SREC: Encrypted Semantic Super-Resolution Enhanced Communication

Haixiao Gao; Rui Meng; Song Gao; Xiaodong Xu; Zhidi Zhang

REVIEW 3 major objections 7 minor 25 references

SREC: Encrypted Semantic Super-Resolution Enhanced Communication

T0 review · 3 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that modulo-256 encryption of JSCC-encoded semantic features, paired with a super-resolution decoder, can deliver secure image transmission that outperforms unencrypted joint source-channel coding at low SNR.

desk verdict The pipeline is new and the low-SNR SR gain is plausible, but the modulo-256 encryption is not invertible on unquantized real-valued features, so the security claim fails as written. read the letter →

arxiv 2509.04787 v1 pith:VLLIPOGO submitted 2025-09-05 eess.SP

Zhidi Zhang , Rui Meng , Song Gao , Haixiao Gao , Xiaodong Xu This is my paper · ORCID

classification eess.SP

keywords semanticcommunicationjointsource-channelcodingmodulo-256encryptionsuper-resolutionwirelesssecurityimagetransmissionAWGNchannelPSNR

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

The paper proposes SREC, a secure semantic image-transmission scheme that encrypts the output of a joint source-channel encoder with a modulo-256 addition of a pseudorandom key, then applies a super-resolution network at the receiver. The central claim is that this combination preserves the security benefit of encryption while actually improving reconstructed image quality at low signal-to-noise ratios compared with unencrypted JSCC. If true, it would give a lightweight way to protect semantic features from eavesdroppers without sacrificing—and in some regimes improving—transmission performance. The evidence comes from AWGN simulations on a standard image super-resolution test set with three modulation formats, where the super-resolution module lifts PSNR most when channel noise is high.

What carries the argument

The two load-bearing components are modulo-256 encryption and a Residual Dense Network (RDN) for super-resolution. Encryption acts element-wise on the JSCC output vector $s$ as $s_{\mathrm{enc}}(i) = (s(i) + \mathrm{KEY}(i)) \bmod 256$, with KEY a pseudorandom uint8 tensor shared over a secure channel; the security argument is that pseudorandom sequences are indistinguishable from random in polynomial time, so the encrypted features hide content. The RDN, built from residual dense blocks with channel attention and global feature fusion, is applied to the decoded image to recover high-frequency detail and semantic information lost to channel noise and to the encryption/decryption round-trip. The interaction between the two is what carries the argument: encryption introduces bit-error sensitivity, and the super-resolution network repairs exactly that damage at low SNR.

What would settle it

Take a real feature tensor $s$ from the learned JSCC encoder on the test set and apply Eq. (10) directly without a disclosed normalization/quantization step; if the receiver cannot recover $s$ exactly from $(s + \mathrm{KEY}) \bmod 256$ because values lie outside $[0, 256)$, the central invertibility assumption fails.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the error amplification introduced by adding a modulo-256 encryption/decryption layer on top of JSCC can be more than compensated by a super-resolution reconstruction module at the receiver. At a representative low SNR of 4 dB, SREC with QPSK modulation reconstructs images with 3.0 dB higher PSNR than unencrypted NTSCC and 4.6 dB higher than encrypted NTSCC without super-resolution; with BPSK the gains are 2.2 dB and 6.4 dB. At higher SNR, the super-resolution gain shrinks and can become negative, because the network's inferred details are less accurate than directly transmitted details when the channel is clean. The paper also claims that an eavesdropper with full model knowledge but without the key cannot restore image content, since the encrypted feature tensor is pseudorandom-looking.

Load-bearing premise

The load-bearing premise is that the real-valued features output by the JSCC encoder can be normalized and quantized into integer bytes in the range 0–255 so that the modulo-256 addition in Eq. (10) is invertible after decryption; the paper does not specify this step, and for arbitrary real features the modulo operation is not invertible.

Editorial extensions

If this is right

If the claim holds, image-oriented semantic communication systems can add confidentiality with a cheap arithmetic operation plus a standard super-resolution decoder, rather than relying on heavyweight cryptography.
The observed crossover—super-resolution helps at low SNR but can hurt at high SNR—implies that a practical system should switch off or down-weight the SR module when channel conditions are good.
Because the security argument depends only on the pseudorandomness of the key and not on the channel code, the same encryption layer could be attached to other JSCC architectures and still provide the same eavesdropper resistance.
The performance gain at low SNR is modulation-dependent (largest for BPSK, smallest for 16QAM), so modulation selection and SR activation should be co-designed with the encryption layer.

Reading between the lines

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

A testable extension would be to apply the same modulo-256 encryption to other learned source-channel codecs (e.g., deep JSCC for video or speech) and check whether a super-resolution-style post-processor recovers the same low-SNR gains; the mechanism suggests it would, since the decoder-side repair is agnostic to the codec's internal representation.
The paper's security argument is asymptotic (polynomial indistinguishability), but the practical threat model is a keyless eavesdropper who knows the model; a concrete known-plaintext attack using the statistical structure of the learned codec's features would clarify whether the modulo addition provides more than obfuscation.
If the normalization/quantization step needed to make the feature vector integer-valued is lossy, the reported PSNR gains may change when that step is made explicit; re-running the experiments with disclosed quantization is a direct way to bound the real-world trade-off.
The crossover behavior suggests a channel-adaptive policy: use the super-resolution module only when the estimated channel SNR is below a threshold; this could be formalized as a decision rule with a small side-information overhead.

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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

Short version: the system combination is new, the SR post-processor does real work at low SNR, but the encryption step is not a defined inverse for real-valued JSCC features, so both security and performance claims are ungrounded as written. The paper needs a do-over on the crypto side and one missing baseline.

The paper puts NTSCC together with a modulo-256 add-on encryption and an RDN-based SR module. That exact pipeline appears to be new, and the component choices are reasonable for the stated goal of lightweight protection plus low-SNR recovery. The empirical story at 4 dB is coherent: SREC beats plain NTSCC by 2–3 dB under QPSK and BPSK. I believe the gain is mostly from SR, not encryption, but the direction is sensible. Credit where due: it is a straightforward engineering composition, not a hoax, and it is honestly tested against NTSCC and encrypted NTSCC.

The load-bearing flaw is real. Equation (10) defines s_enc(i) = (s(i) + KEY(i)) mod 256, with KEY in [0,255]. But s(i) is a real-valued JSCC channel symbol. There is no stated normalization to [0,256), no quantization step, and no decryption formula. For arbitrary real s, the map is non-injective, so the receiver cannot recover the encoder output. Even if s were confined to [0,256), adding an integer key modulo 256 leaves the fractional part untouched, so the ciphertext leaks a deterministic function of the plaintext; that is not semantic security. The citation to [22] does not help, because that scheme operates on quantized image bytes, not on unquantized neural features. The sentence that pseudorandomness implies semantic security for Equation (10) is exactly the conflation the stress-test note describes.

The performance evaluation also misses the baseline that would separate the two design choices: NTSCC plus SR without encryption. Without it, the claimed low-SNR gains could be entirely due to the SR post-processor. The paper reports PSNR on 25 Urban100 images, with no error bars and no quantitative eavesdropper distortion metric; the eavesdropper curves are visual only. That is thin, but it is not the main problem.

Who is this for? Someone working on secure JSCC image transmission who wants a clear example of how not to borrow a crypto primitive. As a reference contribution it is not ready. I would not cite it in its current form. I would still send it to peer review rather than desk-reject, because the flaw is specific and fixable: specify a quantized representation, prove invertibility, and add the NTSCC+SR baseline. With those changes there may be a modest but useful paper.

Referee Report

3 major / 7 minor

Summary. The paper proposes SREC, an encrypted semantic image transmission system that combines NTSCC-based joint source-channel coding with a modulo-256 encryption step applied to the encoded feature vector and a residual-dense-network (RDN) super-resolution post-processor at the receiver. The authors claim that, over AWGN channels with 16QAM, QPSK, and BPSK modulation, SREC can stably guarantee security against eavesdroppers while also achieving higher PSNR than unencrypted NTSCC at low SNR. Experiments are reported on the Urban100 dataset, with NTSCC as the semantic extraction and JSCC network. The central contributions are the encryption-integrated semantic communication scheme and the introduction of super-resolution to compensate for encryption- and channel-induced distortion, especially under low SNR.

Significance. If the claims were correct, the paper would provide a lightweight encryption layer for semantic communication that is compatible with JSCC and a super-resolution-based performance boost at low SNR. These would be useful for practical wireless semantic systems. However, the security mechanism as specified is mathematically ill-defined: modulo-256 addition on real-valued encoded features is not invertible, and no quantization or normalization is specified. The performance evaluation also lacks an NTSCC+SR baseline, so the reported gains over NTSCC cannot be attributed to the SREC design rather than to the super-resolution post-processor alone. The paper provides no code, no formal proofs, and no error bars, and the security argument is an unsupported transfer from a citation rather than a derivation.

major comments (3)

[Section II-B, Eq. (10)] The encryption operation in Eq. (10), s_enc(i) = (s(i) + KEY(i)) mod 256, is not invertible for the real-valued JSCC output s defined in Eq. (2). Since s(i) is a continuous channel symbol and KEY(i) is an integer in [0,255], the modulo operation either requires s(i) to be confined to [0,256) and quantized to a discrete alphabet, or it is non-injective: values of s(i) that differ by 256 map to the same ciphertext, and the fractional part of s(i) is left unchanged by integer addition, leaking a deterministic function of the plaintext. The manuscript never specifies the normalization range, a quantization step, or the corresponding decryption formula, even though the decoder F_d in Eq. (6) operates on the decrypted vector. This makes the entire reconstruction pipeline undefined as written and invalidates both the security and the low-SNR performance claims.
[Section II-B, security claim] The paper states that because pseudorandom sequences cannot be distinguished from random sequences in polynomial time, the encryption in Eq. (10) is semantically secure, citing [22]. This conflates the pseudorandomness of the key with the confidentiality of the encryption scheme; a pseudorandom key does not, by itself, imply semantic security of the encryption transformation unless the message space, key distribution, and encryption operation satisfy appropriate definitions (for example, a one-time pad over a finite group). No formal security model, reduction, or adversary definition is provided, and the non-invertibility issue for real-valued inputs compounds this problem. A concrete security analysis and, ideally, quantitative evidence that an eavesdropper cannot reconstruct meaningful content are needed to support the 'stably guarantee security' claim.
[Section III-B.1, Figs. 3 and 4] The performance comparison does not include an NTSCC+SR baseline. The reported gains of SREC over NTSCC and over encrypted NTSCC at low SNR, such as the 3.0 dB improvement for QPSK at 4 dB SNR, may be entirely due to the super-resolution post-processor rather than to the combined SREC design. Without a baseline that applies the same RDN super-resolution module to unencrypted NTSCC output, the claim that SREC itself 'achieve better transmission performance' than unencrypted NTSCC is not supported. Adding this ablation is necessary to attribute the improvement to the proposed scheme.

minor comments (7)

[Section II-C, Eq. (24)] The text preceding Eq. (24) refers to the final super-resolution image as I_HR, while Eq. (24) defines I_SR = add_mean(F_final); these notations should be made consistent throughout Section II-C.
[Figure 1 caption] The caption has a typographical error: 'performance ar the receiver' should read 'performance at the receiver'.
[Section III] The text 'Throught simulation analysis' should be 'Through simulation analysis'.
[Section III-A, datasets] The sentence 'We select the DIV2K dataset as the training set and validation set respectively' is ambiguous; the authors should specify which subsets of DIV2K are used for training and validation.
[Algorithm 1] Algorithm 1 lists channel coefficient h and noise power sigma as inputs, but the pseudocode does not use them; either remove them from the input list or explain their role.
[Section III-B.1] The 'baseline scheme-eavesdropper curve' is mentioned but never defined; the authors should describe what baseline is used, what the eavesdropper knows, and what metric is reported for the eavesdropper's reconstruction.
[Section III-B, general] All simulation results are reported as single PSNR values without error bars, confidence intervals, or multiple-seed experiments; given the small test set (25 images) and the stochastic training process, some measure of variability should be reported.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the performance claims are empirical, and the security argument is an unsupported cited transfer rather than a self-referential reduction.

full rationale

The central claims of SREC are evaluated empirically rather than derived from fitted parameters: the architecture chains NTSCC feature extraction and JSCC, a modulo-256 encryption operation, and an RDN-based super-resolution module, with the reported PSNR gains at low SNR obtained from Urban100 simulations after training on DIV2K. This is an external, falsifiable benchmark, so there is no fitted-input-called-prediction step and no self-definitional reduction. The only candidate circular step is the security assertion in Section II-B: "Since there currently exists no algorithm capable of probabilistically distinguishing pseudorandom sequences from random sequences in polynomial time, adversaries cannot distinguish s_enc at any time; thus, the encryption method in Equation 10 is semantically secure [22]." That sentence imports a security conclusion from reference [22], which is not authored by the present paper's authors, and it does not re-derive the conclusion from the paper's own fitted values; it is an unsupported transfer of a known result, not a reduction of the claim to its own input. The absence of a specified quantization or invertible inverse for Eq. (10) when s is real-valued is a correctness gap in the construction, not circularity, because the paper does not define success in terms of that equation. The self-citations in the introduction ([3], [5], [13]) are background references and are not load-bearing for the central empirical or security claims. Therefore no significant circularity is present.

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

The paper introduces no new physical entities. Its central claims rest on an asserted cryptographic property of a simple arithmetic operation applied to uncharacterized real-valued features, and on the training behavior of an unspecified super-resolution network.

free parameters (3)

Channel scaling factor eta = 0.2 (and varied in Figure 5)
A system hyperparameter converting semantic entropy to bandwidth cost; chosen for simulations without optimization and affecting all PSNR curves.
NTSCC training SNR = 10 dB
The semantic encoder-decoder is trained at a fixed channel SNR; performance at other SNRs depends on this choice.
Super-resolution scale factor and output size = Not stated
The SR module's upscaling ratio and training data are not reported; the claimed PSNR gains cannot be reproduced without this information.

assumptions (4)

domain assumption Pseudorandom sequences are computationally indistinguishable from random sequences.
Invoked in Section II-B after Eq. (10) to claim semantic security, citing [22], without proving that the real-valued modulo-256 construction satisfies the required cryptographic properties.
domain assumption The channel is AWGN with known noise power sigma and coefficient h.
Stated in Section II-A Eq. (4); a standard simulation assumption but not justified for the claimed generality.
domain assumption A pre-shared key is available over a secure channel.
Algorithm 1 takes a pre-shared key as input; the key distribution problem is outsourced and not addressed.
ad hoc to paper The encoder output s can be normalized and mapped into [0,255] so that modulo-256 addition is invertible.
The paper states a 'normalized feature tensor' but never specifies the mapping; Eq. (10) is not invertible for arbitrary real s.

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Cite this review

Pith. "Pith review of SREC: Encrypted Semantic Super-Resolution Enhanced Communication." pith.science (2026). https://pith.science/paper/VLLIPOGO

@misc{pith2026250904787,
  author       = {Pith},
  title        = {Pith review of: SREC: Encrypted Semantic Super-Resolution Enhanced Communication},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VLLIPOGO}},
  note         = {Machine review of arXiv:2509.04787}
}

read the original abstract

Semantic communication (SemCom), as a typical paradigm of deep integration between artificial intelligence (AI) and communication technology, significantly improves communication efficiency and resource utilization efficiency. However, the security issues of SemCom are becoming increasingly prominent. Semantic features transmitted in plaintext over physical channels are easily intercepted by eavesdroppers. To address this issue, this paper proposes Encrypted Semantic Super-Resolution Enhanced Communication (SREC) to secure SemCom. SREC uses the modulo-256 encryption method to encrypt semantic features, and employs super-resolution reconstruction method to improve the reconstruction quality of images. The simulation results show that in the additive Gaussian white noise (AWGN) channel, when different modulation methods are used, SREC can not only stably guarantee security, but also achieve better transmission performance under low signal-to-noise ratio (SNR) conditions.

Figures

Figures reproduced from arXiv: 2509.04787 by the authors.

**Figure 1.** The architecture of the proposed SREC, where semantic extraction, JSCC encoding, and encryption on the input image [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** The architecture of the super-resolution based semantic [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Visualization images with η = 0.2 and different modulation schemes from the curves that under low SNR conditions, the superresolution module significantly improves the PSNR of images—particularly for QPSK with SNR below 8 dB and BPSK with SNR below 5 dB, the model with the superresolution module even achieves a better PSNR than the model without encryption and decryption modules. However, as SNR increases, the im… view at source ↗

Figures from the paper (2 more)

**Figure 3.** Figure 3: PSNR-SNR curves with η = 0.2 and different modulation schemes In terms of model structure, the introduction of encryption and decryption modules will amplify the impact of bit errors under low SNR conditions, reducing image quality; under high SNR conditions, the encr…

**Figure 6.** Figure 6: Visualization images with different η and modulation schemes a smaller compression ratio, where patches with high entropy are allocated more bandwidth; a smaller η indicates a higher compression ratio, where the bandwidth allocated to all patches has smaller differenc…

Review history (2 revisions) →

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Reference graph

Works this paper leans on

25 extracted references · 15 canonical work pages

[22]

Scalable coding of encrypted images,

X. Zhang, G. Feng, Y . Ren, and Z. Qian, “Scalable coding of encrypted images,”IEEE transactions on image processing, vol. 21, no. 6, pp. 3108–3114, 2012

work page 2012
[1]

6g global landscape: A comparative analysis of 6g targets and technological trends,

K. Trichias, A. Kaloxylos, and C. Willcock, “6g global landscape: A comparative analysis of 6g targets and technological trends,” in2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2024, pp. 1–6

work page 2024
[2]

Importance-aware robust semantic transmission for leo satellite-ground communication,

H. Cao, R. Meng, X. Xu, S. Han, and P. Zhang, “Importance-aware robust semantic transmission for leo satellite-ground communication,” arXiv preprint arXiv:2508.11457, 2025

arXiv 2025
[3]

Intellicise model transmission for semantic communication in intelligence-native 6g networks,

W. Yining, H. Shujun, X. Xiaodong, M. Rui, L. Haotai, D. Chen, and Z. Ping, “Intellicise model transmission for semantic communication in intelligence-native 6g networks,”China Communications, vol. 21, no. 7, pp. 95–112, 2024

work page 2024
[4]

Important bit prefix m-ary quadrature amplitude modulation for semantic commu- nications,

H. Lu, R. Meng, X. Xu, Y . Liu, P. Zhang, and D. Niyato, “Important bit prefix m-ary quadrature amplitude modulation for semantic commu- nications,”arXiv preprint arXiv:2508.11351, 2025

arXiv 2025
[5]

semantic communication,

P. Zhang, W. Xu, Y . Liu, X. Qin, K. Niu, S. Cui, G. Shi, Z. Qin, X. Xu, F. Wang, Y . Meng, C. Dong, J. Dai, Q. Yang, Y . Sun, D. Gao, H. Gao, S. Han, and X. Song, “semantic communication,”IEEE Communications Surveys & Tutorials, vol. 27, no. 3, pp. 2051–2084, 2025

work page 2025
[6]

Deep joint source- channel coding for wireless image transmission,

E. Bourtsoulatze, D. Burth Kurka, and D. G ¨und¨uz, “Deep joint source- channel coding for wireless image transmission,”IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 567–579, 2019

2019
[7]

Lotterycodec: Search- ing the implicit representation in a random network for low-complexity image compression,

H. Wu, G. Chen, P. L. Dragotti, and D. G ¨und¨uz, “Lotterycodec: Search- ing the implicit representation in a random network for low-complexity image compression,”arXiv preprint arXiv:2507.01204, 2025

arXiv 2025

Show all 25 references

[8]

Security and privacy challenges in semantic communication networks,

Q. T. Do, D. Won, T. S. Do, T. P. Truong, and S. Cho, “Security and privacy challenges in semantic communication networks,” in2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2025, pp. 0032–0035

2025
[9]

Secur- ing semantic communications with physical-layer semantic encryption and obfuscation,

Q. Qin, Y . Rong, G. Nan, S. Wu, X. Zhang, Q. Cui, and X. Tao, “Secur- ing semantic communications with physical-layer semantic encryption and obfuscation,” inICC 2023 - IEEE International Conference on Communications, 2023, pp. 5608–5613

2023
[10]

Semantic entropy can simultaneously benefit transmission efficiency and channel security of wireless semantic communications,

Y . Rong, G. Nan, M. Zhang, S. Chen, S. Wang, X. Zhang, N. Ma, S. Gong, Z. Yang, Q. Cui, X. Tao, and T. Q. S. Quek, “Semantic entropy can simultaneously benefit transmission efficiency and channel security of wireless semantic communications,”IEEE Transactions on Information F...

2025
[11]

Actions speak louder than words: Rate-reward trade-off in markov decision processes,

H. Wu, G. Chen, and D. G ¨und¨uz, “Actions speak louder than words: Rate-reward trade-off in markov decision processes,”arXiv preprint arXiv:2502.03335, 2025

2025 arXiv
[12]

Deep joint source-channel and encryption coding: Secure semantic communications,

T.-Y . Tung and D. G ¨und¨uz, “Deep joint source-channel and encryption coding: Secure semantic communications,” inICC 2023 - IEEE Inter- national Conference on Communications, 2023, pp. 5620–5625

2023
[13]

Secure semantic communication with homomorphic encryption,

R. Meng, D. Fan, H. Gao, Y . Yuan, B. Wang, X. Xu, M. Sun, C. Dong, X. Tao, P. Zhanget al., “Secure semantic communication with homomorphic encryption,”arXiv preprint arXiv:2501.10182, 2025

2025
[14]

Cooperative resource management in quantum key distribution (qkd) networks for semantic communication,

R. Kaewpuang, M. Xu, W. Y . B. Lim, D. Niyato, H. Yu, J. Kang, and X. Shen, “Cooperative resource management in quantum key distribution (qkd) networks for semantic communication,”IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4454–4469, 2024

2024
[15]

Semkey: Boosting secret key generation for ris-assisted semantic communication systems,

R. Zhao, Q. Qin, N. Xu, G. Nan, Q. Cui, and X. Tao, “Semkey: Boosting secret key generation for ris-assisted semantic communication systems,” in2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022, pp. 1–5

2022
[16]

Covert uav data transmission via semantic communication: A drl-driven joint position and power optimization method,

R. Xu, G. Li, Z. Yang, J. Kang, X. Zhang, and J. Li, “Covert uav data transmission via semantic communication: A drl-driven joint position and power optimization method,” in2024 IEEE/CIC International Conference on Communications in China (ICCC), 2024, pp. 66–71

2024
[17]

Secure semantic com- munication for image transmission in the presence of eavesdroppers,

S. Tang, C. Liu, Q. Yang, S. He, and D. Niyato, “Secure semantic com- munication for image transmission in the presence of eavesdroppers,” in GLOBECOM 2024 - 2024 IEEE Global Communications Conference, 2024, pp. 2172–2177

2024
[18]

A survey of super-resolution based on deep learning,

Z. Yang, P. Shi, and D. Pan, “A survey of super-resolution based on deep learning,” in2020 International Conference on Culture-oriented Science & Technology (ICCST), 2020, pp. 514–518

2020
[19]

Seesr: Towards semantics-aware real-world image super-resolution,

R. Wu, T. Yang, L. Sun, Z. Zhang, S. Li, and L. Zhang, “Seesr: Towards semantics-aware real-world image super-resolution,” in2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25 456–25 467

2024
[20]

Single image super-resolution from transformed self-exemplars,

J.-B. Huang, A. Singh, and N. Ahuja, “Single image super-resolution from transformed self-exemplars,” inProceedings of the IEEE confer- ence on computer vision and pattern recognition, 2015, pp. 5197–5206

2015
[21]

A novel encryption-then-lossy-compression scheme of color images using customized residual dense spatial network,

C. Wang, T. Zhang, H. Chen, Q. Huang, J. Ni, and X. Zhang, “A novel encryption-then-lossy-compression scheme of color images using customized residual dense spatial network,”IEEE Transactions on Multimedia, vol. 25, pp. 4026–4040, 2023

2023
[23]

Residual dense net- work for image super-resolution,

Y . Zhang, Y . Tian, Y . Kong, B. Zhong, and Y . Fu, “Residual dense net- work for image super-resolution,” inProceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 2472–2481

2018
[24]

Ntire 2017 challenge on single image super-resolution: Dataset and study,

E. Agustsson and R. Timofte, “Ntire 2017 challenge on single image super-resolution: Dataset and study,” in2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, pp. 1122–1131

2017
[25]

Nonlinear transform source-channel coding for semantic communications,

J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear transform source-channel coding for semantic communications,”IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2300– 2316, 2022

2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [22]

Scalable coding of encrypted images,

X. Zhang, G. Feng, Y . Ren, and Z. Qian, “Scalable coding of encrypted images,”IEEE transactions on image processing, vol. 21, no. 6, pp. 3108–3114, 2012

work page 2012

[2] [1]

6g global landscape: A comparative analysis of 6g targets and technological trends,

K. Trichias, A. Kaloxylos, and C. Willcock, “6g global landscape: A comparative analysis of 6g targets and technological trends,” in2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2024, pp. 1–6

work page 2024

[3] [2]

Importance-aware robust semantic transmission for leo satellite-ground communication,

H. Cao, R. Meng, X. Xu, S. Han, and P. Zhang, “Importance-aware robust semantic transmission for leo satellite-ground communication,” arXiv preprint arXiv:2508.11457, 2025

arXiv 2025

[4] [3]

Intellicise model transmission for semantic communication in intelligence-native 6g networks,

W. Yining, H. Shujun, X. Xiaodong, M. Rui, L. Haotai, D. Chen, and Z. Ping, “Intellicise model transmission for semantic communication in intelligence-native 6g networks,”China Communications, vol. 21, no. 7, pp. 95–112, 2024

work page 2024

[5] [4]

Important bit prefix m-ary quadrature amplitude modulation for semantic commu- nications,

H. Lu, R. Meng, X. Xu, Y . Liu, P. Zhang, and D. Niyato, “Important bit prefix m-ary quadrature amplitude modulation for semantic commu- nications,”arXiv preprint arXiv:2508.11351, 2025

arXiv 2025

[6] [5]

semantic communication,

P. Zhang, W. Xu, Y . Liu, X. Qin, K. Niu, S. Cui, G. Shi, Z. Qin, X. Xu, F. Wang, Y . Meng, C. Dong, J. Dai, Q. Yang, Y . Sun, D. Gao, H. Gao, S. Han, and X. Song, “semantic communication,”IEEE Communications Surveys & Tutorials, vol. 27, no. 3, pp. 2051–2084, 2025

work page 2025

[7] [6]

Deep joint source- channel coding for wireless image transmission,

E. Bourtsoulatze, D. Burth Kurka, and D. G ¨und¨uz, “Deep joint source- channel coding for wireless image transmission,”IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 567–579, 2019

2019

[8] [7]

Lotterycodec: Search- ing the implicit representation in a random network for low-complexity image compression,

H. Wu, G. Chen, P. L. Dragotti, and D. G ¨und¨uz, “Lotterycodec: Search- ing the implicit representation in a random network for low-complexity image compression,”arXiv preprint arXiv:2507.01204, 2025

arXiv 2025