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arxiv: 2512.20901 · v2 · pith:OXVGF6VTnew · submitted 2025-12-24 · 💻 cs.CV

Benchmarking and Enhancing VLM for Compressed Image Understanding

Zifu Zhang , Tongda Xu , Siqi Li , Shengxi Li , Yue Zhang , Mai Xu , Yan Wang This is my paper

Pith reviewed 2026-05-25 07:23 UTC · model grok-4.3

classification 💻 cs.CV
keywords vision-language modelsimage compressionbenchmarkuniversal adaptorcompressed image understandinggeneralization gapmultimodal models
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The pith

A single universal adaptor boosts VLM performance on compressed images by 10-30% across codecs and bitrates.

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

The paper builds the first large benchmark of over one million compressed images to measure how vision-language models handle inputs from standard codecs at many bitrates and across multiple tasks. It separates the observed performance drop into two parts: information that compression permanently removes, and the model's failure to generalize to the altered statistics of compressed data. Only the generalization part proves fixable. The authors then show that one added adaptor raises accuracy by 10-30 percent no matter which codec or bitrate is used.

Core claim

VLMs exhibit a generalization failure on compressed images that a single universal adaptor can mitigate, rather than being limited solely by irreversible information loss. The benchmark quantifies this gap across codecs and tasks, and the adaptor delivers consistent 10-30% gains on the compressed inputs without requiring codec-specific changes.

What carries the argument

The universal VLM adaptor, a single module that improves understanding of compressed inputs irrespective of codec or bitrate.

If this is right

  • VLMs become usable on low-bitrate streams without retraining the entire model.
  • Existing image codecs can be paired with VLMs more effectively through one shared adaptation step.
  • Systematic evaluation of compression effects on multimodal tasks is now possible with the released benchmark.
  • Generalization gaps for other degraded inputs may be addressable by similar lightweight modules.

Where Pith is reading between the lines

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

  • The same adaptor principle could extend to other input degradations such as sensor noise or low resolution.
  • Real-world applications with bandwidth limits become more practical once VLMs handle compressed data reliably.
  • Testing whether comparable adaptors improve video or audio compression handling would be a direct next step.

Load-bearing premise

The performance gap on compressed images is caused by a generalization failure that one adaptor can correct, rather than by information loss that no model change can recover.

What would settle it

The adaptor produces no accuracy gain on a new codec outside the training set, or the gains vanish on tasks that require details known to be lost in compression.

Figures

Figures reproduced from arXiv: 2512.20901 by Mai Xu, Shengxi Li, Siqi Li, Tongda Xu, Yan Wang, Yue Zhang, Zifu Zhang.

Figure 1
Figure 1. Figure 1: Visualization of VLM performance drop due to image compression and improvement by our method, measured by BD￾Metric. The boom of multimedia services and applications has resulted in dramatic increase in image data, creating significant chal￾lenges in terms of transmission bandwidth and storage ca￾pacity. To address this, efficient image compression meth￾ods are essential for reducing data volume while main… view at source ↗
Figure 2
Figure 2. Figure 2: Comparative visualization of four image compression technique: uncompressed, tradi [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) BD-Metric values of different VLMs across different compression methods for the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Rate-Metric curves for all types of codecs on six tasks using Qwen-VL2.5-3B. Specifically, [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Rate–Metric curves validating the scaling law of distortion robustness are presented for the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Correlation matrix be￾tween human-vision metrics and VLM vision tasks. Finding5: VLM vision tasks correlate with human vision pixel-level metrics, but a gap remains. To investigate the relationship between human vision and machine perception, we conducted a comparative analysis of VLMs on compressed images using both task-level benchmarks and pixel-level im￾age quality metrics in [PITH_FULL_IMAGE:figures/… view at source ↗
Figure 7
Figure 7. Figure 7: Rate-accuracy comparison on POPE and SEEDBench [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Subjective results for POPE and SEEDBench metrics of standard VLM and our method. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Rate-accuracy comparison on POPE and SEEDBench [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: , serving as a reference upper bound for each model-task pair. The metrics are computed using a standardized comparison protocol, which may differ slightly from those reported in the original papers. However, we conducted a careful cross-check and found the discrepancies to be minor. Since our focus is on quantifying the degradation caused by compression, the absolute metric values are less critical than … view at source ↗
Figure 11
Figure 11. Figure 11: Rate-OCRBench curve for all types of codecs using Chatgpt-4o. [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Rate-metric curve based on COCO-Caption task for all types of codecs using Qwen-Chat [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p024_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Rate-Metric curves for all types of codecs on GQA, MMB, MME, OCRBench, POPE, [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Visualization of various VLM models across all metrics under three different compres [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Rate-Metric drop curves to validate the scaling law based on InternVL3 series models. [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Rate-Metric drop curves to validate the scaling law based on Qwen-VL2.5 series models. [PITH_FULL_IMAGE:figures/full_fig_p028_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Rate-accuracy comparison on GQA, MMBench, [PITH_FULL_IMAGE:figures/full_fig_p028_23.png] view at source ↗
read the original abstract

With the rapid development of Vision-Language Models (VLMs) and the growing demand for their applications, efficient compression of the image inputs has become increasingly important. Existing VLMs predominantly digest and understand high-bitrate compressed images, while their ability to interpret low-bitrate compressed images has yet to be explored by far. In this paper, we introduce the first comprehensive benchmark to evaluate the ability of VLM against compressed images, varying existing widely used image codecs and diverse set of tasks, encompassing over one million compressed images in our benchmark. Next, we analyse the source of performance gap, by categorising the gap from a) the information loss during compression and b) generalisation failure of VLM. We visualize these gaps with concrete examples and identify that for compressed images, only the generalization gap can be mitigated. Finally, we propose a universal VLM adaptor to enhance model performance on images compressed by existing codecs. Consequently, we demonstrate that a single adaptor can improve VLM performance across images with varying codecs and bitrates by 10%-30%. We believe that our benchmark and enhancement method provide valuable insights and contribute toward bridging the gap between VLMs and compressed images. The source code is available at https://github.com/bblgbr/CompressVLMBench.

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

2 major / 1 minor

Summary. The manuscript introduces the first large-scale benchmark (>1M compressed images) for VLMs across standard codecs, bitrates, and tasks. It partitions observed performance degradation into information loss versus generalization failure via qualitative visualizations, concludes that only the latter is mitigable, and presents a single universal adaptor that yields 10-30% gains on compressed inputs.

Significance. If the adaptor results are reproducible with proper controls, the benchmark and method would offer a practical contribution to deploying VLMs under bandwidth constraints. The scale of the benchmark and public code release are positive features. The work remains primarily empirical and does not supply parameter-free derivations or formal bounds.

major comments (2)
  1. [Gap analysis] Gap analysis section: the claim that only generalization failure (not information loss) is mitigable rests on qualitative examples and visualizations alone. No quantitative separation—such as task-specific mutual-information bounds, oracle comparisons against lossless reconstructions, or ablations isolating recoverable versus lost content—is provided, leaving the load-bearing distinction between the two gap types unverified.
  2. [Experiments / Results] Experimental evaluation: the reported 10-30% gains are presented without details on adaptor training procedure, exact baseline models and hyperparameters, statistical significance testing, train/test splits, or error analysis. These omissions prevent verification of the central claim that one adaptor generalizes across codecs and bitrates.
minor comments (1)
  1. Figure captions for the gap visualizations should explicitly state the codec, bitrate, and task for each example to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point-by-point below and commit to revisions that strengthen reproducibility and analysis without overstating the current manuscript's contributions.

read point-by-point responses

  1. Referee: [Gap analysis] Gap analysis section: the claim that only generalization failure (not information loss) is mitigable rests on qualitative examples and visualizations alone. No quantitative separation—such as task-specific mutual-information bounds, oracle comparisons against lossless reconstructions, or ablations isolating recoverable versus lost content—is provided, leaving the load-bearing distinction between the two gap types unverified.

    Authors: We agree that the distinction relies on qualitative visualizations of concrete examples (e.g., cases of irreversible detail loss versus patterns the VLM can adapt to). These examples support our conclusion that only the generalization gap is mitigable, as evidenced by the adaptor's consistent gains. We acknowledge the absence of quantitative separation such as mutual-information bounds or oracle lossless comparisons. In revision we will add oracle comparisons against lossless reconstructions and targeted ablations to isolate recoverable content; however, formal task-specific mutual-information bounds for VLMs lie outside the empirical scope of this work. revision: partial

  2. Referee: [Experiments / Results] Experimental evaluation: the reported 10-30% gains are presented without details on adaptor training procedure, exact baseline models and hyperparameters, statistical significance testing, train/test splits, or error analysis. These omissions prevent verification of the central claim that one adaptor generalizes across codecs and bitrates.

    Authors: We agree these details are necessary for verification. The revised manuscript will expand the experimental section with the full adaptor training procedure, exact baseline models and hyperparameters, train/test splits, statistical significance testing (including p-values), and error analysis. These additions will directly support the claim of cross-codec/bitrate generalization. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark and measured adaptor gains

full rationale

The paper introduces a benchmark of over one million compressed images, categorizes performance gaps qualitatively via examples, and reports measured improvements (10%-30%) from a proposed universal adaptor. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text. The gains are presented as experimental outcomes on the benchmark rather than derivations that reduce to the benchmark construction or prior self-citations by definition. The work is self-contained as an empirical study with no load-bearing steps that collapse to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claims rest on the assumption that a single adaptor architecture can generalize across all tested codecs and bitrates, and that the benchmark tasks and image set are representative. No free parameters are explicitly named in the abstract. The adaptor itself is an invented module whose effectiveness is demonstrated only within the paper's experiments.

axioms (1)
  • domain assumption The performance gap between high-bitrate and low-bitrate compressed images contains a mitigable generalization component separate from irreversible information loss.
    Stated directly in the abstract as the basis for proposing the adaptor.
invented entities (1)
  • universal VLM adaptor no independent evidence
    purpose: To enhance VLM performance on compressed images from existing codecs without retraining the base model.
    Introduced in the paper as the proposed solution; no independent evidence outside the reported experiments is provided.

pith-pipeline@v0.9.0 · 5765 in / 1319 out tokens · 22317 ms · 2026-05-25T07:23:47.875279+00:00 · methodology

discussion (0)

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