Zero-shot diffusion image compression based on reverse channel coding achieves high perceptual quality at low bitrates without retraining, yet its bitstream remains vulnerable to bit flips. Prior work has shown that a joint combinatorial representation can amplify a single bit flip into substitutions of multiple atoms and that indexing each atom independently confines this error propagation. However, independent indexing reduces the number of atoms that fit within the bit budget and gives the decoder no indication of which atoms are corrupted. To address these issues, we propose BitFlat, a training-free error-localizing bitstream framework with two components. Blockwise Locality Alignment (BLA) decomposes the globally coupled atom selection at each diffusion step into independent selection units, each describing one signed atom from a dedicated codebook with a fixed-width field, so that any bit error affects only the unit in which it occurs. Reliability-Aware Diffusion Control (RADC) uses the bits saved by BLA to impose structured valid-codeword constraints within each field and converts the decoding state of each field into a binary reliability gate, which turns detected corrupted atoms into erasures at known positions and excludes them from noise synthesis. BitFlat requires neither target residuals nor additional network forward passes and leaves the diffusion model unchanged. Experiments on Kodak, DIV2K, and CLIC2020 show that BitFlat outperforms conventional, learned, and diffusion-based codecs at bit error rates from 10−4 to 10−2 and, even with 26.7% fewer bits, surpasses Robust Turbo-DDCM.
Figures & tables
Fig. 1: Effect of the bitstream representation on error propagation. A bit flip can cause multiple atoms under joint combinatorial coding. Independent indexing localizes substitutions, while BitFlat converts detected corrupted selections into erasures.
Fig. 2: Overview of BitFlat. (a) BLA represents signed atom selections from dedicated codebooks as independent fixed-width fields. (b) RADC converts field-level decoding states into binary reliability gates, excluding detected but uncorrectable selections from noise synthesis. The retained atoms are aggregated and rescaled to drive reverse diffusion. All pretrained networks remain frozen, and no additional network forward passes are required.
Fig. 3: PSNR, LPIPS, and DISTS of different methods versus BER on the Kodak, DIV2K, and CLIC2020 datasets. Missing points indicate that all decodings fail at that BER.
Fig. 4: Visual comparison of different methods at different BERs. From top to bottom, the rows show Kodak #7 at 10−4 , Kodak #21 at 3×10−4 , DIV2K 0802 at 10−3 , DIV2K 0898 at 3×10−3 , and a CLIC2020 image at 10−2 . Turbo, Robust, and Ours denote Turbo-DDCM, Robust Turbo-DDCM, and BitFlat, respectively. The bpp and the LPIPS averaged over five channel realizations are reported below each image. N/A indicates decoding failure, for which LPIPS is unavailable and marked as —.
Dataset
Method
bpp
PSNR (dB) ↑
LPIPS ↓
DISTS ↓
10−4
3×10−4
10−3
3×10−3
10−2
10−4
3×10−4
10−3
3×10−3
10−2
10−4
3×10−4
10−3
3×10−3
10−2
Kodak
Robust Turbo-DDCM
0.1003
24.10
23.70
22.51
20.76
17.73
0.149
0.162
0.207
0.290
0.481
0.091
0.098
0.122
0.160
0.250
Robust Turbo-DDCM
0.0728
23.62
23.23
22.23
20.60
17.74
0.183
0.197
0.240
0.321
0.498
0.117
0.124
0.145
0.182
0.260
BitFlat (Ours)
0.0735
24.36
24.15
23.64
22.61
20.32
0.134
0.141
0.157
0.196
0.311
0.083
0.085
0.094
0.114
0.168
DIV2K
Robust Turbo-DDCM
0.1003
23.28
22.86
21.62
19.70
16.64
0.158
0.170
0.209
0.287
0.469
0.106
0.113
0.133
0.170
0.259
Robust Turbo-DDCM
0.0728
22.62
22.25
21.15
19.43
16.51
0.200
0.210
0.251
0.327
0.502
0.136
0.142
0.161
0.199
0.283
TABLE I: Comparison with Robust Turbo-DDCM at a reduced bitrate. Each value is averaged over all images and five channel realizations. All gains of BitFlat over both Robust Turbo-DDCM configurations are statistically significant under image-level paired t -tests at p<0.05 .
Method
Encoding (s)
Decoding (s)
Memory (GiB)
DDCM
55.12 ± 0.94
31.49 ± 0.57
3.78
Turbo-DDCM
1.68 ± 0.12
1.58 ± 0.11
3.03
Robust Turbo-DDCM
1.46 ± 0.10
1.63 ± 0.09
3.03
BitFlat (Ours)
1.83 ± 0.10
1.16 ± 0.09
3.03
TABLE II: Per-image encoding time, decoding time, and peak GPU memory on Kodak, reported as mean ± standard deviation.
Dataset
ID
BLA
Constraint
Gating
G
PSNR (dB) ↑
LPIPS ↓
DISTS ↓
10−4
3×10−4
10−3
3×10−3
10−2
10−4
3×10−4
10−3
3×10−3
10−2
10−4
3×10−4
10−3
3×10−3
10−2
Kodak
(a)
–
–
–
73
24.10
23.70
22.51
20.76
17.73
0.149
0.162
0.207
0.290
0.481
0.091
0.098
0.122
0.160
0.250
(b)
–
✓
–
68
24.05
23.66
22.53
20.89
17.54
0.151
0.164
0.207
0.285
0.498
0.093
0.098
0.121
0.159
0.255
(c)
–
✓
✓
68
24.14
23.92
23.27
22.17
19.35
0.148
0.155
0.177
0.223
0.377
0.091
0.095
0.107
0.129
0.200
(d)
✓
–
–
99
24.86
24.47
23.39
21.58
18.77
0.108
0.118
0.153
0.226
0.383
0.066
0.071
0.090
0.123
0.195
(e)
✓
✓
–
99
24.76
24.32
23.29
21.75
18.77
0.113
0.126
0.160
0.221
0.384
0.070
0.077
0.094
0.122
0.195
TABLE III: Ablation study of BLA and RADC under the same bit budget. RADC is decomposed into the valid-codeword constraint and reliability gating.
Dataset
Field code
Lγ
G
PSNR (dB) ↑
LPIPS ↓
DISTS ↓
10−4
3×10−4
10−3
3×10−3
10−2
10−4
3×10−4
10−3
3×10−3
10−2
10−4
3×10−4
10−3
3×10−3
10−2
Kodak
Parity
11
99
24.85
24.61
23.96
22.83
20.40
0.109
0.116
0.136
0.175
0.288
0.069
0.072
0.083
0.103
0.149
Shortened Hamming
14
78
24.58
24.58
24.56
24.47
23.29
0.124
0.124
0.124
0.127
0.164
0.077
0.077
0.077
0.079
0.097
SECDED
15
73
24.46
24.46
24.45
24.38
23.66
0.131
0.131
0.131
0.133
0.155
0.081
0.081
0.081
0.082
0.093
DIV2K
Parity
11
99
24.22
24.00
23.33
22.04
19.31
0.116
0.121
0.139
0.179
0.289
0.077
0.080
0.090
0.110
0.163
Shortened Hamming
14
78
23.84
23.84
23.82
23.71
22.64
0.135
0.135
0.136
0.139
0.169
0.092
0.092
0.092
0.094
0.110
TABLE IV: Comparison of field codes in RADC with Kg=512 under the same bit budget. A stronger code requires a wider field Lγ and therefore transmits fewer atoms G per diffusion step.
Modern image compression methods are typically optimized for the rate--distortion--perception trade-off, whereas their robustness to bit-level corruption is rarely examined. We show that diffusion-based compressors built on the Reverse Channel Coding (RCC) paradigm are substantially more robust to bit flips than classical and learned codecs. We further introduce a more robust variant of Turbo-DDCM that significantly improves robustness while only minimally affecting the rate--distortion--perception trade-off. Our findings suggest that RCC-based compression can yield more resilient compressed representations, potentially reducing reliance on error-correcting codes in highly noisy environments.
Amit Vaisman, Gal Pomerants, Raz Lapid
Department of Computer Science Technion - Israel Institute of Technology · Deepkeep Tel Aviv, Israel
Diffusion-based image compression has achieved strong perceptual quality at ultra-low bitrates. However, existing codecs are often tied to specific backbones and specialized components, making diverse, rapidly evolving generative models difficult to reuse. This raises a natural question: Can modern generative foundation models be connected to image compression through a simple and extensible interface? Two insights guide our design: stronger generative priors make a simpler codec interface viable, and generation and compression can be intrinsically linked through latent transport. We therefore propose Gen2-IC with two stages: (1) Latent Compression maps clean image latents to entropy-constrained latents; and (2) Latent Transport refines them with one near-terminal update based on the pretrained model. Gen2-IC requires neither auxiliary conditioning signals nor task-specific backbone modifications. With lightweight adaptation and no distillation, it supports fast encoding and one-step decoding across multiple bitrates. We validate Gen2-IC on SD-2.1, SANA-1.5, FLUX.1-dev, and Qwen-Image-2512, spanning U-Net and Transformer architectures as well as diffusion and flow-matching formulations. With stronger priors, Gen2-IC delivers gains below 0.05 bpp: the Qwen variant leads diffusion-based generative codecs in reconstruction fidelity (PSNR), perceptual similarity (LPIPS and DISTS), and recognizer-based semantic fidelity (OCR CER/WER and face-ROI similarity) across four benchmarks.
Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success in visual media applications. In particular, diffusion methods have enabled new approaches to data compression, where realistic reconstructions can be generated at extremely low bit-rates. This article provides a unifying review of recent diffusion-based methods for generative lossy compression, with a focus on image compression. These methods generally encode the source into an embedding and use a diffusion model to iteratively refine it during decoding, so that the reconstruction approximately follows the true data distribution. The embedding can take various forms and is typically transmitted via an auxiliary entropy model, and recent methods also explore the use of diffusion models themselves for information transmission via channel simulation. We review representative approaches through the lens of rate-distortion-perception theory, highlighting the role of common randomness and connections to inverse problems, and identify open challenges.
Yibo Yang, Stephan Mandt
Chan Zuckerberg Biohub · University of California Irvine