and 16x speedups come from working in Stable Cascade's smaller latent space rather than Stable Diffusion's.
The low generation randomness (LPIPS standard deviation 0.003 at SNR 10 dB and above) means repeated transmission of the same image yields nearly identical reconstructions, which is what a communication system needs for predictable behavior.
Fine-tuning on Cityscapes alone transfers to unseen DIV2K images with semantic coherence if not exact color fidelity, suggesting that one diverse training set could yield a general-purpose semantic image communicator.
The embedding-size ablation shows a tunable bandwidth-fidelity tradeoff: increasing the embedding from [16,24,24] to [16,32,32] improves perceptual scores by more than 10% but lowers the compression ratio from 341 to 192.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural test is to move beyond AWGN to fading or quantized channels; the conditioning-denoiser mechanism would need retraining or adaptation, since only additive white Gaussian noise is evaluated.
The advertised 0.29% figure counts raw tensor dimensionality; a fully rate-matched comparison that includes the embedding's quantization, power normalization, and any digital protection bits could shrink or widen the gap with JPEG2000+LDPC.
The receiver never sees the clean embedding, so the scheme is effectively a learned joint source-channel code; adding a lightweight confidence or feedback signal to detect overly corrupted conditioning is an unexplored direction.
The same conditioning idea could transfer to video by transmitting compact per-frame embeddings, though the paper only mentions video as future work and does not test temporal consistency.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a believable engineering result that transplants Stable Cascade into SIC, fine-tuning stage B on noisy image embeddings. The ablation showing the original SC fails without fine-tuning is the strongest piece of evidence. But the paper never states its own sampling steps, and the SNR comparison is not clearly power/rate-normalized, so the headline 3x/16x speedups and quality gains over Img2Img-SC and JPEG2000+LDPC are not yet pinned down.

What's genuinely new: applying the Würstchen/Stable Cascade two-stage setup to SIC and conditioning stage B on a compact EfficientNet-V2 embedding rather than text. That changes the game relative to text-conditioned diffusion SIC systems like Img2Img-SC because it directly attacks generation randomness. The predictability table supports that: variance 0.003 at 20 dB is real. The paper also does the right thing with ablations: fine-tuning vs. original and embedding size [16,24,24] vs [16,32,32]. And it discloses the GESCO 256x512 limitation and the arbitrary assignment of failed JP2+LDPC decodes, which is more honest than most.

Soft spots, in order: (1) sampling steps for the proposed model are never given. They only report wall-clock times, so the 3x/16x speedup could just be from using 4 steps versus 30. This is a one-line fix and should be in the paper. (2) The SNR definition in Eq. (5) is ambiguous between total and per-symbol power, and no power normalization is specified for JPEG2000+LDPC. With 2304 vs 16384 coefficients on the other side, "SNR = 10 dB" may not mean equal transmit power or equal bandwidth. The compression ratio in Table 1 is also tensor-element count, not bits or channel uses. For a fair comparison, they need at least a rate-matched or power-matched protocol. (3) Failed JP2 decodes assigned PSNR=0/SSIM=0/LPIPS=1/FID=500 will dominate low-SNR averages; better to exclude failures and report success rates separately. (4) GESCO at 256x512 is disclosed but still makes the visual comparison hard to interpret. None of these are fatal to the core idea, but they all matter for the relative claims.

Bottom line: this is a workshop-length empirical paper with a plausible core and honest disclosure. It deserves a serious referee, not a desk reject, and I'd send it to a venue that will hold the authors to a fairer benchmark. If the code and sampling steps materialize, the result is likely to hold qualitatively. For my own work, I wouldn't cite it until the evaluation is cleaned up.

Referee Report

5 major / 6 minor

Summary. The paper proposes SC-SIC, a Stable Cascade-based semantic image communication system. A compact image embedding extracted by EfficientNet-V2 (e.g., [16,12,12] for 512x512 and [16,24,24] for 1024x1024, corresponding to 0.29% of the original tensor size) is transmitted over an AWGN channel, and a fine-tuned Stage B LDM denoises/reconstructs the latent given the corrupted embedding as conditioning, followed by the VQGAN decoder. Experiments on Cityscapes and DIV2K compare the method against GESCO, Img2Img-SC, and JPEG2000+LDPC using LPIPS, FID, SSIM, and PSNR, and include inference-time, predictability, and ablation analyses. The paper's central claims are that this system reconstructs images more faithfully under noisy channels, is computationally faster (3x at 512x512, 16x at 1024x1024 vs Img2Img-SC), and transmits a much smaller conditioning signal.

Significance. If the central claims hold, the paper makes a useful contribution to DM-based semantic image communication: it demonstrates that a complex multi-stage generative architecture can be repurposed for SIC with a very compact learned embedding as conditioning, and it provides ablation evidence that fine-tuning on noisy conditioning is what confers channel robustness. The generalization test on DIV2K is a particularly honest stress test, and the explicit reporting of generation randomness via repeated-sampling LPIPS statistics is a strength. The reliance on external pretrained components (VQGAN, EfficientNet-V2, stage B checkpoint) is clearly acknowledged, and the promise of released code and fine-tuned weights supports reproducibility if honored. The main reservations concern the fairness and interpretability of the benchmark comparisons, not the soundness of the core training recipe.

major comments (5)

[§3.2, Eq. (5), §4.2] The SNR definition in Eq. (5) uses "received signal power" P, but the paper does not state whether P is total power across all transmitted symbols or per-symbol power, nor how the JPEG2000+LDPC signal is power-normalized relative to the analog embeddings. Since the proposed method transmits 2304 analog coefficients for 512x512 images, Img2Img-SC transmits 16384, and JPEG2000+LDPC transmits an unreported number of coded QAM symbols, equal SNR values do not necessarily mean equal transmit power or equal bandwidth; because all headline claims are relative to these baselines, the reported comparisons do not yet establish the claimed superiority.
[§4.2, Figure 5] The number of diffusion sampling steps used by the proposed model is never reported, while the text states that GESCO and Img2Img-SC use 1000 and 30 steps, respectively. The inference-time comparison (0.78 vs 2.53 vs 29.19 s) is only meaningful if step counts are comparable or explicitly held constant; as written, the claimed 3x/16x speed advantage could be an artifact of a smaller number of denoising steps.
[§4.3.1, Figure 4] For failed JPEG2000+LDPC decodes, the paper assigns PSNR=0, SSIM=0, LPIPS=1, and FID=500 before averaging. These arbitrary values can dominate the low-SNR aggregates and inflate the apparent gap between the proposed method and the digital baseline; the authors should exclude failed transmissions and report decode success rates, or otherwise justify the imputation.
[Table 1, §4.3.2] The "0.29% of original image size" figure is a ratio of tensor element counts, not a bit-rate or channel-use comparison. For the JPEG2000+LDPC baseline, the number of channel uses depends on quantization, LDPC parity overhead, and QAM modulation order, none of which are reported, so the bandwidth-efficiency claim is not a rate-matched comparison and cannot be directly compared with the analog transmission of continuous embeddings.
[§4.2] GESCO is evaluated at 256x512 while all other methods are evaluated at 512x512, and the paper does not discuss the effect of this resolution mismatch on PSNR, SSIM, LPIPS, or FID. Since these metrics are not resolution-invariant, the quality comparison against GESCO in Figure 4 and Table 2 is not strictly apples-to-apples.

minor comments (6)

[Eq. (3)] Eq. (3) uses inconsistent notation: the left-hand side and the right-hand side both use XVG,t, but the forward process should read XVG,t = sqrt(αbar_t) * XVG + sqrt(1-αbar_t) * epsilon; please fix.
[Table 1] Table 1 lists DIFFSC and CASC, but these two systems are not compared in the experiments; either add the comparisons or clarify why they are listed only for dimensionality.
[Figure 4] The horizontal axis in Figure 4 starts at 5 dB, so the results at SNR=1 dB, which are described in the text, are not visible in the plots.
[§4.3.5] The statement that increasing the embedding size improves LPIPS, FID, and SSIM by greater than 10% should also report the PSNR change or explain why it is omitted.
[§4.1, §4.2] The manuscript says code and weights "will be accessible" at a GitHub URL; please make the link live and include a short reproducibility statement describing the exact sampling configuration (including step count) used for the proposed model.
[References, Introduction] Minor typographical issues include the duplicated "IEEE" in the DiffSC reference and the use of "V AEs" in the introduction; please proofread.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper's claims are empirical comparisons against external baselines, and the compression ratio is a definitional dimensionality ratio rather than a fitted or self-referential result.

full rationale

The paper contains no derivation chain whose conclusion is smuggled into its assumptions. The only equations (1)-(6) define the VQGAN encoding/decoding maps, the diffusion noise schedule and MSE training loss, and the AWGN channel SNR; none of these is used to derive the headline performance numbers. The 0.29% compression figure is a direct dimensionality ratio (Table 1: [3,512,512] vs [16,12,12]), exactly following the CR definition cited from Jiang et al., so it is definitional rather than a fitted or predicted result. The claimed superiority over GESCO, Img2Img-SC, and JPEG2000+LDPC is assessed by running all models on the same test images and SNR values, with metrics computed from actual outputs; no parameter is fitted to those benchmark results and then re-reported as a prediction. The stage A VQGAN, EfficientNet-V2 encoder, and pretrained stage B checkpoint come from external prior work (Pernias et al., 2023), not from the authors' own prior papers, so there is no load-bearing self-citation. Training is on Cityscapes with random SNR 1-20 dB, while evaluation is on held-out Cityscapes test images plus the unseen DIV2K dataset, so the noise-robustness result is not simply a re-statement of the training objective on training data. Concerns about equal transmit-power or channel-use normalization across baselines are benchmark-fairness issues, not circularity, and do not make any derived quantity equivalent to an input by construction. Therefore no circular step is present and the score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or mathematical entities. Its load-bearing choices are the inherited Stable Cascade components, the embedding size and training SNR range, and the (unreported) sampling step count. The evaluation fairness assumptions are listed as axioms.

free parameters (3)

Image embedding spatial size = [16, 24, 24] for 1024x1024 input, [16, 12, 12] for 512x512
The embedding dimensions are a design choice, not fitted to data, but they directly determine the advertised compression ratio (341x). Ablation (Fig. 8b) shows [16,32,32] improves LPIPS/FID/SSIM by more than 10% at the cost of CR dropping to 192, so the headline 0.29% figure is tied to this hand-chosen size.
Number of diffusion sampling steps = not reported
The inference-time comparison (0.78 s, 3x/16x faster) depends on the number of denoising steps, which the paper never states. Without this, the speed advantage cannot be independently reproduced or assessed.
Training SNR range = 1-20 dB
Fine-tuning stage B with SNR sampled uniformly in 1-20 dB is a choice that directly shapes the robustness curves; a different range would change the low-SNR behavior.

assumptions (4)

domain assumption The pretrained Stable Cascade stage A (VQGAN) and EfficientNet-V2 image encoder provide a compact latent representation that retains enough semantic content for high-fidelity reconstruction after conditioning an LDM.
Invoked in Sections 2 and 3.1; the paper inherits these components from Pernias et al. (2023) without independent validation for the SIC task.
domain assumption The AWGN channel with SNR defined in Eq. (5) via received signal power is a valid and fair basis for comparing analog embedding transmission with digitally coded JPEG2000+LDPC and with other DM-based SIC systems.
Section 3.2 defines the channel; Section 4.2 uses it for all baselines. The fairness of equal-SNR comparison across different signal formats and bandwidths is assumed, not justified.
standard math Standard latent diffusion training with the MSE noise-prediction objective (Eq. 4) and a fixed noise schedule (Eq. 3) is sufficient to learn robustness to noisy conditioning.
Section 2 uses the standard DDPM/LDM objective; the noise schedule parameters are inherited from Stable Cascade.
domain assumption Text conditioning is unnecessary because image embedding conditioning dominates, as noted in the original Stable Cascade paper.
Section 2 relies on the claim in Pernias et al. (2023) that text has no significant impact on stage B quality; the authors did not ablate text conditioning in their own SIC setting.

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Cite this review

Pith. "Pith review of Efficient and Robust Semantic Image Communication via Stable Cascade." pith.science (2026). https://pith.science/paper/3K6GUKHD

@misc{pith2026250717416,
  author       = {Pith},
  title        = {Pith review of: Efficient and Robust Semantic Image Communication via Stable Cascade},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3K6GUKHD}},
  note         = {Machine review of arXiv:2507.17416}
}

read the original abstract

Diffusion Model (DM) based Semantic Image Communication (SIC) systems face significant challenges, such as slow inference speed and generation randomness, that limit their reliability and practicality. To overcome these issues, we propose a novel SIC framework inspired by Stable Cascade, where extremely compact latent image embeddings are used as conditioning to the diffusion process. Our approach drastically reduces the data transmission overhead, compressing the transmitted embedding to just 0.29% of the original image size. It outperforms three benchmark approaches - the diffusion SIC model conditioned on segmentation maps (GESCO), the recent Stable Diffusion (SD)-based SIC framework (Img2Img-SC), and the conventional JPEG2000 + LDPC coding - by achieving superior reconstruction quality under noisy channel conditions, as validated across multiple metrics. Notably, it also delivers significant computational efficiency, enabling over 3x faster reconstruction for 512 x 512 images and more than 16x faster for 1024 x 1024 images as compared to the approach adopted in Img2Img-SC.

Figures

Figures reproduced from arXiv: 2507.17416 by the authors.

**Figure 1.** 1024 × 1024 Image reconstructions using our model under different channel SNR conditions. Even at an SNR of 1 dB, images are faithfully reconstructed and perceptually very similar to the transmitted images. The advancement of Deep Learning (DL) and generative AI has enabled the emergence of SemCom as a viable alternative to traditional communication. DL and generative AI models are used for extracting the relevant … view at source ↗

**Figure 2.** Our system model. At the transmitter side, a compact image embedding Z of size [16, 24, 24] is extracted from an image X of size [3, 1024, 1024]. Z is transmitted across the physical channel. The receiver uses the noisy embedding Zˆ as conditioning for the LDM. Finally, the VQGAN decoder is used to project the image back into pixel space. recent years. In (Grassucci et al., 2023), segmentation maps are used to guide… view at source ↗

**Figure 3.** Image reconstructions using our model, GESCO, Img2Img-SC and JPEG2000+LDPC in low SNR conditions. It can be observed that our model generates the most semantically similar images with the least generation randomness. The red crosses indicate that the JPEG2000+LDPC system was unable to recover the image at the corresponding SNR. using a single NVIDIA RTX A6000 (48-GB) GPU. All code scripts and fine-tuned model weight… view at source ↗

Figures from the paper (4 more)

**Figure 5.** Figure 5: Inference time comparison of our model with GESCO and Img2Img-SC. 4.3.2. INFERENCE SPEED AND BANDWIDTH EFFICIENCY In terms of computational complexity, we evaluate both inference latency and the dimensionality of the transmitted data. As shown in [PITH_FULL_IMAGE:fig…

**Figure 6.** Figure 6: Performance of our model on unseen DIV2K data. 4.3.3. RECONSTRUCTION PREDICTABILITY We assess reconstruction predictability across varying SNR conditions using the LPIPS metric. For each case, we simulate image transmission 25 times with fixed parameters, computing th…

**Figure 8.** Figure 8: Results of ablation experiments highlighting (a) the performance gains obtained via fine-tuning and (b) the impact of increasing the embedding size from [16, 24, 24] to [16, 32, 32] on performance metrics [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: Images reconstructed by the original Stable Cascade model. It can be seen that without proper fine-tuning, the original model fails to deal with the effects of channel noise. 1024 × 1024 images. It can be seen from Figure 8b that there is a noticeable improvement in pe…

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Reference graph

Works this paper leans on

23 extracted references · 14 canonical work pages

[1]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...
[2]

and Timofte, R

Agustsson, E. and Timofte, R. Ntire 2017 challenge on single image super-resolution: Dataset and study. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017

work page 2017
[3]

B., and G \"u nd \"u z, D

Bourtsoulatze, E., Kurka, D. B., and G \"u nd \"u z, D. Deep joint source-channel coding for wireless image transmission. IEEE Transactions on Cognitive Communications and Networking, 5 0 (3): 0 567--579, 2019

work page 2019
[4]

CASC: Condition-Aware Semantic Communication with Latent Diffusion Models

Chen, W. and Yang, Q. Casc: Condition-aware semantic communication with latent diffusion models. arXiv preprint arXiv:2411.06552, 2024

work page Pith review arXiv 2024
[5]

Language-oriented semantic latent representation for image transmission

Cicchetti, G., Grassucci, E., Park, J., Choi, J., Barbarossa, S., and Comminiello, D. Language-oriented semantic latent representation for image transmission. In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), pp.\ 1--6. IEEE, 2024

work page 2024
[6]

The cityscapes dataset for semantic urban scene understanding

Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 3213--3223, 2016

work page 2016
[7]

and Nichol, A

Dhariwal, P. and Nichol, A. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34: 0 8780--8794, 2021

2021
[8]

Taming transformers for high-resolution image synthesis

Esser, P., Rombach, R., and Ommer, B. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 12873--12883, 2021

2021

Show all 23 references

[9]

Generative semantic communication: Diffusion models beyond bit recovery

Grassucci, E., Barbarossa, S., and Comminiello, D. Generative semantic communication: Diffusion models beyond bit recovery. arXiv preprint arXiv:2306.04321, 2023

2023 arXiv
[10]

Diffsc: Semantic communication framework with enhanced denoising through diffusion probabilistic models

Jiang, Z., Liu, X., Yang, G., Li, W., Li, A., and Wang, G. Diffsc: Semantic communication framework with enhanced denoising through diffusion probabilistic models. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp.\ 13071--13...

2024
[11]

Semantic communications: Overview, open issues, and future research directions

Luo, X., Chen, H.-H., and Guo, Q. Semantic communications: Overview, open issues, and future research directions. IEEE Wireless Communications, 29 0 (1): 0 210--219, 2022

2022
[12]

Language-oriented communication with semantic coding and knowledge distillation for text-to-image generation

Nam, H., Park, J., Choi, J., Bennis, M., and Kim, S.-L. Language-oriented communication with semantic coding and knowledge distillation for text-to-image generation. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\ 135...

2024
[13]

L., Pal, C

Pernias, P., Rampas, D., Richter, M. L., Pal, C. J., and Aubreville, M. W \"u rstchen: An efficient architecture for large-scale text-to-image diffusion models. arXiv preprint arXiv:2306.00637, 2023

2023 arXiv
[14]

Qin, Z., Tao, X., Lu, J., Tong, W., and Li, G. Y. Semantic communications: Principles and challenges. arXiv preprint arXiv:2201.01389, 2021

2021 arXiv
[15]

High-resolution image synthesis with latent diffusion models

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 10684--10695, June 2022

2022
[16]

pytorch-fid: FID Score for PyTorch

Seitzer, M. pytorch-fid: FID Score for PyTorch . https://github.com/mseitzer/pytorch-fid, August 2020. Version 0.3.0

2020
[17]

Strinati, E. C. and Barbarossa, S. 6g networks: Beyond shannon towards semantic and goal-oriented communications. Computer Networks, 190: 0 107930, 2021

2021
[18]

and Le, Q

Tan, M. and Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pp.\ 6105--6114. PMLR, 2019

2019
[19]

C., Sheikh, H

Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13 0 (4): 0 600--612, 2004

2004
[20]

A., and Niyato, D

Xia, L., Sun, Y., Liang, C., Zhang, L., Imran, M. A., and Niyato, D. Generative AI for Semantic Communication: Architecture, Challenges, and Outlook . IEEE Wireless Communications, 32 0 (1): 0 132--140, 2025. doi:10.1109/MWC.003.2300351

2025 doi
[21]

Rate-adaptive generative semantic communication using conditional diffusion models

Yang, P., Zhang, G., and Cai, Y. Rate-adaptive generative semantic communication using conditional diffusion models. IEEE Wireless Communications Letters, 14 0 (2): 0 539--543, 2025

2025
[22]

F., Niu, X., Bai, B., Han, W., Deng, L., and G \"u nd \"u z, D

Yilmaz, S. F., Niu, X., Bai, B., Han, W., Deng, L., and G \"u nd \"u z, D. High perceptual quality wireless image delivery with denoising diffusion models. In IEEE INFOCOM 2024-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp.\ 1--5. IEEE, 2024

2024
[23]

A., Shechtman, E., and Wang, O

Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 586--595, 2018

2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

[2] [2]

and Timofte, R

Agustsson, E. and Timofte, R. Ntire 2017 challenge on single image super-resolution: Dataset and study. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July 2017

work page 2017

[3] [3]

B., and G \"u nd \"u z, D

Bourtsoulatze, E., Kurka, D. B., and G \"u nd \"u z, D. Deep joint source-channel coding for wireless image transmission. IEEE Transactions on Cognitive Communications and Networking, 5 0 (3): 0 567--579, 2019

work page 2019

[4] [4]

CASC: Condition-Aware Semantic Communication with Latent Diffusion Models

Chen, W. and Yang, Q. Casc: Condition-aware semantic communication with latent diffusion models. arXiv preprint arXiv:2411.06552, 2024

work page Pith review arXiv 2024

[5] [5]

Language-oriented semantic latent representation for image transmission

Cicchetti, G., Grassucci, E., Park, J., Choi, J., Barbarossa, S., and Comminiello, D. Language-oriented semantic latent representation for image transmission. In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), pp.\ 1--6. IEEE, 2024

work page 2024

[6] [6]

The cityscapes dataset for semantic urban scene understanding

Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 3213--3223, 2016

work page 2016

[7] [7]

and Nichol, A

Dhariwal, P. and Nichol, A. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34: 0 8780--8794, 2021

2021

[8] [8]

Taming transformers for high-resolution image synthesis

Esser, P., Rombach, R., and Ommer, B. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 12873--12883, 2021

2021