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arxiv: 2511.06424 · v2 · submitted 2025-11-09 · 📡 eess.IV · cs.AI· cs.CV· eess.SP· stat.ML

Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression

Amit Vaisman , Guy Ohayon , Hila Manor , Michael Elad , Tomer Michaeli This is my paper

Pith reviewed 2026-05-18 00:23 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.CVeess.SPstat.ML
keywords image compressiondiffusion modelszero-shot compressiondenoising diffusion codebook modelsfast compressionflexible compression
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The pith

Turbo-DDCM speeds up diffusion-based image compression by combining many noise vectors per denoising step.

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

The paper introduces Turbo-DDCM as a faster version of Denoising Diffusion Codebook Models for zero-shot image compression. It replaces the original sequential selection of noise vectors with a method that efficiently combines a large number of them at each denoising step. This change cuts the total number of denoising operations while an improved encoding protocol helps maintain rate-distortion performance on par with state-of-the-art methods. Two variants add flexibility by letting users prioritize specific image regions or compress to a target PSNR value instead of a fixed bit rate. A sympathetic reader would care because zero-shot diffusion compression has been too slow for most practical uses, and this approach makes it substantially quicker without apparent quality loss.

Core claim

Turbo-DDCM efficiently combines a large number of noise vectors at each denoising step from reproducible random codebooks, thereby significantly reducing the number of required denoising operations while maintaining performance on par with state-of-the-art techniques, supported by an improved encoding protocol and flexible variants for priority-aware and distortion-controlled compression.

What carries the argument

Efficient combination of multiple noise vectors at each denoising step in the DDCM framework, replacing sequential selection.

If this is right

  • Fewer total denoising operations suffice to reach target compression performance.
  • Rate-distortion curves stay comparable to prior zero-shot diffusion methods.
  • Compression can prioritize user-specified image regions without retraining.
  • Users can target a specific PSNR value rather than a fixed bits-per-pixel budget.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The speed gain could make diffusion compression practical for real-time or on-device scenarios.
  • Noise-combination ideas might extend to other sequential diffusion sampling tasks.
  • Varying the number of vectors combined per step could expose further speed-quality operating points.

Load-bearing premise

Combining many noise vectors at each denoising step preserves the reconstruction quality and rate-distortion behavior of the original sequential DDCM selection process without introducing new artifacts or requiring additional post-processing.

What would settle it

An experiment that measures rate-distortion curves and visual artifacts for Turbo-DDCM versus standard sequential DDCM at identical total denoising compute budgets and shows clear degradation or new artifacts in the combined case.

Figures

Figures reproduced from arXiv: 2511.06424 by Amit Vaisman, Guy Ohayon, Hila Manor, Michael Elad, Tomer Michaeli.

Figure 1
Figure 1. Figure 1: Turbo-DDCM: Our method provides reconstructions with equal or better fidelity com￾pared to previous methods, while being much faster. At the same BPP and runtime, the priority￾aware variant (bottom-right) better serves key regions of choice. from a reproducible Gaussian codebook, implying that the final generated image can be efficiently stored/transmitted by storing/transmitting the indices of the selecte… view at source ↗
Figure 2
Figure 2. Figure 2: Turbo-DDCM overview: Building on DDCM, we replace its random noise sampling with an effective and efficient closed-form selection rule that can quickly combine an arbitrary number of noise vectors, enabling significantly fewer diffusion steps. The selected indices are encoded using our new bit transmission protocol, which achieves substantially higher encoding efficiency than DDCM’s protocol. The decoder r… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results: The presented images are taken from the Kodak24 (512 × 512) dataset. Our method produces highly realistic reconstructions while achieving a speedup ranging from 3× to an order of magnitude compared to previous approaches, depending on the bitrate. step, preserving the order in which the atoms were selected, which is crucial for the noise constric￾tion on the decoder. However, in our th… view at source ↗
Figure 4
Figure 4. Figure 4: Quantitative evaluation: Comparison with zero-shot (top two rows) and other (bottom row) methods, reporting distortion (PSNR, LPIPS), perceptual quality (FID), and runtime (round￾trip compression–decompression in seconds). PSC’s runtime is omitted due to its extreme com￾plexity (>300 s/image). Turbo-DDCM achieves superior or competitive results against all zero-shot methods while being substantially faster… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results of the priority-aware (PA) variant: Regular methods fail to recon￾struct key regions, whereas our PA variant reconstructs them faithfully according to the prioritization mask. In the second row, the first two lines of the sign that are highly prioritized, are fully recon￾structed, while the third line, medium prioritized, is only partially reconstructed. These results are better viewed … view at source ↗
read the original abstract

While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents an efficient zero-shot diffusion-based compression method that runs substantially faster than existing methods, while maintaining performance that is on par with the state-of-the-art techniques. Our method builds upon the recently proposed Denoising Diffusion Codebook Models (DDCMs) compression scheme. Specifically, DDCM compresses an image by sequentially choosing the diffusion noise vectors from reproducible random codebooks, guiding the denoiser's output to reconstruct the target image. We modify this framework with Turbo-DDCM, which efficiently combines a large number of noise vectors at each denoising step, thereby significantly reducing the number of required denoising operations. This modification is also coupled with an improved encoding protocol. Furthermore, we introduce two flexible variants of Turbo-DDCM, a priority-aware variant that prioritizes user-specified regions and a distortion-controlled variant that compresses an image based on a target PSNR rather than a target BPP. Comprehensive experiments position Turbo-DDCM as a compelling, practical, and flexible image compression scheme.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper proposes Turbo-DDCM as an acceleration of the Denoising Diffusion Codebook Models (DDCM) framework for zero-shot image compression. DDCM sequentially selects diffusion noise vectors from reproducible codebooks to guide reconstruction; Turbo-DDCM instead combines a large batch of such vectors at each denoising step, coupled with an improved encoding protocol, to reduce the total number of denoising operations. Two flexible extensions are introduced: a priority-aware variant that weights user-specified regions and a distortion-controlled variant that targets a user-specified PSNR rather than a fixed BPP. Experiments are reported to show rate-distortion performance on par with prior zero-shot diffusion methods while achieving substantial speed-ups.

Significance. If the empirical claims hold, the work provides a practical engineering improvement that directly mitigates the computational bottleneck of diffusion-based compression, potentially enabling wider deployment. The two flexible variants add application-level utility without requiring retraining. Credit is due for framing the contribution as a targeted modification of an existing pipeline rather than a new theoretical guarantee, and for explicitly isolating the batch-combination operator as the source of the speed-up.

major comments (1)
  1. [§4] §4 (Experiments) and the description of the combination operator: the central claim that batch-combining noise vectors preserves DDCM rate-distortion behavior rests on the unverified assumption that the aggregation step does not materially alter the guided trajectory or introduce new artifacts. The manuscript should include an explicit ablation (e.g., varying batch size while holding total compute fixed) and visual inspection of reconstructions to confirm this assumption holds across the reported datasets.
minor comments (2)
  1. [Abstract] The abstract states that Turbo-DDCM 'significantly reduc[es] the number of required denoising operations' but does not report concrete factors (e.g., 5× or 10×); adding these numbers would strengthen the significance paragraph.
  2. [Method] Notation for the improved encoding protocol and the exact aggregation function over the noise batch should be formalized with a short equation or pseudocode block to aid reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive evaluation and the recommendation of minor revision. The feedback on verifying the impact of the batch-combination operator is well-taken and will improve the clarity of our claims.

read point-by-point responses

  1. Referee: [§4] §4 (Experiments) and the description of the combination operator: the central claim that batch-combining noise vectors preserves DDCM rate-distortion behavior rests on the unverified assumption that the aggregation step does not materially alter the guided trajectory or introduce new artifacts. The manuscript should include an explicit ablation (e.g., varying batch size while holding total compute fixed) and visual inspection of reconstructions to confirm this assumption holds across the reported datasets.

    Authors: We agree that an explicit ablation isolating the batch-combination operator would strengthen the manuscript. While Section 4 already reports that Turbo-DDCM matches DDCM rate-distortion curves on the evaluated datasets (with the same total number of denoising steps), we did not include a controlled study that varies batch size while holding overall compute fixed. We will add this ablation together with side-by-side visual comparisons of reconstructions for representative images from each dataset. The revised manuscript will therefore contain both quantitative and qualitative evidence that the aggregation step does not introduce measurable artifacts or trajectory deviations under the reported operating regimes. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes Turbo-DDCM as a direct algorithmic extension of the prior DDCM scheme, with an explicit new rule for batch-combining noise vectors at each denoising step plus an improved encoding protocol. No equations, predictions, or first-principles results are shown to reduce by construction to fitted inputs, self-definitions, or self-citation chains; the performance claims rest on empirical rate-distortion comparisons rather than any internal derivation that presupposes the target outcome. The work is therefore self-contained as an engineering acceleration whose validity is externally testable against DDCM baselines.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the diffusion denoiser behaves predictably when fed combined noise vectors and that the original DDCM codebook construction remains valid under the new selection protocol. No new physical constants or invented particles are introduced.

free parameters (1)
  • batch size of noise vectors per step
    Chosen to balance speed and quality; value not stated in abstract but required for the claimed runtime reduction.
axioms (1)
  • domain assumption The pre-trained diffusion denoiser can be guided by any combination of noise vectors drawn from the same reproducible codebook distribution used in DDCM.
    Invoked when the paper states that combining vectors still guides the denoiser to the target image.

pith-pipeline@v0.9.0 · 5515 in / 1409 out tokens · 27385 ms · 2026-05-18T00:23:53.401640+00:00 · methodology

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Forward citations

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · cited by 1 Pith paper

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