Title: OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Model

Image Compression

One-step Diffusion
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/.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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)\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nSomewhat. 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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.\n\nQUESTIONS:\nI have no questions for the authors.\n\nLIMITATIONS:\nNo 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1.\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?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}