OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
Authors: Jinpei Guo
Yifei Ji
Zheng Chen
Kai Liu
Min Liu
Wang Rao
Wenbo Li
Yong Guo
Yulun Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion Model
Image Compression
One-step Diffusion
Primary area: applications

Abstract: Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconstruct images by iteratively denoising from random noise, guided by compressed latent representations. While these approaches have achieved high reconstruction quality, their multi-step sampling process incurs substantial computational overhead. Moreover, they typically require training separate models for different compression bit-rates, leading to significant training and storage costs. To address these challenges, we propose a one-step diffusion codec across multiple bit-rates. termed OSCAR. Specifically, our method views compressed latents as noisy variants of the original latents, where the level of distortion depends on the bit-rate. This perspective allows them to be modeled as intermediate states along a diffusion trajectory. By establishing a mapping from the compression bit-rate to a pseudo diffusion timestep, we condition a single generative model to support reconstructions at multiple bit-rates. Meanwhile, we argue that the compressed latents retain rich structural information, thereby making one-step denoising feasible. Thus, OSCAR replaces iterative sampling with a single denoising pass, significantly improving inference efficiency. Extensive experiments demonstrate that OSCAR achieves superior performance in both quantitative and visual quality metrics. The code and models are available at https://github.com/jp-guo/OSCAR/.

OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
Jinpei Guo!.2, Yifei Ji Zheng Chen', Kai Liu', Min Liu', Wang Rao!, Wenbo Li, Yong Guo Yulun Zhang Carnegii Mellon University, Shanghai Jiao Tong University, Joy Future Academy, Soouth China University of Technology
Abstract
Pretrained latent diffusion models have shown strong potential for lossy image com- pression, owing to their powerful generative priors. Most existing diffusion-bar based methods reconstruct images by iteratively denoising from random noise, guided by compressed latent representations. While these approaches have achieved high reconstruction quality, their multi-step sampling process incurs substantial compu- tational overhead. Moreover, they typically require training separate models for different compression bit-rates, leading to significant training and storage costs. To address these challenges, propose one-step diffusion codec across multiple bit-rates. termed OSCAR. Specifically, our method views compressed latents as noisy variants of the original latents, where the level of distortion depends the bit-rate. This perspective allows them to be modeled as intermediate states along diffusion trajectory. By establishing mapping from the compression bit-rate to a pseudo diffusion timestep. we condition a single generative model to support reconstructions multiple bit-rates. Meanwhile, latents retain rich structural information, thereby making one-step denoising fea- sible. Thus, OSCAR replaces iterative sampling with a single denoising pass, significantly improving inference efficiency. Extensive experiments demonstrate that OSCAR achieves superior performance in both quantitative and visual quality metrics. The code and models are available at https://gihuub.co//ppog////////
argue that the compressed
Introduction
With the explosive growth of multimedia content, the need for efficient transmission and storage of high-resolution images has become increasingly critical. Traditional image compression has long relied manually crafted codecs [53 7, which tend to falter at very low bit rates (5 0.2 bpp), where blocking and ringing artifacts severely degrade perceptual quality. Recent research has shifted towards learning- based wansform-coding systems [5] 14] 22 that jointly optimize end-to-end rate- distoriin (R-D) objective 44. Although these neural codecs generally outperform the hand- crafted compression algorithms, the focus on minimizing R-D inevitably sacrifices fine-grained realism at extreme compression bit-rates [9] producing images that appear synthetic or over-smoothed. In addition, supporting multiple compression bit-rates generally means training multiple models, each optimized with a rate-distortion loss weighted by different Lagrange multiplier (A). Maintaining one model per dramatically inflates both training time and storage requirements.
most notably large-scale text-to-image latent diffusion models (LDMs) 42), have revealed powerful generative priors that be repurposed for a wide range of downstream vision tasks 66 10 20). Building on this insight, diffusion based codecs 47 32 3 have been proposed for image compression. These models typically perform
Recent breakthroughs diffusion models 45
"Correponning author: Yulun Zhang, 10lun100@@p gmail.com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Ours 0.03 1bpp
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Figure 1: Qualitative comparison. OSCAR is capable of reconstructing complex textures with high realism. OSCAR effectively reconstructs complex textures with high realism.
quantization in the latent space to enable compression, achieving lower distortion given rate than conventional CNN- or Transformer-base based methods. Some works 12] further integrate pretrained LDMs with vector-quantization mechanism [5T [7]. allowing the compression rate be prede- termined via fixed hyper-encoder. Their practicality, however, is still hindered by two issues: (i) the multiple denoising iterations are required inference time, which impose substantial inference computational overhead; and (ii) achieving multiple bit-rates still requires training separate diffusion networks. Since diffusion models typically have larger model size than conventional learned codecs, this one-model-perr peree-te strategy further amplifies both training and storage costs. These constraints limit real-world deployment, particularly in resource-constrained scenarios.
To address the aforementioned challenges, we propose OSCAR, a one-step diffusion codec across multiple bit-rates. Unlike previous approaches [58
that start from Gaussian noise, introduce the concept of pseudo diffusion timestep. bridging the quantization process and the forward diffusion. This idea inspired by classical signal processing result that quantization noise, especially at higher bit-rates, can be approximated as additive Gaussian noise (8). Formally, we posit that the quantized latents can be decomposed as 2 vā,zo + vI- āēe, where e approximately Gaussian noise and statistically independent of Zo. ã₁ is the diffusion noise schedule, and f. is the pseudo diffusion timestep that depends on the bit-rate. Under the assumption that is orthogonal to 2o in expectation, the expected cosine similarity between ă and 2g satisfies E[sim 2o)] & vā, (Please refer to Appendix for a full derivation). To make this relationship more consistent, we adopt representation alignment training between ž and Zo. We empirically observe that this training paradigm enables their cosine similarity to converge to fixed value for each bit-rate. Thus, by matching measured cosine similarities to the theoretical form, each bit-rate can be directly mapped to pseudo diffusion timestep t. Our experiments provide strong support for this modeling, as the measured distribution of closely follows the assumed Gaussian distribution.
34 12
We further illustrate this perspective Fig. [l), where the quantized latent features can be interpreted as the intermediate diffusion states along diffusion trajectories. Remarkably, even under extreme compression, the cosine similarity between and Zg stabilizes to a high value once representation alignment is applied. This suggests that the 2 retains rich structural information and is amenable to one-step denoising. Since each bit-rate corresponds to a distinct pseudo timestep, the reconstruction process can be unified: A single diffusion model can denoise quantized latents at different compression bit-rates in one pass, conditioned on their respective pseudo diffusion timesteps. As shown in Fig, 2(b) OSCAR achieves strong quality -eflitt-ffficiencyy tradeoff. Furthermore, Fig. shows that OSCAR can faithfully restore fine textures and perceptually coherent details at low bit-rates.
Our main contributions are summarized as follows:
We propose novel and efficient one-step diffusion codec OSCAR, which reconstructs compressed latents into high-quality images in a single denoising step. Unlike existing

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(a) Illustration of mapping between bit-rates timesteps
(b) BD-Rate comparison on Kodak dataset.
Figure 2: Left: Our method bridges quantization and diffusion by mapping bit-rates to pscudo diffusion timesteps. Right: BD-rates measured by DISTS [6], with circle radius indicating multiply- accumulate operations (MACs). OSCAR achieves favorable quality-efficiency tradeoff.
diffusion codecs that require multiple iterative steps, our approach enables rapid and high- fidelity decoding. To the best of our knowledge, this is the first work explore one-step diffusion for image compression.
We propose a unified network design that supports compression at multiple bit-rates. By bridging quantization and forward diffusion, our approach enables a single generative model to handle diverse bit-rates without the need to train separate models. This unified design reduces storage requirements and simplifies training.
Through extensive experiments, we demonstrate that OSCAR exhibits clear advantages over traditional codecs and learning- baaseng-based methods in both quantitative metrics and visual quality. Morcover, it achieves competitive performance compared to multi-step diffusion codecs, while operating orders of magnitude faster.
2 Related Work
2.1 Neural Image Compression
Deep learning has enabled neural image compression methods to outperform traditional codecs such as JPEG [53], BPG 77. and VVC (TT]. Pioneering work (4] introduced end-to-end autoencoders
for learned compression, followed by efforts [5] 38 14
22 jointly optimize rate and distortion
through improved latent representations. Yet, distortion- based losses (e.g.. MSE) often lead to distribution shifts between reconstructed and natural images [9] motivating research B 23 [ to enhance perceptual quality. HiFiC [37 explores the integration of GANs 19 with compression. MS- 3 improves reconstruction quality through improved discriminator design. Other methods leverage textual semantics [30]. causal modeling [21]. or decoding time guidance [1] to further boost perceptual fidelity. However, the aforementioned methods typically require training separate models for each bit-rate, which results in substantial overhead.
ILLMM
To address the inefficiency of training separate models for each bit-rate, recent research has explored adaptive compression strategies that enable flexible bit-rate control. In particular, progressive compression [50] 59) has attracted significant interest for its ability produce scalable bitstreams. DPICT [31] encodes latent features into trit-plane bitstreams, allowing fine-grained scalability. CTC 26 extends this idea by introducing context-based trit-plane coding. Mcanwhile, variable-rate approaches [13] 54 have demonstrated effectiveness by dynamically adjusting scalar parameters or employing conditional convolutions to accommodate diverse quality levels. Despite their flexibility, these methods tend to suffer from notable quality degradation at very low bit-rates.
2.2 Diffusion Models
Diffusion models are powerful generative frameworks that transform random noise into coherent data through a multi-step denoising process (24. Denoising Diffusion Implicit Models (DDIM) [46]

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Overview of OSCAR. Given input image, the VAE encoder first maps it to the latent space. The latent is then processed by a bit-rate-specifie hyper-encoder and quantized via the corresponding codebook. During decompression, the bit-rate mapped to a pscudo diffusion timestep, and the quantized features are denoised by U-Net conditioned on this timestep to recover the latent. Finally, the VAE decoder reconstructs the image from the restored latent.
Figure 3:
accelerate this process without compromising quality, influencing many subsequent advances. Latent Diffusion Models (LDMs) further improve efficiency by operating in compressed latent space. Stable Diffusion (SD) 42 advances the LDM architecture into a scalable rext-to-image system, enabling flexible multimodal conditioning while maintaining strong image fidelity. In our work, we build on SD to benefit from its strong image generation capabilities.
Beyond image synthesis, diffusion models have also been explored in image compression Early work [24] explored diffusion-based compression through reverse channel coding, with an emphasis rate-distortion trade-offs. Follow-up studies 447 extended this line by applying the technique to lossy transmission of Gaussian samples. Recent approaches further leverage large-scale pretrained diffusion models enhance performance [52]. CDC [58] replaces the VAE decoder with a conditional diffusion model, reconstructing images via reverse diffusion conditioned on learned content latent. PerCo 112 adopts VQ-VAE-style codebook for discrete representation and incorporates global textual descriptions to guide the iterative decoding process. DiffEIC [34] uses lightweight control module guide a frozen diffusion model with compressed content. While these models achieve strong results, their multi-step denoising process remains computationally expensive. Improving sampling efficiency therefore essential for practical diffusion-bar image compression.
40
3 Method
3.1
Preliminaries: One-Step Latent Diffusion Model
Latent Diffusion Models (LDMs)
42 operate in a lower-dimensional latent space rather than pixel space, enabling more efficient training and generation. The forward diffusion process perturbs a clean latent variable by injecting Gaussian noise. At timestep t, the corrupted latent is given by:
2, V0.z + V1 04 E, E ~ IO,
where the noise schedule is encoded as o, = [T:=1(1 - B.), and B; the variance. The reverse process aims progressively recover the original latent through learned denoising network 24 46) typically implemented as a U-Net 43 structure LDMs. Standard diffusion models perform multi-step denoising via a Markov chain, where each reverse step is modeled as:
(z (z, M S,I),
with M4 derived from a neural noise estimator Eø (zc, t). In the one-step variant, single denoising pass directly estimates the clean latent 2n from a noisy input 2,:
20 2, / de Eo 1) /oa..
This formulation enables efficient reconstruction by collapsing the multi-step diffusion process into single inference step. By viewing quantized latent features as intermediate diffusion states, our model leverages this formulation to efficiently recover clean latent representations.
3.2 OSCAR Overview
An overview of our OSCAR is shown in Fig. and the corresponding pseudocode is provided in the Appendix. The training of OSCAR consists of two stages. the first stage, we train multiple bit-rate-specific hyper-encoders to align the quantized latent representations ă with the original latents

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i= -0,0 Zo. We empirically find that the cosine similarity between 2 and Zo stabilizes after this stage, allowing
us to construct mapping function (-) that bridges bit-rates and pseudo diffusion timesteps, where R is the predefined bit-rate In the second stage, we jointly fine-tune the SD U-Net and all hyper-encoders to improve reconstruction fidelity. Since our compression and decompression are entirely performed in the latent space, we freeze the parameters of the SD VAE during training.
At inference time, the OSCAR pipeline can be divided into two phases: compression and decom- pression. During compression, given an input image I and the desired bit-rate r E R, we first obtain its latent representation Zo via the frozen VAE encoder Eg. The latent is then quantized using a sit-rate-specific hyper-encoder 2s to produce the quantized latent representation 2:
Zo EI) ž = 2 (zo:r).
During decompression, we interpret 2 as the intermediate diffusion state. A pseudo diffusion timestep is assigned for each bit-rate, and the denoising network DNV a (i.e., the SD U-Net) restores fine-grained latent details to produce žu- The reconstructed latent is subsequently decoded by the VAE decoder to obtain the final image. We denote the full reconstruction appeline-including the hyper-encoder, denoising network, and decoder as a generator Go.0. The reconstructed latent features and image can thus be expressed as:
20 = DNo f (r),
(5)
3.3 Stage 1: Learning Quantization Hyper-Encoders
Bit-rate-specific hyper-encoders. While latent features benefit from low spatial resolution and are well-suited for compression, they still re-
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To address this, we apply hyper-encoders to quantize the latent representations. Given an input image. OSCAR first uses the Stable Dif- fusion (SD) VAE encoder to extract the latent representation zg. This step reduces spatial res- olution (e.g.. a x024x 1024 image yields a latent of shape 4x 128 x 128). To further compress Zo 1xxx we employ a lightweight hyper- encoder that downsamples and quantizes into Zq E xMMxh'xx xx', where M is the dimension of the quantization space. Specifically for the architecture, the front-end of the hyper-encoder consists of several residual blocks, followed by vector quantization (VQ) codebook. With down-
Orderes Values
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Figure 4: Distribution and QQ-plot of residual noise e Eq. (TT before and after representation alignment. Top: red line is the standard Gaussian distribution; blue histogram is the mcasured distri- bution. Bottom: red line is the theoretical quantile line; blue dots are the measured values.
sampling ratio and codebook size V, the resulting bit-rate is bpp log2 V/64s For decoding, the back-end module of the hyper-encoder upsamples Z, and projects it back to the original latent space, yielding the quantized latent feature ă E R4x The back-end module employs attention layers and additional residual blocks to enhance reconstruction quality. We provide the detailed architecture, along with the training and inference algorithms, in the Appendix.
Quantized latent representation alignment. The goal of the first training stage is to align the quan- tized latent features with the original latent representations. Since downsampling and quantization in the hyper-encoder inevitably discard fine-grained details, we instead focus on preserving structural information by aligning the direction of latent vectors. To this end, we train hyper-encoders using cosine similarity as the quantized latent feature alignment loss:
N
sim (Ø) = Egg
Σ n=1
sim (zo" (n) Co(26 (m) eoleb"))))
(6)
N
where N is the number of vectors in Zu and r is the desired compression bit-rate. As in VQ-VAE since vector-quantization (VQ) operation is non-differentiable, the VQ loss is defined as:
[I],
Lvo(Ø)= :- Ez. [|| sg(z)) zg + ||z|- Zg 112].
where sg is the stop-gradient operation, and 2g is the mapping of 2g to its nearest codebook entry.
Following the method in [17]. we adopt exponential moving average (EMA) update for the
5

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codebook, which enhances training stability and eliminates the need for the first term in Eq.
11
Thus, the overall representation alignment objective
Ciepa (0) = (o) + Lvo(o).
(8)
We argue that representation alignment is critical for modeling compressed latents as intermediate diffusion states (We will elaborate this in Section (.4). To support this, we compare the measured additive noise before and after training. As shown in Fig. 4 prior to training, the residual noise exhibits no clear structure and resembles a uniform distribution within the range -2.5 to 2.5. The corresponding quantile-uantile plots (QQ-plots) [56] deviate substantially from the theoretical Gaussian line. This indicates that the latents prior to alignment contain only unstructured noise. In contrast, after training, the residual noise closely follows Gaussian distribution, and the QQ-plots align well with the theoretical line, indicating that the residuals become statistically well-behaved.
3.4 Stage 2: Unified Training for One-Step Reconstruction
Bridging quantization and forward diffusion. Once the hyper-encoders have stabilized, we empirically observe that the cosine similarity between ă and 2n remains stable throughout the remainder of training (Please refer to Appendix). This allows us to establish an empirical mapping function between compression bit-rate and cosine similarity.
N
F
sim (r) = Ezzo
sim
(n) Zu
20
(n)
;r))
(9)
n=1
1
Σ ZanD
sim
Zo
(n)
2
Z m :r))
(10)
DIN
where D is the training set. We hypothesize that the quantized latent ž corresponds to a diffusion state along a trajectory, where each bit-rate can be associated with a specific diffusion timestep. Formally, posit that ž can be decomposed as:
ž Va + VI- c,
(11)
where ai E [0, 1] is the pseudo diffusion coefficient determined solely by the compression bit-rate, and é (0, I) is assumed be orthogonal to Zo. Under this assumption, the expected cosine similarity between ? and Zo is:
Gsim (t) == (e-A((o.)) [sim (Vārzo vI vI- ā. E, Vāg.
(12)
By matching the empirically measured cosine similarities this theoretical form, can infer the corresponding pseudo diffusion timestep / for each bit-rate r:
R (r) argmin .T (l F atm (r)1,
(13)
where T is the maximum diffusion step, R is the predefined bit-rate set. Figure provides strong empirical support for our modeling. With an appropriate pseudo diffusion timestep t.
é Vazzo) /VI ā, closely approximates the Gaussian distribution.
Unified fine-tuning across multiple bit-rates. Remarkably, even under highly compressed condi- tions, the cosine similarity between ž and Zg consistently stabilizes at a high level after the first-stage training (Please refer to Appendix). This indicates that the quantized latents still preserve rich structural information, making single-step reconstruction feasible. Leveraging the mapping func-
tion th (c). we assign each bit-rate a corresponding pscudo diffusion timestep, enabling shared diffusion backbone to support reconstruction across all bit-rates.
Specifically, given the predefined bit-rate set R, we fine-tune all the corresponding and apply LoRA [25] to update the diffusion model. The overall loss combines a representation alignment loss Ciepar a perceptual loss Lper. and an adversarial loss Le:
L(0,0) := E,-R [C) + Cpe 0) + Xo 0)]
(14)
The alignment loss Crepa preserves structural consistency between the quantized and original latents.
The perceptual loss Lper includes MSE and DISTS [16] terms. Following the practice of [15,
additionally use the Sobel operator to extract the edge of the image and measure the corresponding edge-aware DISTS loss:
(1) 0) EIND [E2(T, + CDISTS (I, + (EA.
(15)

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Figure Evaluation of OSCAR and other compression codecs on Kodak, DIV2K- Val, and CLIC2020.
where i = (zo) S()) is the Sobel operator. Finally, introduce diseriminator Dy and adopt GAN loss [3] in latent space to encourage realism of the reconstructed image:
Lg(0, 0) == E log D (2o)),
(b(b) = E [log(1 - D (2o))] - E [log (2o)),
(16)
where ăn = D. ( (2o: r), f (r)) is the reconstructed latent representation.
4 Experiments
4.1
Experimental Settings
Datasets and evaluation metrics. We curate the training data from DF2K, which combines 800 images from DIV2K (2), 2,650 from Flickr2K [48], and an additional subset from LSDIR [3).
resulting in 88,441 high-quality images in total. For evaluation,
benchmark OSCAR on three
standard datasets: Kodak (18] (24 natural images, 768 x x12), DIV2K-val (2 (100 images), and
CLIC2020 [49 (428 images), where all DIV2K-val and CLIC2020 images center-cropped to
1024x 1024. We adopt both full- and no-reference metrics, including LPIPS [60]. DISTS [16].
MUSIQ 27 and CLIPIQA 55 to comprehensively assess perceptual and subjective quality.
Implementation details. Our approach builds upon Stable Diffusion 2.1
42 which comprises
approximately 965.9M parameters. During the first training phase (Section 3.3 we optimize all
hyper-encoders in parallel using the Adam optimizer 288, with a fixed learning rate of 2 10-3 and a batch size of 64. Training is conducted for 10,000 iterations a single NVIDIA RTX A6000 GPU. The training converges stably, producing generalized representations across compression levels.
In the second stage, we train our model with the Adam W optimizer 35), setting the learning rate to 5 10-5, weight decay to 1 x 10-5 and batch size to 64 for both OSCAR and the discriminator. We apply LoRA 25 with rank of 16 for efficient fine-tuning. The discriminator follows the same training protocol as OSCAR. The perceptual loss weight A set to I, and the adversarial loss weight Ag is 50 This stage is trained for 150,000 iterations using eight NVIDIA RTX A6000 GPUs.
7

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Figure 6: Visual comparison across different bit-rates. HQ denotes the original high-quality patch. The number in parentheses indicates the hpp used for compression. OSCAR yields clean reconstructions while preserving intricate structural and textural details.
4.2 Main Results
Compared methods. We benchmark OSCAR against four representative categories of image codecs: 1) Traditional methods: BPG 7 and VVC [[]] which serve as hand-crafted baselines. 2) Learning-basee based single-rate codecs: HiFiC (37 uses conditional GANs to balance rate, distortion, and perceptual quality, while MS-ILLM
enhances local realism via spatially-awarr adversarial
training. 3) Learning-based multi-rate codecs: DPICT 31 and CTC (26) both adopt tri tri-plane
coding for flexible rate control. 4) Diffusion- codees: CDC (58) and DIftEIC (34] are optimized for the rate-distortion objective, while PerCo [12] adopts vector quantization. Both DiffEIC and
PerCo build upon the same pretrained Stable Diffusion 2.1 model as ours. We retrain PerCo using the open source implementation PerCo (SD) [29] Each requires separate model per bit-rate. For HiFiC and CDC, we additionally retrain them on our dataset at lower bit-rates.
Quantitative results. The quantitative comparisons on the Kodak, DIV2K-Val and CLIC2020 datasets are summarized in Fig. 5 OSCAR consistently outperforms all listed baselines across range of evaluation metrics and compression rates. Among multi-step diffusion methods, we include PerCo, which also employs a codebook quantization strategy similar to ours. Further comparisons with other multi-step basclines provided in the appendix. OSCAR's strong results perceptual metrics such as LPIPS and DISTS highlight its ability to preserve fine details and achieve high perceptual fidelity, while its performance on no-reference metries like CLIPIQA further confirms the visual quality of the reconstructions.
Discussion of the trend of RD-curves. The RD-curves of OSCAR are not strictly convex, as the bitrate is jointly determined by both the downsampling ratio of the hyper-encoder and the size of the codebook. When the bitrate changes, the effects introduced by these two factors may not vary consistently. Nevertheless, our method maintains smooth transitions between adjacent bitrates without noticeable performance drops, while still achieving strong results at low bitrates.

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Figure 7: Ablation study key components. RePA denotes representation alignment, and w/o t-mapping refers to using random mapping hetween bit-rates and time-steps.
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Results of generalization unseon bitrates. The
star denotes the newly adapted bitrate.
Figure 8: Ablation study RePA and generalization unseen bitrates.
Qualitative results. As shown in Fig. 6 traditional, learning-based, and Liffusion-basee codecs generally preserve the coarse structure of the image but suffer from noticeable quality degradation, such as blurring, texture loss, color shifts, and visible artifacts. For instance, wall textures often appear overly smooth, while fine structures such window frames, door edges, and decorative patterns are frequently distorted or lost. In contrast, OSCAR preserves such details with high fidelity, maintaining both material realism and structural consistency with the original image. Overall, it achieves a highly favorable rate-distortion trade-off, ensuring that compressed outputs retain both excellent perceptual quality and faithful structural integrity. We provide more results in the appendix.
4.3 Ablation Studies
Mapping bit-rates to pseudo diffusion timesteps. In our method, each compression bit-rate is associated with pseudo diffusion timestep through learned mapping function. To evaluate its effectiveness, we compare this design to a baseline that randomly assigns timesteps. As shown in Fig. removing the calibrated mapping leads to noticeable visual degradation degradatioon-reconsttee reconstructed images fail to preserve the original shape and structure. This degradation is further reflected in Fig. 7b which shows consistent performance drops across all evaluation metrics and bit-rates.
Representation alignment. validate the effectiveness of representation alignment, we compare the residual term in Eq. (1] with and without alignment. As shown in Fig. Sal the aligned model produces residuals that closely match the standard Gaussian, whereas the unaligned one shows large deviations. Cosine similarity between quantized and original latents is also notably higher with

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VAE
VVC
MS-ILLM
Poroo
IPIC
DDCM
DIEEI
OSCAR (curs)
T)
1)
CLICZ020 (FID
27.3
25.0
22.5
23.0
200
11.
22
13.0
Bis poxel
Bits per pixel
Figure 9: PSNR and FID results on Kodak, DIV2K- val, and CLIC2020 datasets. The ycllow dashed line indicates reconstruction by VAE encoding and decoding without compression.
alignment training. Figure 7b further demonstrates the effectiveness alignment through both visual and quantitative comparisons, showing that removing it leads to substantial performance degradation.
OSCAR training loss functions. OSCAR incorporates multiple loss functions during training to enhance performance. To assess the impact of key components, we perform ablation studies on the edge-aware DISTS loss and GAN loss. As shown in Fig. 7a removing either loss leads noticeable reduction in visual detail. Figure 7b further shows performance degradation, particularly on non-reference metrics, indicating their importance for perceptual quality.
Generalization to unseen bitrates. For each predefined bitrate, OSCAR employs an individual hyper-encoder for compression, resulting in set of fixed-rate models. Nevertheless, demonstrate that OSCAR exhibits strong generalization capability to unseen bitrates. Specifically, for a new bitrate, we continue training for 15k iterations under the same settings before, except that in each iteration, the bitrate is sampled from the new one with a probability of 0.5 and from the existing set with probability of 0.5. As shown in Fig. 8b the new bitrate can be effectively learned with only minor performance degradation on the previously trained bitrates.
PSNR and FID results. We present the PSNR and FID performance of state-of-te tite-offthheart codecs in Fig. 9 In addition to the compared baselines, we further include IPIC [57] and DDCM [40], whose results PSNR and FID are highly competitive. For FID evaluation, report results only on the CLIC2020 dataset, as the other two datasets contain too few images for statistically stable estimation.
Diffusion-based codecs generally underperform traditional methods in terms of PSNR. This is mainly due to the inherent limitation of VAEs, which struggle to achieve pixel-wise accurate reconstruction. As indicated by the yellow dashed line in the figure, the reconstruction from VAE (without compression) can even yield lower PSNR than traditional codecs at higher bitrates. Despite these challenges, OSCAR demonstrates competitive performance among multi-step diffusion- codecs.
5 Conclusion
We propose OSCAR, one-step diffusion codec that supports compression across multiple bit-rates within a unified network. By modeling quantized latents intermediate diffusion states and mapping bit-rates to pseudo diffusion timesteps, OSCAR achieves efficient angle-pass reconstruction through a shared generative backbone. This work establishes a novel pathway for efficient, practical, and high-fidelity diffusion--ased image compression in real-world applications. Extensive experiments demonstrate the superiority of OSCAR over recent leading methods.
Acknowledgments
This work was supported by Shanghai Municipal Science and Technology Major Project (202ISHZDZ00102) and the Fundamental Research Funds for the Central Universities.
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Reviews:
[{"review_id": "qW3pDEVbbV", "reviewer": "Reviewer_cDUu", "summary": "This paper proposes OSCAR, a method for restoring images from latent compressed at different bitrates by constructing a mapping from compression bitrate to pseudo diffusion denoising timesteps, enabling a single-step reconstruction using one unified diffusion model.", "questions": "1.\tAs mentioned in the weaknesses regarding generalization to continuous bitrates, could the authors provide constructive insights or perspectives on how this method could be extended to support continuous bitrate adaptation in future work?\n2.\tThe authors mention that a dedicated hyper-encoder is trained for each bitrate. Although the parameter count for each is relatively small, this design choice appears somewhat ad hoc. Would it be possible to allow different hyper-encoders to share modules? If so, would such sharing significantly affect the effectiveness of the proposed method?", "limitations": "N/A", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.\tThis paper adopts a novel and insightful perspective by constructing a mapping network from bitrate to pseudo-timestep, proposing an efficient one-step diffusion reconstruction method. This approach significantly accelerates the decoding process and enhances cross-bitrate generalization, thereby greatly reducing training and deployment costs. The methodology is supported by a rigorous and well-structured theoretical analysis. \n2.\tThe proposed staged training pipeline and the design of the loss functions are clearly motivated and well-aligned with the task objectives. Extensive experiments demonstrate that OSCAR outperforms existing methods across multiple datasets, including Kodak, DIV2K-val, and CLIC2020, achieving superior performance in terms of computational efficiency, structural fidelity, and perceptual quality. \n\nWeakness:\n\n1.\tThe core idea of this work relies on a key assumption: that at high bitrates, the quantization error of the latent representations can be approximated as additive Gaussian noise. However, this assumption does not hold at low bitrates. Although the paper introduces optimized training for representation alignment across various bitrates, these bitrates are still pre-defined and discrete. The generalization ability of the model to unseen or non-predefined bitrates is not discussed. \n2.\tThe design perspective adopted by the authors is highly insightful. However, although a fitting is performed between the cosine similarity and the diffusion timestep, the resulting mapping function is essentially based on empirical regression, lacking explicit theoretical constraints and cross-model generalization analysis. This absence of theoretical grounding may ultimately limit the method’s ability to generalize across continuously varying compression bitrates.\n3.\tIn this work, a separate hyper-encoder is trained for each quantization bitrate, which limits the method’s ability to generalize to continuous or finer-grained bitrate settings.", "was_revised": true, "final_justification": "My concerns and questions were answered.", "rebuttal": "Thank you for your valuable comments and insightful suggestions, as well as for acknowledging our novelty, motivation, and convincing evaluations. Below, we respond to your comments.\n\n- Q1: The authors should discuss the generalization ability to unseen or non-predefined bitrates.\n    \n    Thanks for the suggestion. Since our method employs a learnable hyper-encoder for compression, it does not support unseen bitrates out of the box. However, we conducted additional experiments to demonstrate OSCAR’s ability to quickly adapt to new bitrates with minimal computational overhead. We adopted a simple training strategy: half of the steps are allocated to the new bitrate, and half to the original groups. Extending to a new bitrate takes only 15K steps—under 4 hours on 4×A6000 GPUs. We attribute this efficiency to the fact that the diffusion model has already encountered structurally similar latent distributions during initial training, enabling fast and stable adaptation to nearby bitrates. \n    \n    The tables below report results on the DIV2K-val dataset, where we progressively adapt the model to new bitrates one at a time. Numbers in parentheses indicate the performance change compared to the model before adaptation to the new bitrate.\n    \n    | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- |\n    | 0.0313 | 0.1262 | 0.3883 | 0.7210 |\n    | 0.0507 | 0.1102 | 0.3487 | 0.7623 |\n    | 0.0937 | 0.0694 | 0.2385 | 0.8713 |\n    | 0.1250 | 0.0617 | 0.2157 | 0.8906 |\n    \n    | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- |\n    | 0.0313 | 0.1306 (↑3.49%) | 0.3927 (↑1.13%) | 0.7161 (↓0.68%) |\n    | 0.0507 | 0.1141 (↑3.54%) | 0.3523 (↑1.04%) | 0.7578 (↓0.59%) |\n    | 0.0937 | 0.0728 (↑4.90%) | 0.2427 (↑1.77%) | 0.8595 (↓1.35%) |\n    | 0.1250 | 0.0648 (↑4.93%) | 0.2198 (↑1.91%) | 0.8766 (↓1.57%) |\n    | **0.0430 (new)** | 0.1198 | 0.3670 | 0.7402 |\n    \n    | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- |\n    | 0.0313 | 0.1360 (↑4.19%) | 0.4006 (↑2.03%) | 0.7031 (↓1.81%) |\n    | 0.0507 | 0.1185 (↑3.87%) | 0.3589 (↑1.87%) | 0.7459 (↓1.57%) |\n    | 0.0937 | 0.0752 (↑3.34%) | 0.2457 (↑1.25%) | 0.8506 (↓1.03%) |\n    | 0.1250 | 0.0672 (↑3.75%) | 0.2228 (↑1.38%) | 0.8664 (↓1.16%) |\n    | 0.0430 | 0.1246 (↑4.01%) | 0.3742 (↑1.98%) | 0.7282 (↓1.62%) |\n    | **0.0781 (new)** | 0.0918 | 0.2766 | 0.8412 |\n- Q2: The assumption of the method might not hold at low bitrates.\n    \n    We would like to clarify that although the cosine similarity between quantized and clean latents naturally decreases at lower bitrates, the assumption that the quantization error follows additive Gaussian noise still holds, as long as the cosine similarity remains stable during the second-stage training.\n    \n    To further address your concern, we retrained OSCAR from scratch to include two additional lower bitrates using the same experimental settings, except that we increased the training iterations to 150k. The results show that our method remains effective at these lower rates. We also visualize the quantization error using the same method as in Figure 4, and the distribution continues to closely follow a Gaussian. These results will be included in the revised manuscript.\n    \n    The tables below report results on the DIV2K-val dataset. Numbers in parentheses indicate the performance change relative to our original model.\n    \n    | models | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- | --- |\n    | OSCAR | 0.0313 | 0.1274 (↑0.95%) | 0.3866 (↓0.44%) | 0.7234 (0.33%↑) |\n    | OSCAR | 0.0507 | 0.1098 (↓0.36%) | 0.3479 (↓0.23%) | 0.7644 (0.28%↑) |\n    | OSCAR | 0.0937 | 0.0702 (↑1.15%) | 0.2398 (↑0.55%) | 0.8694 (↓0.22%) |\n    | OSCAR | 0.1250 | 0.0630 (↑0.96%) | 0.2170 (↑0.60%) | 0.8881 (↓0.28%) |\n    | OSCAR | **0.0234 (new)** | 0.1494 | 0.4265 | 0.6993 |\n    | PerCo (SD) | 0.0234 | 0.2359 | 0.4433 | 0.6472 |\n    | OSCAR | **0.0098 (new)** | 0.1820 | 0.4628 | 0.6556 |\n    | PerCo (SD) | 0.0098 | 0.2870 | 0.4841 | 0.5889 |\n- Q3: Authors should provide more theoretical and cross-model generalization analysis.\n    \n    Thanks for the valuable advice. We agree that OSCAR is based on an empirical assumption, which we validate through extensive experiments, rather than deriving from a strictly theoretical framework. While establishing a formal theoretical mapping is certainly an interesting direction, it remains challenging and still has a long way to go. In this context, we propose an alignment-based approach, where we empirically model the quantization error as Gaussian noise, based on the observed cosine similarity between the quantized and original latents. This assumption is strongly supported by our experiments. As shown in Figure 4, the quantization error closely follows a Gaussian distribution. We will include additional visualizations of quantization error at different bitrates in our paper. We believe that our perspective, supported by empirical findings, will provide valuable insights for future research.\n    \n    To assess cross-model generalization, we replaced the UNet-based SD-2.1 with the Transformer-based SD-3 Medium and conducted experiments accordingly. We report the results on the DIV2K-val dataset. The results show that our method remains effective under this different backbone, demonstrating cross-model generalization. \n    \n    | models | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- | --- |\n    | OSCAR (UNet) | 0.0313 | 0.1262 | 0.3883 | 0.7210 |\n    | OSCAR (DiT) | 0.0313 | 0.1076 | 0.3579 | 0.7532 |\n    | OSCAR (UNet) | 0.0937 | 0.0694 | 0.2385 | 0.8713 |\n    | OSCAR (DiT) | 0.0937 | 0.0657 | 0.2124 | 0.9003 |\n- Q4: One hyper-encoder is trained for each bitrate, which might limit the generalization to fine-grained bitrate settings.\n    \n    We agree that generalizing to fine-grained bitrate settings remains a challenge, especially when aiming for both flexibility and high performance. However, as shown in Appendix Figure 1, despite using separate hyper-encoders, the quantized latents of nearby bitrates remain highly similar, mirroring the behavior of variable-bitrate methods. We believe this latent consistency suggests that OSCAR has the potential for future improvements.\n    \n- Q5: Would it be possible to allow different hyper-encoders to share modules?\n    \n    We would like to clarify that each hyper-encoder is tied to a specific bitrate because each uses different internal settings, like the downsampling ratio and codebook size, to control the compression level. The module requires no complex design and is very lightweight ($\\approx$0.5% of the parameters compared to the diffusion backbone). We found that having a separate lightweight hyper-encoder per bitrate works well and keeps things simple. Nevertheless, an interesting future direction is to share a single hyper-encoder across bitrates that use the same downsampling ratio but differ in codebook size. This would allow multiple bitrates to be paired with one hyper-encoder.\n    \n- Q6: How can this method be extended to continuous bitrate adaptation in future work?\n    \n    Thank you very much for the inspiring question. One important current limitation lies in the total training budget. Under constrained resources (e.g., 4×A6000 GPUs), it is challenging for a single model to fully optimize across many bitrates without sacrificing performance. A straightforward solution is to scale up training, e.g., using larger batch sizes and more iterations to allow the model to better generalize across bitrate levels.\n    \n    Furthermore, our results (Appendix Figure 1) show that quantization errors tend to be similar when the downsampling ratios are the same. This suggests a promising future direction: we could train a model on several anchor bitrates with different downsampling ratios, and then generalize to additional bitrate levels, which share the same downsampling ratio as the anchor bitrate but with different codebook sizes. In fact, as discussed in Q1, our simple training strategy already allows fast adaptation to new bitrates with minimal cost, leveraging well-trained anchor bitrates. We believe that designing efficient and scalable mechanisms for bitrate extension is a very promising direction.\n    \n\n---\n\nWe hope this response can help address your concerns. We believe our work can have an important impact on the field of image compression by offering a new perspective on leveraging pre-trained diffusion models for efficient one-step reconstruction across multiple bitrates. If you find our response satisfactory, we would be sincerely grateful if you could consider a higher score, as it would greatly support our efforts. Thank you once again for your consideration."}, {"review_id": "T1li9ds940", "reviewer": "Reviewer_2faj", "summary": "This paper proposes to use latent diffusion models for image compression. First, a set of hyper-encoders are trained to quantize the latent representation of any given image, where each hyper-encoder corresponds to a pre-defined compression bit-rate. The resulting quantized representation of an image is treated as its noisy version, in the form of the forward diffusion equation. The diffusion model is then fine-tuned with LoRA to take any such representation and \"denoise\" it in one-step, such that the resulting output minimizes a loss comprised of several similarity measure to the ground-truth non-quantized image representation. The input time-step to the diffusion model is learned from the bit-rate. Namely, a learned mapping takes the given bit-rate and outputs the corresponding ideal time-step, essentially translating lower bit-rates to larger time-steps (the representation is more \"noisy\" for lower bit-rates). The resulting compression algorithm demonstrates good rate-perception-distortion tradeoffs, outperforming previous methods while doing so only with one-step denoising at inference time.", "questions": "I have no questions for the authors.", "limitations": "No limitations are discussed in the main text.\n\nHere are some of the limitations I noticed:\n1. The mathematical motivation for the proposed approach is not well-explained in the paper. Yet the results are convincing.\n2. The method relies heavily on a large pre-trained diffusion model, thus requiring a large memory footprint.\n3. The ablation study is limited. I would personally be curious to see a toy experiment comparing the proposed approach to end-to-end neural compression, seeing whether the use of a pre-trained denoiser is a key ingredient in the success of the method.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper is overall well written, highly detailed, and provides comprehensive and convincing experiments and results. Compared to previous diffusion-based compression methods, the proposed algorithm only requires one-step denoising during inference, making the proposed approach much faster (as depicted in Fig. 2). However, the proposed algorithm still relies on a heavy latent-based diffusion model that contains many parameters, making the algorithm still not that practical for edge devices. Though, this is not to say that the gained speed-up compared to previous diffusion models is not an important step to take.\n\nThe biggest weakness in my opinion is the loose mathematical explanation that motivates the proposed method. The authors say that they \"bridge\" quantization and forward diffusion, yet this is explained only briefly in the paper and in very loose terms and claims (see e.g. L49). While the results are convincing and promising, it would be better if the authors discuss this idea in the main text in more detail.\n\nAnother weakness is that it's unclear what is the true role of diffusion here. There is no diffusion involved in the proposed method, but rather only the large pre-trained denoiser - which is fine-tuned. This raises the question whether the \"diffusion\" idea behind the proposed method is even significant in the first place - and I believe that it is not. The proposed approach can be considered an end-to-end neural compression method that uses vector quantization in the middle, with particular inductive biases enforced along the way. This raises the important question whether the method works well simply due to such inductive biases, but this is not explored in the paper.\n\nLastly, the main compared competing method is PerCo. However, to the best of my knowledge, there are other diffusion-based compression methods that outperform PerCo, such as [1] and [2]. I suggest that the authors add more comparisons with recent methods to their manuscript.\n\n[1] Xu et al. \"Idempotence and Perceptual Image Compression.\" In ICLR 2024.\n\n[2] Ohayon et al. \"Compressed Image Generation with Denoising Diffusion Codebook Models.\" In ICML 2025.", "was_revised": true, "final_justification": "The authors addressed most of my concerns and I think this paper is worthy of publication. The authors should add additional comparisons with recent methods, as discussed. This is especially important since the proposed method is not theoretically grounded and based only on evidence. It may be interesting to investigate in the future the reasons behind the effectiveness of the approach, but for the time being, this is a borderline paper for me.", "rebuttal": "Thanks for your valuable comment, nice suggestions, and for acknowledging our writing and empirical performance. Your questions and suggestions are instrumental in further strengthening our paper. Below, we respond to your specific comments.\n\n- Q1: The mathematical explanation and the motivation could be discussed in more detail.\n    \n    Thank you for this insightful comment. We acknowledge that the core of OSCAR is based on an empirical assumption. While this assumption is not derived from a rigorous mathematical formulation, it is strongly supported by experimental evidence.\n    \n   **The motivation behind OSCAR is as follows:** We begin by recalling the standard formulation of the forward diffusion process: $z_t=\\sqrt{\\alpha_t} z_0+\\sqrt{1-\\alpha_t}\\epsilon$, where  $\\epsilon \\sim \\mathcal{N}(0,1)$. Since we aim to use the diffusion formulation to reconstruct the clean latent $z_0$ from the quantized latent $\\tilde{z}$, we naturally ask the question, can $\\tilde{z}$ also be expressed in the form $\\tilde{z}=\\sqrt{\\alpha} z_0+\\sqrt{1-\\alpha}\\epsilon$? We test different values of $\\alpha$ and visualize distribution of the quantization error, computed as $\\epsilon=(\\tilde{z}-\\sqrt{\\alpha} z_0)/ (\\sqrt{1-\\alpha})$. Interestingly, we find that after the first-stage alignment, it is possible to select a suitable $\\alpha$ such that the resulting quantization error closely approximates a Gaussian distribution, as illustrated in Figure 4. For a fixed bitrate, we find that the selected $\\alpha$ values are similar across different images. This motivates us to statistically determine the most appropriate $\\alpha$ for each bitrate. Specifically, we compute the average cosine similarity across samples in our training set and select the pseudo timestep $t$ whose theoretical cosine similarity $\\sqrt{\\bar{\\alpha}_t}$ best matches the observed value. This mapping procedure ensures a stable and interpretable assignment of bitrates to diffusion timesteps.\n    \n    In the introduction section L45–59, we outline the high-level motivation. In Section 3.4, we provide detailed formulations of how we map the bitrates to pseudo diffusion timesteps. Appendix C provides a mathematical justification for the validity of using cosine similarity to establish the mapping. We will consolidate these discussions in the main text to provide a more coherent explanation.\n    \n- Q2: Whether the method's effectiveness stems from inductive biases rather than the use of diffusion.\n    \n    We would like to clarify the role of diffusion in our method. In Section 3.1, we provide the mathematical formulation of one-step diffusion. The only adaptation we introduce in OSCAR is treating the quantized latent as the noisy input to the denoising process—no changes are made to the core diffusion architecture or its operations. This design remains fully consistent with the one-step diffusion paradigm. \n    \n    To demonstrate the role of diffusion, we removed the time module in the diffusion backbone and let the diffusion backbone predict the clean latent directly (thus making the backbone a pure denoiser). We trained this denoiser with the same experimental setting as OSCAR. Below, we report the performance on the DIV2K-val dataset.\n    \n    | models | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- | --- |\n    | OSCAR | 0.0313 | 0.1262 | 0.3883 | 0.7210 |\n    | Denoiser | 0.0313 | 0.1687 | 0.4259 | 0.6761 |\n    | OSCAR | 0.0507 | 0.1102 | 0.3487 | 0.7623 |\n    | Denoiser | 0.0507 | 0.1479 | 0.3726 | 0.7254 |\n    | OSCAR | 0.0937 | 0.0694 | 0.2385 | 0.8713 |\n    | Denoiser | 0.0937 | 0.0933 | 0.2697 | 0.8224 |\n    | OSCAR | 0.1250 | 0.0617 | 0.2157 | 0.8906 |\n    | Denoiser | 0.1250 | 0.0887 | 0.2514 | 0.8504 |\n    \n    The diffusion formulation shows a clear advantage over the denoiser. We attribute this to our design strictly following the one-step diffusion framework, thus allowing us to fully leverage pre-trained diffusion backbones. \n    \n- Q3: Comparison with more recent baselines.\n    \n    Thank you for the advice. We will supplement these comparisons in our paper, as well as a discussion of similar works in the related work section.\n    \n    1. Comparison with IPIC. \n        \n        The table below presents results on the Kodak dataset.\n        \n        | models | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n        | --- | --- | --- | --- | --- |\n        | IPIC (s=1000) | 0.0721 | 0.1674 | 0.3885 | 0.8221 |\n        | OSCAR (s=1) | 0.0507 | 0.1156 | 0.3360 | 0.7610 |\n        | IPIC (s=1000) | 0.1200 | 0.1640 | 0.2811 | 0.8447 |\n        | OSCAR (s=1) | 0.1250 | 0.0766 | 0.2340 | 0.8585 |\n        \n        The results show that OSCAR performs better than IPIC on DISTS and LPIPS, while IPIC shows better MS-SSIM at low bitrates. This could be attributed to the generative nature of the diffusion backbone, which prioritizes perceptual quality over pixel-wise reconstruction.\n        \n    2. Comparison with DDCM.\n        \n        In our method, we use LPIPS-AlexNet as a training loss, and LPIPS-VGG as the evaluation metric to ensure a fair comparison. Here we follow DDCM to use LPIPS-AlexNet (denoted as LPIPS*), PSNR, and FID for evaluation, and use the DIV2K-val dataset with images cropped to 768$\\times$768.\n        \n        | model | bpp | PSNR↑ | LPIPS*↓ | FID↓ |\n        | --- | --- | --- | --- | --- |\n        | DDCM (s=1000) | 0.0420 | 21.1670 | 0.2010 | 18.3300 |\n        | OSCAR (s=1) | 0.0313 | 21.3304 | 0.1958 | 31.8673 |\n        | DDCM (s=1000) | 0.0660 | 21.7800 | 0.1720 | 16.3960 |\n        | OSCAR (s=1) | 0.0507 | 21.9478 | 0.1554 | 28.9917 |\n        | DDCM (s=1000) | 0.1370 | 22.7070 | 0.1380 | 14.6620 |\n        | OSCAR (s=1) | 0.1250 | 23.4529 | 0.1097 | 15.7407 |\n        \n        Compared to the 1000-step DDCM, our one-step OSCAR performs better on reference-based metrics (PSNR and LPIPS*), suggesting higher fidelity to the original images. While DDCM achieves lower FID scores, the gap narrows as the bitrate increases.\n        \n- Q4: The method relies heavily on a large pre-trained diffusion model.\n    \n    We appreciate your acknowledgment that, despite the reliance on a pre-trained diffusion model, our one-step diffusion design takes an important step to reduce the time burden. Here we would like to supplement that leveraging pre-trained diffusion models has become a common practice in recent works—PerCo, DiffEIC, and DDCM all rely on them. Compared to these multi-step diffusion codecs, our one-step approach not only greatly reduces computational overhead but also achieves competitive or even better performance.\n    \n\n---\n\nWe hope this response can help address your concerns. We believe our work can have an important impact on the field of image compression by offering a new perspective on leveraging pre-trained diffusion models for efficient one-step reconstruction across multiple bitrates. If you find our response satisfactory, we would be sincerely grateful if you could consider a higher score, as it would greatly support our efforts. Thank you once again for your consideration."}, {"review_id": "jLEwZxIQu7", "reviewer": "Reviewer_nsjq", "summary": "This paper proposed a one-step diffusion codec across multiple bit-rates called OSCAR. It views compressed latents as noisy variants of the original latents and  the level of distortion depends on the bit-rate. This enables the model to support reconstructions at multiple bit-rates. The compressed latents retain rich structural information, thus making one-step diffusion feasible and improve the inference efficiency.", "questions": "- In Figure 6, the paper presents visual comparisons, but the results of DiffEIC are not included. Could you provide visual comparisons with DiffEIC to facilitate a more comprehensive evaluation?\n- While the diffusion-based method can generate fine details, it may lead to severe discrepancies from the original image. Do the proposed method show better fidelity than existing diffusion-based method (e.g. DiffEIC)?\n- Some existing methods typically introduce quantization parameters to control the bitrate, which is both simple and practical in applications. In contrast, the proposed method introduces a mapping between bitrates and pseudo diffusion timesteps to enable variable bit-rate coding. Could the authors elaborate on the advantages of this design choice? Additionally, is there a reason for not combining this approach with existing methods to leverage the strengths of both?\n\n- The complexity comparison in Figure 2. Could you provide the number of the MACs and running time of OSCAR, MS-ILLM and DiffEIC for better comparion? Moreover, both encoding and decoding time should be provided.\n- The RD-curves presented in Figure 7 appear somewhat unusual, as they do not exhibit the typical convex or concave shapes commonly observed in rate-distortion analysis (depending on the metric used). Furthermore, disabling certain key components leads to significantly different trends in the curves, which raises questions about the consistency of the experimental setup. The authors are encouraged to double-check their experimental results and provide additional analysis.\n- The proposed method supports variable bit-rate coding by views compressed latents as noisy variants of the original latents. What is the range of bitrates that the proposed model can effectively support? It would be helpful if the authors could clarify the bitrate scalability of the model. Can the model still work well at extremely low-bitrates?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n- The proposed model use one-step diffusion for compression, leading to improved inference efficiency compared to existing methods.\n- The proposed method supports variable bit-rate coding by views compressed latents as noisy variants of the original latents.\n\n  \nWeaknesses:\n- Additional experiments and analysis are expected to improve this paper, see the Questions part for more details.\n- Visual comparison with DiffEIC in Figure 6 should be included and analysis about the RD-curves presented in Figure 7 should be provided. See **Questions** for more details.", "was_revised": true, "final_justification": "Considering that the authors have addressed my previous concerns well and this paper is one of the early works that introduce one-step diffusion into neural image compression, I raise my score to 4: Borderline accept.", "rebuttal": "Thanks for the valuable comments, constructive advice, and recognition of our idea and empirical results. Below we respond to your specific comments.\n\n- Q1: Visual comparisons with DiffEIC.\n    \n    Thank you for the suggestion. We have prepared the visual comparisons with DiffEIC. However, as additional PDFs are not permitted in rebuttal this year, we will include these visual results in the revised version of our paper. \n    \n- Q2: Does the proposed method show better fidelity than the existing diffusion-based method?\n    \n    Pre-trained diffusion models inherently prioritize perceptual quality over exact pixel-wise reconstruction due to their strong generative priors. As a result, diffusion codecs often produce visually appealing images at the cost of pixel-level fidelity. Since OSCAR also builds upon a diffusion backbone, this trade-off naturally applies. However, despite supporting multiple bitrates and requiring only a single denoising step, OSCAR achieves performance competitive with DiffEIC and significantly surpasses PerCo in both perceptual and pixel-level fidelity.\n    \n    The table below reports results on the DIV2K-val dataset:\n    \n    | models | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- | --- |\n    | PerCo (s=20) | 0.0507 | 0.1647 | 0.3646 | 0.6883 |\n    | DiffEIC (s=50) | 0.0422 | 0.1346 | 0.3501 | 0.7586 |\n    | OSCAR (s=1) | 0.0507 | 0.1102 | 0.3487 | 0.7623 |\n    | PerCo (s=20) | 0.0937 | 0.0993 | 0.2769 | 0.8081 |\n    | DiffEIC (s=50) | 0.0945 | 0.0687 | 0.2275 | 0.8838 |\n    | OSCAR (s=1) | 0.0937 | 0.0694 | 0.2285 | 0.8773 |\n- Q3: Could the authors elaborate on the advantages of mapping bitrates to diffusion timesteps?\n    \n    The core idea of our approach is that for each bitrate, the quantization error can be well approximated as Gaussian noise at a specific diffusion timestep. By assigning the most appropriate timestep to each bitrate, OSCAR can more effectively leverage a pre-trained diffusion model for image compression. This design is validated by our extensive experiments, showing that OSCAR can support multiple bitrates using only one-step diffusion while achieving competitive or even superior performance compared to existing multi-step diffusion models that operate at a single bitrate.\n    \n    As for the bitrate control, most existing variable-bitrate methods rely on hyperparameters such as quality factor (e.g., BPG and VVC) and compression level (e.g., CTC). This typically requires users to tune these parameters to match a target bitrate. In contrast, our method allows users to specify the bitrate explicitly, thereby offering a more user-friendly interface for bitrate control.\n    \n- Q4: Can we combine this approach with existing variable-bitrate coding?\n    \n    As discussed in Q3, our bitrate-to-timestep mapping is specifically designed to model the quantized latent as a diffusion state, enabling better utilization of the pre-trained diffusion model for image compression. This design is fundamentally different from existing variable-bitrate methods that rely on quantization parameters to control compression levels. Therefore, these methods cannot be simply combined.\n    \n- Q5: The numerical value of the MACs and running time of the leading baselines should be provided.\n    \n    Thank you for the suggestion. We will include the number of MACs and the runtime (covering both encoding and decoding) in our paper.\n    \n    The results below are measured on images of size 1024$\\times$1024.\n    \n    | models | Encoding (ms) | Decoding (ms) | Total (ms) | MACs (T) |\n    | --- | --- | --- | --- | --- |\n    | MS-ILLM | 208.77 | 314.72 | 523.49 | 1.05 |\n    | DiffEIC | 478.70 | 21821.79 | 22300.49 | 304.87 |\n    | OSCAR | 239.48 | 622.26 | 861.74 | 8.54 |\n- Q6: Additional analysis on the RD-curves presented in Figure 7.\n    \n    We agree that RD-curves typically exhibit convex or concave trends. However, in our method, the bitrate is jointly determined by the downsampling ratio and codebook size. Specifically, 0.0937 and 0.1250 share the same downsampling ratio, as do 0.0313 and 0.0507. Bitrates with the same downsampling factor tend to show more similar distortion behaviors, leading to the observed patterns in our RD-curve. Appendix Figure 1 further illustrates this, bitrates with the same downsampling ratio also show similar cosine similarity. A similar trend is also observed in PerCo (Fig. 5(a)), which also uses a similar codebook-based design.\n    \n    RD-curves typically measure the distortion between reconstructed and original images. For no-reference IQA metrics such as MUSIQ and CLIP-IQA, the usual convex or concave trends may not apply, as these metrics are influenced by perceptual quality beyond simple image similarity. As a result, removing key modules may alter the curve’s shape more significantly. Therefore, while these metrics are valuable for comparing overall perceptual quality, they may be less suitable for analyzing RD curve trends.\n    \n    We confirm the consistency of our experimental setup and will release all code and models to ensure reproducibility.\n    \n- Q7: The authors should clarify the bitrate scalability of the model.\n    \n    Thank you for the helpful comment. We retrained OSCAR to include two lower bitrates and evaluated its performance in the extremely low-bitrate setting. The results show that OSCAR achieves competitive performance down to 0.0098 bpp, demonstrating its scalability across a broad bitrate range (0.0098 to 0.1250). These results will be included in the revised manuscript.\n    \n    The table below shows results on the DIV2K-val dataset. Numbers in parentheses indicate relative change compared to the original version of OSCAR.\n    \n    | models | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- | --- |\n    | OSCAR | 0.0313 | 0.1274 (↑0.95%) | 0.3866 (↓0.44%) | 0.7234 (0.33%↑) |\n    | OSCAR | 0.0507 | 0.1098 (↓0.36%) | 0.3479 (↓0.23%) | 0.7644 (0.28%↑) |\n    | OSCAR | 0.0937 | 0.0702 (↑1.15%) | 0.2398 (↑0.55%) | 0.8694 (↓0.22%) |\n    | OSCAR | 0.1250 | 0.0630 (↑0.96%) | 0.2170 (↑0.60%) | 0.8881 (↓0.28%) |\n    | OSCAR | **0.0234 (new)** | 0.1494 | 0.4265 | 0.6993 |\n    | PerCo (SD) | 0.0234 | 0.2359 | 0.4433 | 0.6472 |\n    | OSCAR | **0.0098 (new)** | 0.1820 | 0.4628 | 0.6556 |\n    | PerCo (SD) | 0.0098 | 0.2870 | 0.4841 | 0.5889 |\n\n---\n\nWe hope this response can help address your concerns. We believe our work can have an important impact on the field of image compression by offering a new perspective on leveraging pre-trained diffusion models for efficient one-step reconstruction across multiple bitrates. If you find our response satisfactory, we would be sincerely grateful if you could consider a higher score, as it would greatly support our efforts. Thank you once again for your consideration."}, {"review_id": "Pc9oBAiGLS", "reviewer": "Reviewer_7kzL", "summary": "The paper introduces OSCAR, a one-step diffusion-based compression method that supports multiple bit-rates using a single generative model. This is different from previous diffusion based works that require iterative denoising, and usually need multiple models for multiple timesteps. They do this by basically changing the forward process to be the compression (quantized latent vectors), such that the reverse process yield decompression. This is quite different from previous diffusion-based approaches that leverage the denoising prior directly. The qualitative results, while lacking, show promising performance.", "questions": "- How many parameters are added by the rank 16 LoRAs?\n- If PerCo (SD) was used, they release just 3 checkpoints, 0.1250bpp, 0.0313bpp and 0.0019bpp, yet the authors include also for 0.051bpp and 0.094bpp (Fig. 6). Did the authors finetune those themselves? If so, this should be noted.", "limitations": "Somewhat. The authors note in the supplementary how the method supports only a small amount of bit-rates at once, yet they do not relate how the pre-determined choice of bitrates requires retraining the entire model when support should be added for a single new bitrate. Additionally, they do not discuss how the latent-space nature of the method hurts pixel-space based distortion.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 2, "significance": 4, "originality": 4, "strengths_and_weaknesses": "## Strengths\n- The proposed idea of changing the “degradation” to be the compression is an interesting and novel idea. Specifically, the choice of a quantization degradation which can be modeled as additive noise is elegant.\n- Single model for multiple bitrates.\n- One-step sampling is great and efficient.\n- The ablation studies are helpful and support the authors’ claims.\n- The algorithms provided in the appendix are helpful.\n## Weaknesses\n- The authors **played with the template’s caption, section and paragraph spacings** in such a way that left the reading very confusing to this reviewer at times. For example, the captions of Fig. 1 and 8 merged with the content paragraph and made for an uneasy reading. Sec 4.2 was squeezed such that paragraphs had no room to breathe. This is quite problematic – as it is not fair towards other papers that did shorten their paper to fit neatly into the page limit. \n- **Missing (highly) related works**:\n  - L32 – the original paper of Sohl-Dickstein 2015 should be cited.\n  - A very relevant missing work is the DDCM compression approach [1], which uses the entire diffusion process for compression and complementary exact restoration. It shares similarity with the current paper, since they also use the gaussian noise to represent the intermediate compression vectors, which can be seen as using the original DDPM degradation directly – in a zero-shot manner – to provide similar effects. As a result, they also do not need multiple models for different bitrates (L40). This should be cited and compared to accordingly. While it suffers from the same multi-sampling problem, it could also serve as a non-bitrate-specific diffusion-based baseline which the paper is missing.\n  - Another missing related work is with regards to diffusion models that change the forward model. For example, Cold Diffusion [2] propose a pixelated degradation version that is conceptually similar to what is proposed here and should be explicitly compared to.\n- The paper includes multiple comparisons to PerCo. However, their internal model was not released. Did the authors train their own PerCo version, or used the open-sourced version, \"PerCo (SD)\"? If the latter, the corresponding paper [3] should be cited. \n- **Missing important metrics:** PSNR and FID metrics are missing from the evaluations. \n  - Specifically, in this reviewers experience, MUSIQ and especially CLIPIQA are not corelative with compression performance on visual inspection, and FID is commonplace to report, e.g. [1,3]. \n  - PSNR is widely used (in addition to the MS-SSIM reported in the appendix) to allow for consistent fair comparisons. At least one such pixel-space metric, preferably PSNR, should be in the main text, to showcase the inherent limitation in using latent-space-based methods (as mentioned in L121 in the appendix). This limitation should be discussed and noted as such.\n  - Even when considering the reported MS-SSIM metric in Fig. 3, for some reason the authors omit BPG, CTC and DPICT. This is problematic, as this reviewer would expect this methods to perform well in pixel-space distortion, especially in higher bitrates.\n- As the authors note in the supplementary, the method supports multiple bit-rates with one model, however it is limited to a small set of pre-chosen bitrates. This is unlike variable rate codecs (e.g. BPG or [1]). The authors mention how learning a single model for multiple bitrates is hard. An additional weakness is the need to retrain the entire model for every new wanted bitrate that would be added later, unlike e.g. PerCo which would just finetune for the new bitrate a pre-trained encoder that has most of the prior intact. \n- The visualization choice to only show small crops from the images is problematic in this reviewer’s opinion. The authors should include full images comparisons, or at least large crops, to see how the methods affects the entire image and not just a precisely selected small patch.\n\n[1] Ohayon, Guy, Hila Manor, Tomer Michaeli, and Michael Elad. \"Compressed Image Generation with Denoising Diffusion Codebook Models.\" arXiv preprint arXiv:2502.01189 (2025).\n\n[2] Yen, Hao, François G. Germain, Gordon Wichern, and Jonathan Le Roux. \"Cold diffusion for speech enhancement.\" In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5. IEEE, 2023.\n\n[3] Körber, Nikolai, Eduard Kromer, Andreas Siebert, Sascha Hauke, Daniel Mueller-Gritschneder, and Björn Schuller. \"PerCo (SD): Open Perceptual Compression.\" arXiv preprint arXiv:2409.20255 (2024)", "was_revised": true, "final_justification": "All of my concerns have been addressed:\n- The authors promised to fix the style issue - which is very important to me.\n- They compared in the rebuttal to missing related works and promised to include those results and discuss further related work. \n- The included PSNR and FID here and say they will include them in the paper.\n- They clarified details such as the used PerCo version.\n- They promised to expand the limitation section.\n- They will include further visualizations.", "rebuttal": "Thanks for the thorough review and valuable comments. We are encouraged by your recognition of the elegance of our method and the clarity of our empirical results. Below we respond to your specific comments.\n\n- Q1: The paragraph spacing issue.\n    \n    Thank you very much for your attention to this matter. We strictly followed the official LaTeX template, but acknowledge that some section and caption placements may have affected readability. We will carefully revise the formatting in our revised paper to ensure a clear presentation.\n    \n- Q2: Authors should expand the related work.\n    \n    Thanks for the suggestion. We will add the citations and include the comparison results in the revised paper, as well as a discussion of similar works in the related work section.\n    \n    - Comparison with DDCM compression approach.\n        \n        In our method, we use LPIPS-AlexNet as a training loss, and LPIPS-VGG as the evaluation metric to ensure a fair comparison. Here we follow DDCM to use LPIPS-AlexNet (denoted as LPIPS*) and crop DIV2K-val images to 768$\\times$768 for evaluation.\n        \n        | model | bpp | PSNR↑ | LPIPS*↓ | FID↓ |\n        | --- | --- | --- | --- | --- |\n        | DDCM (s=1000) | 0.0420 | 21.1670 | 0.2010 | 18.3300 |\n        | OSCAR (s=1) | 0.0313 | 21.3304 | 0.1958 | 31.8673 |\n        | DDCM (s=1000) | 0.0660 | 21.7800 | 0.1720 | 16.3960 |\n        | OSCAR (s=1) | 0.0507 | 21.9478 | 0.1554 | 28.9917 |\n        | DDCM (s=1000) | 0.1370 | 22.7070 | 0.1380 | 14.6620 |\n        | OSCAR (s=1) | 0.1250 | 23.4529 | 0.1097 | 15.7407 |\n        \n        Compared to the 1000-step DDCM, our one-step OSCAR achieves better performance on reference-based metrics (PSNR and LPIPS*), indicating more accurate reconstructions. Although DDCM yields better FID scores, the performance gap narrows as the bitrate increases.\n        \n    - Comparison with Cold Diffusion.\n        \n        We carefully reviewed this paper and found that it focuses on speech enhancement, without an image compression module and an open-source implementation. We'd appreciate it if you could clarify how it might be adapted to image compression.\n        \n    - What is the PerCo Version? Did the authors finetune 0.051 bpp and 0.094 bpp PerCo themselves?\n        \n        We use PerCo (SD) as our baseline. For 0.0313 bpp and 0.1250 bpp, we use the checkpoints released in their open-source code. For 0.051 bpp and 0.094 bpp, we train the model ourselves using their codebase. We will clarify this in the revised manuscript and include the corresponding paper in our citations.\n        \n- Q3: Authors should supplement PSNR, MS-SSIM, and FID results, and discuss how the latent-space nature of the method hurts pixel-space distortion.\n    \n    Thank you for the valuable advice.  We omitted the MS-SSIM results of BPG, DPICT, and CTC in the original paper as they generally underperform MS-ILLM, which we reported as a representative non-diffusion baseline. We will include the full results and the detailed discussion in our revised paper.\n    \n    Tables below present results on the DIV2K-val dataset.\n    \n    | model | bpp | PSNR↑ | MS‑SSIM↑ | FID↓ |\n    | --- | --- | --- | --- | --- |\n    | BPG | 0.0584 | 23.9356 | 0.8624 | 127.6502 |\n    | DPICT | 0.0745 | 24.5078 | 0.8896 | 78.0774 |\n    | CTC | 0.0707 | 25.8485 | 0.9166 | 75.4264 |\n    | MS‑ILLM | 0.0493 | 24.5745 | 0.8814 | 42.6882 |\n    | PerCo (s=20) | 0.0507 | 19.0644 | 0.6883 | 29.8819 |\n    | DiffEIC (s=50) | 0.0414 | 21.7711 | 0.7586 | 29.4854 |\n    | OSCAR (s=1) | 0.0507 | 21.9662 | 0.7623 | 28.8701 |\n    \n    | model | bpp | PSNR↑ | MS‑SSIM↑ | FID↓ |\n    | --- | --- | --- | --- | --- |\n    | BPG | 0.1361 | 26.1172 | 0.9293 | 94.8704 |\n    | DPICT | 0.1401 | 26.3581 | 0.9336 | 41.4491 |\n    | CTC | 0.1399 | 26.8273 | 0.9365 | 35.2881 |\n    | MS‑ILLM | 0.1622 | 27.0104 | 0.9417 | 18.3835 |\n    | PerCo (s=20) | 0.1250 | 21.9182 | 0.8613 | 16.8871 |\n    | DiffEIC (s=50) | 0.1264 | 23.4237 | 0.9088 | 15.1083 |\n    | OSCAR (s=1) | 0.1250 | 23.4810 | 0.8906 | 15.7285 |\n    \n    We observe that diffusion codecs generally outperform non-diffusion methods in perceptual metrics such as FID (shown in the table), LPIPS, and DISTS (reported in our main paper), but underperform in pixel-level fidelity metrics like PSNR and SSIM. OSCAR, along with PerCo, DiffEIC, and the recent DDCM, all exhibit this behavior. We attribute this common behavior to the inherent characteristics of the diffusion backbone.\n    \n    This trade-off between pixel-wise fidelity and perceptual quality arises mainly from two factors. (1) The strong generative prior in pre-trained diffusion models, which prioritizes perceptual quality over pixel-wise reconstruction. (2) The nature of denoising in latent space amplifies this trade-off, as the decoder must reconstruct images from compressed, high-level features, which are optimized for semantic or perceptual quality rather than pixel accuracy. Unlike pixel-space compression, latent-space representations inherently discard low-level details, making exact pixel-wise reconstruction more difficult. \n    \n    Notably, this gap in pixel-level fidelity tends to narrow at higher bitrates, especially for MS-SSIM. This suggests that the limitations of latent-space reconstruction can be gradually mitigated when more information is retained.\n    \n- Q4: The method supports multiple bitrates with one model, but is limited to pre-chosen bitrates.\n    \n    The goal of our method is not to support a very fine-grained range of bitrates, as in traditional variable-bitrate approaches. Rather, we aim to explore whether the proposed bitrate-to-timestep mapping can enable a one-step diffusion model to support a set of bitrates while achieving performance comparable to single-bitrate models. Our experiments provide strong evidence that OSCAR is effective in achieving this target.\n    \n    To the best of our knowledge, current variable-bitrate methods still underperform single-bitrate baselines (Figure 5 in our paper). While our approach is currently validated on a set of predefined bitrates, we believe it offers a promising path toward achieving both flexibility and high performance in future designs.\n    \n- Q5: The entire model needs to be retrained for every new wanted bitrates, while PerCo just finetunes a pre-trained encoder.\n    \n    We acknowledge that the diffusion backbone needs to be retrained for new bitrates. However, we demonstrate that OSCAR can generalize well to new bitrates with minimal computational overhead. \n    \n    We adopted a simple training scheduler: half of the training steps are allocated to the new bitrate, and the other half to the original bitrate group. Extending to a new bitrate requires only 15K training iterations, which take less than 4 hours on 4×A6000 GPUs. We attribute this efficiency to the fact that the diffusion model has already learned to handle structurally similar latent distributions, enabling fast and stable adaptation to nearby bitrates, while causing minimal performance degradation on previously trained bitrates.\n    \n    The tables below report results on the DIV2K-val dataset, where we progressively adapt the model to new bitrates one at a time. Numbers in parentheses indicate the performance change compared to the model before adaptation to the new bitrate. These results will be included in the revised paper.\n    \n    | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- |\n    | 0.0313 | 0.1262 | 0.3883 | 0.7210 |\n    | 0.0507 | 0.1102 | 0.3487 | 0.7623 |\n    | 0.0937 | 0.0694 | 0.2385 | 0.8713 |\n    | 0.1250 | 0.0617 | 0.2157 | 0.8906 |\n    \n    | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- |\n    | 0.0313 | 0.1306 (↑3.49%) | 0.3927 (↑1.13%) | 0.7161 (↓0.68%) |\n    | 0.0507 | 0.1141 (↑3.54%) | 0.3523 (↑1.04%) | 0.7578 (↓0.59%) |\n    | 0.0937 | 0.0728 (↑4.90%) | 0.2427 (↑1.77%) | 0.8595 (↓1.35%) |\n    | 0.1250 | 0.0648 (↑4.93%) | 0.2198 (↑1.91%) | 0.8766 (↓1.57%) |\n    | **0.0430 (new)** | 0.1198 | 0.3670 | 0.7402 |\n    \n    | bpp | DISTS↓ | LPIPS↓ | MS-SSIM↑ |\n    | --- | --- | --- | --- |\n    | 0.0313 | 0.1360 (↑4.19%) | 0.4006 (↑2.03%) | 0.7031 (↓1.81%) |\n    | 0.0507 | 0.1185 (↑3.87%) | 0.3589 (↑1.87%) | 0.7459 (↓1.57%) |\n    | 0.0937 | 0.0752 (↑3.34%) | 0.2457 (↑1.25%) | 0.8506 (↓1.03%) |\n    | 0.1250 | 0.0672 (↑3.75%) | 0.2228 (↑1.38%) | 0.8664 (↓1.16%) |\n    | 0.0430 | 0.1246 (↑4.01%) | 0.3742 (↑1.98%) | 0.7282 (↓1.62%) |\n    | **0.0781 (new)** | 0.0918 | 0.2766 | 0.8412 |\n    \n    In addition, we carefully examined the training procedure of PerCo. In their original paper, they state that all linear layers in the UNet (about 15% of the model) must be finetuned for each new bitrate. In contrast, our method uses LoRA to finetune only 1.8% of the total parameters. PerCo (SD) finetunes the entire UNet and hyper-encoder according to the open-source implementation. We kindly request you to double-check this point.\n    \n- Q6: The visualization should include full images or large crop comparisons.\n    \n    Thank you for the suggestion. We have prepared the full image comparisons. However, we noticed that additional PDFs are not permitted in rebuttal this year. We will include these visual results in the revised version of our paper. \n    \n- Q7: How many parameters are added by the rank 16 LoRA?\n    \n    The SD-2.1 UNet has 882.1M parameters, and the rank-16 LoRA introduces an additional 16.2M parameters.\n    \n\n---\n\nWe hope this response can help address your concerns. We believe our work can have an important impact on the field of image compression by offering a new perspective on leveraging pre-trained diffusion models for efficient one-step reconstruction across multiple bitrates. If you find our response satisfactory, we would be sincerely grateful if you could consider a higher score, as it would greatly support our efforts. Thank you once again for your consideration."}]

Author rebuttal:

Meta-review: OSCAR proposes an efficient one-step diffusion codec that supports multiple bitrates using a single model, achieving strong empirical results across datasets, bitrate settings, and model backbones. It performs competitively while being significantly faster than prior diffusion-based codecs.

were initially positive but raised concerns around generalization to unseen bitrates, lack of theoretical grounding, and unusual RD-curves. The authors responded thoroughly. They added new results on low-bitrate performance, RD-curve analysis, baselines, and visual comparisons. Reviewers acknowledged the updates, and all maintained or raised their positive scores after discussion.

While the method lacks deep theoretical grounding and still depends on pre-defined bitrate anchors, it offers a empirically valuable contribution. With the promised revisions, the AC recommends acceptance. This is an interesting and impactful paper—congrats to the authors for their work and clear rebuttal!
Decision: Accept (poster)