Title: Prompt-Guided Alignment with Information Bottleneck Makes Image Compression Also a Restorer
Venue: NeurIPS 2025
Accepted: yes
Keywords: Learned Image Compression

Image Restoration

Degradation

Prompt Learning

Information Bottleneck
Abstract: Learned Image Compression (LIC) models face critical challenges in real-world scenarios due to various environmental degradations, such as fog and rain. Due to the distribution mismatch between degraded inputs and clean training data, well-trained LIC models suffer from reduced compression efficiency, while retraining dedicated models for diverse degradation types is costly and impractical. Our method addresses the above issue by leveraging prompt learning under the information bottleneck principle, enabling compact extraction of shared components between degraded and clean images for improved latent alignment and compression efficiency. In detail, we propose an Information Bottleneck-constrained Latent Representation Unifying (IB-LRU) scheme, in which a Probabilistic Prompt Generator (PPG) is deployed to simultaneously capture the distribution of different degradations. Such a design dynamically guides the latent-representation process at the encoder through a gated modulation process. Moreover, to promote the degradation distribution capture process, the probabilistic prompt learning is guided by the Information Bottleneck (IB) principle. That is,IB constrains the information encoded in the prompt to focus solely on degradation characteristics while avoiding the inclusion of redundant image contextual information. We apply our IB-LRU method to a variety of state-of-the-art LIC backbones, and extensive experiments under various degradation scenarios demonstrate the effectiveness of our design. Our code will be publicly available.


Peer reviews:
comments: SUMMARY:
The paper presents IB-LRU, a method using IB-constrained prompt learning (via PPG) to dynamically adapt pre-trained image compressors to handle fog, rain, etc., by focusing prompts purely on the degradation, leading to better latent alignment and compression without costly retraining.

STRENGTHS AND WEAKNESSES:
Strengths:
(1) The article presents its content clearly and understandably.
(2) When applying LIC models trained on clean image data to degraded image data, their performance significantly deteriorates. However, the alternative approach of first applying image restoration models to degraded images and then compressing the restored images incurs high computational costs (e.g., large parameter count, excessive inference time). Thus, the proposed idea of using a prompt-based scheme to fine-tune pre-trained LIC models is well-justified. Furthermore, empirical results confirm that the proposed scheme achieves superior LIC metrics while maintaining advantages in both parameter count and inference time.
(3) Employing Information Bottleneck theory for prompt design demonstrates significant novelty.

Weaknesses:
(1) A performance comparison between the proposed prompt scheme and existing prompt schemes is not yet provided in the paper.
(2) As shown in Figures 4 and 6, the proposed method outperforms the baselines on some tasks even without the Information Bottleneck loss. Does this suggest that introducing prompts, rather than the IB loss, is the key factor for performance improvement?

QUESTIONS:
see the Weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-16

comments: SUMMARY:
This paper propose a lightweight plug-and-play module for degradation image compression. The author propose two module, Probabilistic Prompt Generator (PPG) and Degradation Adaptive Gating Modulation (DAGM) to align the latent distribution across degradation types and use Information Bottleneck principle for optimization.

STRENGTHS AND WEAKNESSES:
### Strength

- The proposed framework is unified for different degradation images.
- The plug-and play module can be used for different LIC backbones.

### Weakness

- The proposed module, PPG and DAGM seems to have little difference with PromptIR, which is lack of novelty.
- The results are worse than MOCE-IR in some cases.

QUESTIONS:
- In Fig.4, the results of restoration-then-compression paradigm use the same frozen LIC backbone, which made no adaptation for restored images. If using the restored image output by the restoration methods and the clean image pair to finetune the LIC model, is it can achieve better results, even better than the proposed methods? As the plug-and-play model needs to train for 100 epochs, a more convincing way is to compare with the adaptive LIC models which are finetuned using restored images.
- In line 275, “While both are capable of achieving satisfactory perceptual quality, they tend to result in a significant increase in bpp.” However, it seems the proposed method have higher bpps in low bpp regions. It’s better to explain why.
- Perceptual quality is usually measured by LPIPS or FID. The author use PSNR as metric, but use perceptual quality as description is confused.

LIMITATIONS:
See Questions section.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 1
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a prompt tuning method to finetune an image codec, enabling it to simultaneously perform image restoration tasks (such as denoising, deraining, dehazing, etc.) and image compression.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: The authors propose a prompt tuning method to finetune an image codec to achieve joint image restoration and compression.\n\nWeaknesses:\n1) Is it necessary to combine image restoration tasks, such as deraining and dehazing, with image compression? Rain and haze are common natural phenomena and may well be part of the information the user intends to record. The goal of image compression is to reduce storage bitrate while preserving image fidelity as much as possible. Enforcing deraining or dehazing during the compression process may go against the user's intention and destroy the value of the original image.\n2) Using prompting tuning to finetune image compression for domain and task adaption have been studied in previous works, such as [a,b,c]. The novelty of this work is limited.\n3) Joint image denoising and compression is reasonable and has been studied in previous works, such as [d,e]. The authors should compare with these related methods. It is unfair to compare with restoration-then-compression scheme.\n4) What would happen if the authors' PPG and DALM were replaced with fine-tuning strategies such as AdaIN or LoRA? Is the prompt tuning strategy proposed by the authors the best possible approach?\n\n[a] Yi-Hsin et al., TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception, ICCV, 2023.\n\n[b] Han Li et al, Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation, ECCV, 2024.\n\n[c] Koki Tsubota et al., Universal Deep Image Compression via Content-Adaptive Optimization with Adapters, WACV, 2023.\n\n[d] Benoit Brummer, Christophe De Vleeschouwer, On the Importance of Denoising When Learning to Compress Images, WACV, 2023.\n\n[e] Xi Zhang, Xiaolin Wu, FLLIC: Functionally Lossless Image Compression, arxiv, 2024.\n\nQUESTIONS:\nPlease answer the questions in the above weaknesses.\n\nLIMITATIONS:\nThe authors do not discuss the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the performance degradation of LIC models under image degradations, including noise and blur. Instead of using a separate restoration module, the authors propose learning degradation-aware prompts that adaptively condition the compression process. These latent conditioning signals guide the encoder to handle diverse degradations better. Additionally, an information bottleneck constraint ensures that the prompts capture essential degradation features while minimizing redundant content information.\n\nSTRENGTHS AND WEAKNESSES:\n- **Strength**  \n\n  - **Prompt-based latent representation alignment**: The paper introduces a new approach that generates probabilistic prompts to modulate encoder features adaptively, enabling better alignment between degraded and clean latent distributions.\n\n  - **Integration of the information bottleneck principle**: The authors apply the IB principle to control the mutual information in prompt learning, ensuring that the prompts capture essential degradation signals while suppressing redundant content.\n\n  - **Effective bitrate reduction**: The proposed IB-LRU scheme demonstrates substantial bitrate reductions and perceptual quality improvements across multiple degradation types.\n\n   - **Lightweight and flexible design**: Unlike cascaded restoration-then-compression pipelines, the method requires fewer additional parameters and achieves significantly faster inference.\n\n\n- **Weakness**  \n\n  - **Limited diversity of degradation types**: Although the paper covers haze, rain, and noise, it does not evaluate more complex or domain-specific degradations (e.g., heavy compression artifacts, under-display camera artifacts, or motion blur), which may limit the generalizability of the approach.\n\n  - **Dependence on frozen LIC backbones**: The method assumes pre-trained LIC models remain fixed during training, which could restrict potential improvements that might arise from jointly fine-tuning the backbone along with the prompt module.\n\nQUESTIONS:\n- How sensitive is the model's performance to the choice of the latent prompt dimensionality d? Can authors provide the experiments with different prompt sizes?\n\n- Do authors have any insights into how the IB constraint weight should be tuned for unseen degradation types?\n\nLIMITATIONS:\nNo. Since the checklist guides the authors to include a separate Limitations” section, the authors are recommended to add one in the paper. Potential limitations may include the dependence on frozen LIC backbones and the limited diversity of degradation types evaluated.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}