Prompt-Guided Alignment with Information Bottleneck Makes Image Compression Also a Restorer
Authors: Xuelin Shen
Quan Liu
Jiayin Xu
Wenhan Yang
Venue: NeurIPS 2025 poster ()
Keywords: Learned Image Compression
Image Restoration
Degradation
Prompt Learning
Information Bottleneck
Primary area: applications

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.

Prompt-Guided Alignment with Information Bottleneck Makes Image Compression Also a Restorer
Xuelin S*
Quan Liu
Jiayin Xu
Wenhan Yang'
Peng Cheng Laboratory
GGanggoog Laboratory of Artificial Intelligence and Digital Economy (SZ) CCollege of Computer Science and Software Engineering, Shenzhen University thenxuelin@gmi ac. en, quanliu@gml. acc. cn jiayinxu@gml cn, yangwh@pcl ac
Abstract
Learned Image Compression (LIC) models face critical challenges in real-world scenarios due to various environmental degradations, such fog and rain. Due 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 infor- mation 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 pottleneck-conntrraln resentation Unifying (IB-LRU) scheme, which a Probabilistic Prompt Generator (PPG) is deployed to simultancously capture the distribution of different degrada- tions. Such design dynamically guides the latent-reprssentation process the encoder through a gated modulation process. Moreover, to promote the degradation distribution capture process, the probabilistic prompt learning guided by the In- formation Bottleneck (IB) principle. That is, IB constrains the information encoded in the prompt focus solely on degradation characteristics while avoiding the inclu- sion of redundant image contextual information. We apply our IB-LRU method to a variety of state-of- the--art LIC backhones, and extensive experiments under various degradation scenarios demonstrate the effectiveness of our design. Code is available at htttps://githtt: hotp:t/..ooomm/uuuuunu0222211111 LRU- syee/BB-LLLUU----oeeeiii
Latent Rep-
Introduction
In the past decade, Learned Image Compression (LIC) has emerged competitive alternative to conventional image coding standards [1] 2 3 by leveraging deep neural networks to optimize the rate-distortion trade-off in end-to-end manner. Existing LIC models typically adopt an autoencoder structure, where the encoder extracts latent representation and the decoder reconstructs the image, characterized by integrated entropy model that captures spatial dependencies in the latent representation and plays a critical role in compression efficiency. A milestone in entropy modeling is the introduction of the hyperprior [4) which adopts the variational autoencoder (VAE) framework and introduces a Gaussian- approach enables the compression model adaptively learn compact latent representations tailored
prior to model the distribution of latent representations. This
"These authors contributed equally. Corresponneece to: Wenhan Yang.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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App 0.482
(4)
(b)
App 0.411
(d)
(e)
Figure An intuitive illustration of compression performance under degradation scenarios. (a) Input image with rain degradation; (b) Output from well-trained LIC network [3]: (c) Output from the restoration-thens then-compression paradigm (PromptIR (6] + LIC); (d) Output from our proposed IB-LRU scheme. In addition, we compare the commonly employed factorized prompt [6] (e) and our proposed probabilistic prompt (f) in terms of their capacity capture discriminative degradation characteristics, visualized via t-SNE plots.
the statistical properties of natural images, significantly improving rate-distortion performance and forming the foundation for the following LIC models.
Despite their impressive performance under standard conditions, existing LIC models often struggle in real-world settings, where the imaging process typically suffers from various environmental degradations, e.g., noise, fog, rain, or low-light conditions. This limitation arises from their data- driven nature, that are trained on high-quality natural images and relies on learned priors that assume clean image statistics. However, degraded scenarios, the obtained latent representations deviate significantly from these assumptions. Consequently, as the entropy model is tightly coupled with the prior, it becomes difficult accurately estimate the distribution of latent features, leading to a substantial drop in compression performance. In other words, compressing degraded images often requires significantly more bits than pristine ones, an intuitive demonstration is provided in Fig. (b). Moreover, the wide range of possible degradation types and intensities in real-world settings makes it impractical to retrain dedicated LIC models for each case to capture their specific distributions.
In facing this challenge. potential path is restoration- centtiionnhheeeee weaet-tthe-comoope compress paradigm, which involves deploying cutting-edge multy-in-one image restoration models [7 8 g 10 (denoting their capacity to handle multiple degradation within a unified process) at encoding end, with the aim of reducing the degradation in a preprocessing stage so that the restored images can be compressed with an acceptable rate cost. However, in our coding practice, this paradigm does not yield optimal performance. Although it may provide satisfactory perceptual quality, the restored images still exhibit distributional divergence from natural images, leading to suboptimal compression results in the context of LIC, as shown in Fig. (c). Moreover, adding a separate restoration model may reduce flexibility in real-world deployments, especially when computational resources at the imaging end are limited.
To tackle the aforementioned challenges, we introduce the Compressor-as as- a-sestorationn paradigm, where prompts are employed to adaptively steer the encoder, thereby achieving unified framework Latent
for compression and restoration. We propose novel Representation Unifying (IB-LRU) scheme, a lightweight encoder-side plugin module that con- strains latent representations by aligning degraded and clean-image distributions, thereby enhancing compression and reconstruction without modifying the LIC backbone. In particular, to address diverse input degradations within unified training framework, we adopt prompt learning strategy enhanced by VAE-based Probabilistic Prompt Generator (PPG), which models each degradation using distribution parameters. Unlike existing approaches that rely on factorized prompt vectors, our probabilistic design yiclds more compact and discriminative representations with fewer parameters,

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as shown in Fig. (e) and (f). During inference, degradation information is sampled from the PPG's posterior and passed to Iegradation-Adaptive Gating Modulation (DAGM) module, which guides the encoding process. Additionally, we incorporate the Information Bottleneck (IB) principle to constrain the mutual information between prompts and image content, encouraging the prompt to focus on essential degradation characteristics. In experiments, we implement our IB-LRU scheme on multiple LIC backbones, and extensive results demonstrate that the proposed scheme effectively improves compression performance under various degradation settings.
Our key contributions are as follows,
To the best of our knowledge, the proposed IB-LRU is the first exploration aimed at improv- ing the efficacy of LIC models under various degradation settings through a lightweight plug-and-play design.
We propose a novel probabilistic prompt learning strategy that characterizes each degradation type using a set of distribution parameters, rather than factorized vectors, resulting in more discriminative degradation representations.
We introduce an Information Bottleneck (IB)-based optimization criterion for the probabilis- tic prompt learning process, and derive a variational approximation bound in theory that guides the design of our optimization strategy.
2 Related Work
Learned Image Compression. The past decade has witnessed the rapid advancement of Learned Image Compression (LIC), driven by the progress of deep learning technologies. The first attempt was made by Ballé et al. [4], introduced an end-to-end autoencoder pipeline, with a factorized entropy model responsible for constraining the compactness of the latent representation. Furthermore, Ballé el al. [1I made a significant advancement by introducing a hyperprior into the entropy coding process, enabling the learning of hierarchical entropy model in which the distribution of latent features is conditioned on hyper-latent variable. In the following works, numerous efforts have been made further enhance the entropy model by leveraging contextual dependencies, such as local con- text 12) global context information [13], and channel-wise context [14 [5] resulting in incremental improvements. In addition, some studies focus on reducing the computational complexity of the entropy coding process, by leveraging checkerboard context models [16) or sparse sampling method- ology [7]. Besides efforts aimed at optimizing the entropy model, other works focus on improving the backbone networks by incorporating architectures such as residual networks [18), invertible neural networks [19 20), and Swin- Transformers [21 22 23 24). They enable more expressive latent representation process, leading to notable improvements in compression performance.
Prompt Learning. The concept of prompt learning originated from the field of natural language processing, involving in inducing pre- mre-trinned language models (e.g., BERT [25) or GPT (26)) to generate answers given cloze-style prompts, extracting information useful for downstream tasks. Subsequent research in prompt learning shifted toward automating this process using labeled data, replacing manually designed prompts with learnable ones. For instance, Jiang et al. (27 employed text mining and paraphrasing techniques to generate pool of candidate prompts, from which the
optimal ones were selected based on training accuracy. Meanwhile, other studies 28 29 30 proposed to factorize prompts into set of continuous vectors that can be end-to-end optimized with respect to a given objective, a strategy known as continuous prompt learning. In the field of computer vision, CoOp R1 was one of the earliest works to adopt the continuous prompt learning methodology. demonstrating notable improvements in transfer learning performance. Building on this, Zhou et al. (32) further applied prompt learning techniques to image classification, achieving significant gains in generalization capability. Similarly, Ju et al. [33] explored the use of prompt learning to reformat video-related tasks in alignment with pre-training objectives. Among existing prompt learning works for machine vision, the most relevant to ours is PromptIR (6) which uses a set of factorized vectors as prompts to capture the characteristics of different degradation types for multi-in-one image restoration network. However, the naive prompt representation and learning strategy yield suboptimal performance in capturing clear and distinct features of various degradation.
Information Bottleneck The Information Bottleneck (IB) principle, originally proposed by Tishby et al. (34), offers theoretical framework for extracting the most relevant information from input
3

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Aughhol
MIUP
Liner
Signoid
Encoder
Oela
Stale
I
Ouiput
Linbar
Affine
10:43
Five
(a)
(b)
(c)
Figure 2: (a) Compression pipeline of the proposed IB-LRU scheme. (b) Details of the proposed
PPG. (c) Details of the proposed DAGM module.
data with respect to the task target by compressing the input representation while preserving task- relevant features. In the context of deep learning, the IB framework has been adapted to encourage models to learn compact and generalizable representations by constraining the mutual information between the input and intermediate features (35] In particular, this work introduced the variational information bottleneck (VIB), which formulates the objective in a tractable form using variational approximations, enabling its integration into neural network training pipelines. More recently, the IB principle has been employed in computer vision tasks enhance robustness and generalization 7 36 37 3S These works inspire our use of IB to guide prompt learning, aiming to extract clear degradatiin-relevant information while suppressing redundancy from irrelevant image context.
3
Methodology
3.1 Motivation and Overview
Existing LIC models typically follow an autoencoder-like pipeline. An encoder G-(-) maps the input image x to a latent representation = = (x),, which is then fed to a uniform quantizer that converts its discrete form 1. Subsequently, an entropy encoder R(-) losslessly encodes é into binary bitstream, transmits it to the decoder side, and provides its rate estimation. At the decoder side, the decoder G-(-) reconstructs the image & (2). The entire framework optimized under the rate-distortion criterion,
L = R(2) + (D(
(1)
where R(2) is the estimated rate measured in bits per pixel (bpp). D(:,)) denotes the distortion metric (e.g. MSE: ||.- B112), ), and À is Lagrange parameter, being responsible for obtaining codecs at different compression levels. However, while LJC models perform well on clean natural images, their effectiveness degrades real-world conditions involving noise, fog, blur, al. This is because:
The latent distributions degraded images deviate significantly from those of clean images;
The entropy model relies heavily on prior assumptions, making accurate distribution estima- tion challenging under degradation.
To address the generalization issues of LIC models, we propose an Constrained Latent Representation Unifying (IB-LRU) scheme, whose central idea is perform prompt optimization with information bottleneck constraint to guide the encoding of degraded images so that the distribution of their latent representations aligns with that pristine images, thereby improving compactness and ensuring high-quality outputs at the decoder side. In general, the pro- posed IB-LRU scheme is designed with three key objectives: flcxibility through lightweight plug-in design; compatibility with existing LIC backbones without requiring parameter modification; and generalizability to support diverse degradation types within a single training process.
The overall framework is illustrated in Fig. 2. The proposed IB-LRU scheme includes two main com- ponents: Probabilistic Prompt Generator (PPG): A baseb encoder that learns degradation- specific prompts using set of distribution parameters drawn from multivariate Gaussian. At infer- ence time, degradation representations are sampled and used guide encoder; 2) Degradation- Adaptive Gating Modulation (DAGM): A modulation module that adjusts the encoding process using the sampled prompt, effectively aligning the latent distribution across degradation types. More- promote probabilistic prompt learning capture clear and distinct representations of different

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degradations, we investigate: 3) Information Bottleneck (IB) Principle: variational approximation of IB our context is explored constrain the mutual information between the learned prompt and the input image while preserving the task-relevant information, thus suppressing redundant information of the learned prompt from the broader image context. The detailed implementations of the proposed PPG and DAGM are presented in Subsection 3.2 while the derivation of the variational approximation of IB is provided in Subsection 3.3
3.2 Prompt-Guide Latent Representation Alignment
Probabilistic Prompt Generator: As shown in Fig. (b), the PPG involves a VAE-style encoder, denoted as p = (z 0) where 0 represents the model parameters and p the probabílistic prompt characterized by its distribution parameters [up, Tp.. In particular, during the inference stage, they are sampled from the approximate posterior (pp θ) NN(:: Mpp ()) where Mp E R is the
mean vector, and the diagonal covariance matrix is given by diag(o with of IR representing the per-dimension variances. Herein, d denotes the dimensionality of the prompt.
Degradattion-Aaaptiee Gating Modulation: These posterior statistics, /'p and log of. are then fed into a gated modulation process, as shown in Fig. (c). Given an intermediate feature from a frozen backbone LIC encoder, a modulation network Mmod() (-) tasks as input the Mp and f to produce basis modulation maps [7b, anp]:
F
(Tb, [b] (mp:
Concurrently, an MLP layer computes a gating signal from log oB. which provide information related to the spread of the approximate posterior. This signal derives final modulation maps [Y, a]:
sigmoid(MLP(logoo of)),
= 1 + s (nb - 1),
(3)
C Os-
The restored feature fres is then obtained via affine transformation:
fies 7 3 f + a.
(4)
3.3 Information Bottleneck- Prompt Learning Constraint
As for the training process of the proposed PPG, we posit that an effective prompt should act as minimal sufficient statistic of the degradation signal relative to the content already captured in the intermediate feature f of the frozen backbone LIC encoder. Directly learning p might lead it to capture redundant information, e.g., image content information, which would inevitably prevent the learned prompt from capturing clean and distinct characteristics of different degradation types. Therefore, we investigate the Information Bottleneck (IB) principle effective guidance for the prompt learning process. In the context of IB, we aim to find prompt p that minimizes the mutual information retains about the input x while maximizing the mutual information relevant to the target task y, which denotes the pristine ground truth in this case. We formulate the IB-based optimization objective for as:
34 to provide reliable and
(5) where the left term represents the information (potentially redundant) about the input X that P contains, and the right term quantifies the additional information P offers about the target Y beyond that already captured by the backbone features F, 3 denotes the trade-off parameter.
LIB prompt I(P:X) (I(P
As directly optimizing Eq. (5) is intractable, we follow Variational Information Bottleneck (VIB) B5 to derive tractable variational upper and lower bounds for the left and right terms, respectively.
Variational Lower Bound for Z(P; F): By definition, Y = H(Y H(Y F). Since F is deterministic function of X (frozen), H(YFF) can be treated constant with respect to P. Thus, maximizing (P;; F\F) is equivalent to minimizing the conditional entropy H(Y F):
H(Y P. F') Eup. wttp:://ww/p////o/pppppgo /loggl//g//) p.
(6)
"In the following derivations, X, Y. P. F' represent random variables, while x. single instances of random variables
and f are scalar or
5

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We apply a variational approximation g f) for ply f), corresponding to our decoder. Using the non-negativity of KL divergence, DxI. ppy p. f) qu p. f)] 0, we have for any given p. f:
Eptele. (log) /)] 2 Epcupp / flog (ulp,
Substituting this inequality (after taking expectation over p(p f]) an upper bound for the conditional entropy:
into the entropy expression yields
[( F) < - plog g
(8)
Therefore, variational lower bound for the mutual information is:
I(P 2 Eplv.p,) flog f()lp,
Given the above derivation, we found that maximizing this lower bound is equivalent to maximizing the expected log-likelihood Epg. p...p... f) [log ( p, f)). Thus, in actual implementation, the distortion term in Eq. ①1 is employed and acting as a proxy for log ().
Variational Upper Bound for ((;;X): We aim derive a tractable upper bound for the mutual information Z(P; X). By definition, we obtain:
log ((P xx)
I(P; X) P(zpp)>p(x.p)
((p)
p)pppppp
[log p(pp=)())))))))))))()))) (pgpp
(10)
As the true prior p(p) Ex=((=) [p][z)) is intractable, introduce r(p). standard Gaussian distribution, as approximation to the true prior p(p): Using the non-negativity of the KL divergence between the true prior and our variational approximation, Dxi. (p(p) || r(p)] 2 O, we have:
p [(p)] S Ppp-pi)loogr(0
(11)
Substituting this inequality back into Eq. (10)
X(P;X) S p(pp)) [ogr(p)))] (p-p(p)logr(()))
(12)
This bound still involves the true posterior p(p]x) inside the expectations. Now, we further ap- proximate the expectation involving the true posterior by replacing (p) with our PPG q(P ax; 0). This step makes the bound computable using samples from the encoder. Recognizing the struc- ture resembles the KL. divergence, we arrive at the commonly used VIB upper bound for mutual information:
I(P; X) S Ex-p(z) Ep-p(pla) log p(p())
r(p)
Ex)p(z) (k)()(p/=) r(p)).
(13)
Things have to be mentioned that, although replacing the true posterior p(p]ær) with the approximate posterior g(p) zz; 0) inside the expectation introduces an additional approximation beyond the rigorous bound derived using DKL (p r) 2 0, Eq. [13 provides a practical objective for IB implementations.
With the trackable IB-based optimization creation for prompt learning, the final loss function for IB-LRU scheme is formulated as,
Liotal Lrd + P || r(p)]-
(14)
4 Experiments
In the experimental stage, first implement the proposed IB-LRU scheme multiple backbone LIC networks and evaluate its effectiveness across a variety of degradation settings. Subsequently, we compare its performance with the isstoratonnthhen-compeeee edge multy-in-one restoration models. Moreover, comprehensive ablation studies are conducted to validate the effectiveness of our design.
paradigm, which is built upon cutting-

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Table Comparison between the proposed scheme and the restoration ssstorationttieenccomrrssssooooo paradigm regrading parameters and inference speed.
Model
AirNet+TIC
Restormer+TIC
PromptIR+TIC
MOCE-IR-TIC
Ours+TIC
Extra Param.
8.93M
26.12M 363.9
35.59M 416.5
25.35M 349.4
4.6M
Inference Speed (ms)
483.3
109.5
27.0
25.5
Ap
22.5
26.4
15.00
15.75
15.00
24.0
11.70
15.45
11.55
03
30
02
05
02
06
BF9
BP2
(a) Haze
(b) Rain
(c) Rain
PSNT
24.0
22.5
254
252
02
03
0.4
0.5
36
03
05
BIF
(d) Noise 15
(e) Noise 25
(I) Noise 50
Figure 3: Performance comparison between the LIC backbone networks with and without the integration of the proposed IB-LRU scheme.
4.1 Experimental Setting
Benchmark:
The performance is assessed across three degradation types, including haze, rain,
and noise, with multiple degradation intensities employed for each type: 1) Rain: The Rain100
dataset (39] consists of 2,000 degraded-clean pairs for each of the heavy and light rain scenarios. In our experiments, 1,800 pairs are used for training and 200 pairs for testing in each scenario. 2) Noise: For the noise scenarios, the training set was constructed using 400 images from BSD400 [40] and 4,744 images from WED 41. with degraded versions generated by adding Additive White Gaussian Noise (AWGN) with a E (15, 25, 50). The testing set is formed by combining BSD68 42 and
Urban100 43). using the same noise settings. 3) Haze: The SOTS dataset [44] is employed, with
72,135 500 images for training /testing. The training sets of all above datasets are combined to support the training of our IB-LRU scheme.
LIC backbone: Three widely adopted LIC networks are employed, including the milestone Bmshj2018 8 [II], as well as two cutting-edg' models: the Swin Transformer based TIC [3] and MLIC++ [45] These three LIC backbone networks were pre-trained exclusively on the clean COCO 2017 dataset. For each model, four checkpoints corresponding to different compression levels are employed, and their parameters are kept frozen during the training of our IB-LRU scheme.
Anchors: Four cutting-edge multy-in-one image restoration networks are employed for the restorationnthen-commee compreeossoooion paradigm, including (8) Restormer (9] and PromptR 6), and MOCE-IR [46]. All models are trained from scratch using the same training set as ours to ensure fair comparisons. The restored output images are then fed into well-trained LIC backbones for evaluation.
We introduce rate-perception criterion for this unique task. this context, perception refers to the distance between decoded images and corresponding pristine ground truths, measured in terms of PSNR (dB), while the bit-per-pixc (bpp) value is used to quantify the rate.
Evaluation Criteria:
Implementation Details: We train our model using the AdamW optimizer with settings 3 0.9 and B2 0.999. The initial learning rate set to 1 x 10-1, This rate maintained for the first 80 epochs (80% of the total training) and then decayed to 1 10 - for the remaining 20 epochs. The
7

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301
26.4
360
Ano
260
24
27.2
24.
0.5
0.30
4.25
1L30
025
0.55
0.0
025
112)
033
8.0
145
232
0.30
0.2
100
0.35
(a) Haze
(b) Rain
(c) Rain
26.0
28.5
26.0
26.1
27.
25.0
Aohe
26.9
NdC::
25.1
218
364
25.
260
264
0.30
0.5
8.30
D/9
9:45
333
325
035
307
0.35
6:00
0.5
030
D77
0:30
033
(d) Noise 15
(e) Noise 25
(1) Noise 50
Figure 4: Performance comparison between the restoration-heen-copprssssin paradigm and our IB-LRU scheme, both of which are based on the TIC backbone.
model trained for a total of 100 epochs. Training is performed on randomly cropped 256 x 256 patches using setup of 8 NVIDIA RTX 6000 GPUs.
4.2 Experimental Results
Performance is compared between the LIC backbones with and without our IB-LRU implementation to examine effectiveness of the proposed scheme. is worth noting that although the LIC backbones are not designed for image restoration, we focus on bitrate savings in this part, while still reporting their perception indices to ensure consistent and clear representation.
Effectiveness Evaluation:
The corresponding results, presented in Fig. S show encouraging improvements. In particular, under the light rain, heavy rain, noise_le noise 25, and noise scenarios, our proposed method brings notable improvements in rate performance, resulting in average bpp reductions of 27.1% %, 51.6%, 18.2%, 34.6%, and 64.9%, respectively, across the three LIC backbones. Moreover, significant improvements in perceptual quality can also be observed, demonstrating the contribution of latent representation unification to the image reconstruction process. It is worth noting that in haze scenario, although the IB-LRU scheme remarkably improves perceptual quality, the bpp values are even increased. According to our analysis and comprehensive examination of the corresponding dataset, this is primarily because haze tends to act smoothing effect image content rather than introducing additional degradation signals. As a result, hazy images may still be well represented by the prior of the LIC model, they resemble smooth images.
Comparison with Settorrationthee--oopreee propose IB -R-LUU and the restoration thhnn compression methods regarding rate rate-perception perfor- mances are provided in Fig. As shown, our scheme capable of achieving competitive perceptual quality compared to these cascaded approaches, while offering overall advantages in bpp reduc- tion-especially high bitrate regions. In particular, an interesting observation arises from the fact that both our IB-LRU and the cetorratioottee-cooopee LIC checkpoint. While both are capable of achieving satisfactory perceptual quality, they tend to result in significant increase in bpp. As previously discussed, the underlying reason is that although the restored images may align well with perceptual preferences, their distributions still diverge considerably from those of natural images due to the generative nature of the restoration models. Consequently, during entropy modeling, these distributions cannot be accurately estimated, leading to increased bitrate costs. Meanwhile, a better trade-off between rate and perception. To provide intuitive insight into the performance, a set of visual examples is presented in Fig.
Paradigm: Comparisons results between our
approaches are built upon the same frozen
method focuses unifying the latent distribution, achieving
To further demonstrate the flexxibiityy of our proposed IB-LRU scheme, we compare the number of additional parameters and inference speed with those of the restoration-thhe-coompsssstt paradigm.

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Input
TIC
TIC PrompiIR
Ours
AOSOOO
Oop 0.484
0.395
0.296
Jpp 0.489
bop
Aop
Figure 5: An intuitive illustration of compression performance under different degradation scenarios. The first, second, and third rows correspond to rain. noise, and haze settings. respectivelv.
27.0
2562
27.5
27.4
26.4
360
Z-1-2
27.5
pirt
273
270
0.5
6.18
0.25
DPP
119
(a) Haze
(b) Rain_
(c) Noise_ _2
Figure 6: Ablation results the IB constraint by varying its weight parameter 3.
The corresponding results, presented in Table 1 demonstrate our overwhelming advantages and highlight the strong potential of IB-LRU for real-world implementation.
4.3 Ablation Studies
This part specifically examines the effectiveness of the constraint and its influence on the compres- sion pipeline. In particular, the IB principle is employed to regularize the probabilistic prompt, guiding it to capture minimal yet sufficient degradation information. In this part, we retrain the entire scheme with varying values of B in Eq. (14) based on the TIC backbone, specifically {1 x 10 2, x 10 10-4,1 x 10-5,
10 10-?) -7 to examine the influence of the IB constraint. Fig. 6 illustrates the corresponding results, where observe that the optimal vate-perception performance is generally achieved when 33 is set to x 10 or 1 x 10-5, 5, whereas values that are too large or too small lead suboptimal results.
To gain deeper insight,
When 3 is too small (e.g., 1 x 10-"), the IB constraint becomes weak, allowing the prompt capture excessive redundant image context rather than distinct and clear degradation characteristics. This, in turn, compromises latent representation unification, well as compression efficiency and restoration quality.
Increasing 3 to 1 10-5 and 1 x 10-1 significantly improves rale-perception performance, as a moderate constraint effectively regularizes the prompt, guiding it to discard irrelevant information while retaining what is essential for adaptive restoration.

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When 3 becomes too large (e.g., x 10 10- the vate-perception performance begins to de- grade. This suggests that overly strong IB constraints may excessively compress the prompt. resulting in loss of useful information necessary for capturing degradation characteristics.
Conclusion
propose IB-LRU, a lightweight and modular scheme that enhances the robustness of Learned Im- age Compression (LIC) models under various real-world degradations. By leveraging a probabilistic prompt guided by the Information Bottleneck principle, our method constrains latent representations to retain task-relevant degradation information while suppressing redundancy. IB-LRU operates without altering the original LIC parameters and is compatible with multiple pre-trained backbones. Empirical results demonstrate consistent gains in compression and restoration performances across diverse degradation scenarios, with minimal additional computational cost. Our findings suggest that latent distribution alignment is promising direction for improving the generalization of LIC models in practical settings.
Acknowledgements
This work was in part by the Interdisciplinary Frontier Research Project of PCL under Grant 2025QYB013, in part by the Major Key Project of PCL (PCL2025A03), in part by Guangdong Basic and Applied Basic Research Foundation under Grant Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) under Grant No. GML-KF-24-27, in part by the Natural Science Foundation Guangdong Province under Grant 2023A1515011667.
in part by the Open
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A Supplementary Experiments and Analyses
A.1 Effectiveness of the Probabilistic Prompt Design
This section provides straightforward comparison between our probabilistic prompt and the existing factorized prompt design. Specifically, replace our probabilistic prompt with the factorized prompt from PrompuR, keep the Seature-modulate module unchanged, and retrain the plug-in on the TIC backbone. Table 2 reports PSNR (dB) at single compression level, where our methods deliver overall performance gains with significantly fewer parameters.
Table 2: Performance and parameter comparison between our proposed probabilistic prompt and the existing factorized prompt.
Prompt Type
Rain L. (bpp=0.23)
Haze (bpp=0.19)
Noise 50 (bpp=0.26)
Para. (M)
Probabilistic Factorized
28.01
26.53
25.93
4.6
27.95
26.39
25.76
16.0
We also assess generalization on unseen conditions, including a mixed haze-and-rain setting created by adding synthetic rain streaks to the SOTS haze test images, and the unseen DID de-raining dataset. Table shows that our method achieves stronger robustness in both cases.
Table 3: Comparison of generalization performance between probabilistic and factorized prompt designs regarding bpp/ PSNR(dB).
Prompt Type
Haze + Rain (Mixed)
Unseen Domain (DID)
Probabilistic
0.183 24.09
0.202 / 25.15
Factorized
0.207 19.24
0.214 / 22.93
A.2 Effectiveness the Information Bottleneck (IB) Constraint
We evaluate the impact of the Information Bottleneck by removing it entirely (8 = 0). As reported in Table 4 the absence of the IB constraint leads to uniform degradation performance, supporting its role in guiding the prompt to capture essential degradation features.
Table 4: Effect of removing the IB constraint (3 0) on vate-distortion (hpp / PSNR).
Version
Rain
Haze
Noise 25
Ours (Full Model)
0.235 / 28.01
0.191 26.53
0.289 27.51 0.289 26.99
Ablated (3
0.232 / 27.61
0.200 26.00
A.3 Comparison with Joint Denoising-Comprrssion Methods
We compare against the joint denoising-compresstie method of Brummer et al. 47 (JDC-CN), For fair comparison, we retrain both approaches the same TIC backbone using only the Gaussian noise split of our training set. As shown in Table 5 our method demonstrates a notable advantage even in this single-degradation setting.
Table 5: Comparison with joint denoising--oompressio method (bpp/PSNR(dB).
Method
Noise_15 15
Noise_25
Noise_50
Ours
0.271 / 28.23
0.294 28.07
0.245 26.53
JDC-CN
0.254 / 24.71
0.283 24.20
0.265 23.01
21


Reviews:
[{"review_id": "naUqsmRIDS", "reviewer": "Reviewer_Tuoa", "summary": "This 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.", "questions": "- 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?", "limitations": "No. 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- **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.", "was_revised": true, "final_justification": "Most of my concerns have been addressed in the rebuttal, and I appreciate the authors’ clarifications. However, after reading the exchange between the authors and other reviewers, I noticed one aspect that could still be improved.\n\nThe discussion around integrating restoration (e.g., deraining, dehazing) into compression could benefit from a more user-centric perspective. As reviewer d5Gs pointed out, degradations like rain or haze may be part of the scene that users intentionally capture. Automatically removing them during compression may not always align with user intent. While the authors justify this approach as a means to maintain a stable bitrate, a more desirable research direction could be to preserve the original content while still achieving efficient and stable compression.\n\nThat said, the paper is practically motivated and technically sound overall. I therefore maintain my original score of 4: Borderline Accept.", "rebuttal": "First of all, we would like to express our sincere appreciation for your positive attitude and constructive suggestions on our work. Your feedback is highly valuable to us and serves as a strong source of encouragement. We hope the following point-by-point responses successfully address your concerns, and we look forward to any further suggestions you may have.\n\nQ1: Limited diversity of degradation types\n\nAns.: Thanks again for your constructive suggestions. Accordingly, we first evaluate our method under a complex degradation scenario of combined haze and rain, which frequently occurs in real world applications. Specifically, we use the haze test set from our manuscript and add rain degradations using the approach from *\"Joint Rain Detection and Removal from a Single Image with Contextualized Deep Networks\"* (TPAMI 2019). Comparison results between ours and the employed restoration-then-compression anchor are shown below.\n\n||bpp/PSNR|\n|-|-|\n|Ours (TIC)|0.183/24.09|\n|Prompt+TIC|0.187/18.90|\n\nThese results clearly demonstrate the effectiveness of our method in complex scenarios.\n\nMoreover, to evaluate a different degradation type, we investigate the common low light condition. We incorporated the training set of LOLv2 *(Low-light image enhancement via structure modeling and guidance)* and LOD *(Crafting Object Detection in Very Low Light )* into our training stage and retrained our method. The testing results regarding bpp and PSNR(dB) are listed below.\n\n||bpp/PSNR|\t \n|-|-|\n|Ours (TIC)|0.138/20.33|\n|Prompt+TIC|0.132/18.71|\n\nDue to time and computational resource limitations, we will provide additional results on other domain specific degradations in our supplementary materials. We appreciate your understanding.\n\nQ2: Dependence on frozen LIC backbones\n\nAns.: Thanks for your kind suggestions. To comprehensively examine the effectiveness of relaxing the parameters of the backbone network, we incorporate two additional comparisons: a）Jointly training: We release the LIC backbone from the beginning and jointly train it with our proposed modules, keeping all other settings exactly the same. b) Finetuning, based on our current version, we released the parameters of the TIC backbone and fine tuned the entire framework for an additional 100 epochs. The comparison results on the compression level of bpp=0.191 are listed below.\n\n|PSNR(dB)|Haze|COCO|\n|-|-|-|\n|Ours|26.53|28.96|\n|Jointly|24.95|25.88|\nFinetuning|26.90|27.26|\n\nFirst, regarding the joint training scheme, it even leads to reduced performance. According to our analysis, this is mainly because jointly training from scratch disrupts the prior knowledge of pristine images contained in the LIC backbone, which in turn prevents our prompt from effectively decoupling the degradation information.  \n\nMoreover, finetuning the backbone can indeed improve performance on certain test cases. However, we would like to emphasize our motivation for adopting the plug-and-play strategy, as well as its necessity in practical deployment scenarios. 1) First, as noted in our manuscript, we take degraded images as input, which exhibit significantly different statistics from natural images. As a result, fine tuning on such inputs can degrade compression performance on pristine images. This outcome is unacceptable in real world scenarios, where most content consists of normal, undegraded scenes. In contrast, our probabilistic prompt design can effectively recognize undegraded inputs through the learned distribution parameters and adapt accordingly. 2) Second, the fine tuning dataset is typically much smaller than the original LIC training set, which increases the risk of overfitting and reduces generalization to other conditions. \n\nTo further validate this, we evaluated the performance on an additional pristine dataset COCO. The comparison results are listed in the table above. As shown, the fine-tuning strategy inevitably leads to a performance drop on normal scenes. Given these issues, recent works across a wide range of research domains increasingly favor plug and play approaches over backbone fine tuning.\n\nQ3 Discussion on the impact of different prompt dimensionalities (d) on performance\n\nAns.: Thank you for your constructive suggestions, which are indeed necessary and have helped improve the quality of our manuscript. Since our proposed probabilistic prompt is designed to capture discriminative characteristics of different degradations, we chose the prompt size based on a more straightforward and intuitive evaluation using t-SNE visualizations, rather than solely focusing on final compression performance (An example of t-SNE plot is shown in Fig. 1 (f) of our manuscript). We tested a range of prompt sizes *d*=64, 128, 256, 512 and selected *d*=128 as it already yielded satisfactory representation. Increasing the size further brought minimal improvement while significantly increasing the model’s parameter scale. For intuitive demonstration, we list the parameter scale corresponding to different choices of d below.\n\n|*d*|64|128|256|512|\n|-|-|-|-|-|\nPar.#|4.2M|4.6M|6.2M|12.0M|\n\nDue to the rebuttal constraint, we are not capable of improve the visualize result but we will incorporate to our revised manuscript as an ablation study, thanks again for your suggestions.\n\nQ4.: Guidance on tuning the IB constraint weight for unseen degradations\n\nAns.: Thanks a lot for your kind suggestions. First, we would like to clarify that our scheme relies on the training process to capture the distribution of different degradation types. From a theoretical standpoint, our method is not capable of being directly applied to degradation scenarios whose patterns differ significantly from those in the training set. However, our lightweight design offers strong flexibility and can be easily adapted to unseen scenarios by expanding the training set, with limited computational overhead. As shown in the experimental results provided in response to your first question, our method achieves satisfactory performance in low-light scenes after only 20 epochs of training (less than 2 GPU Hours). In the context of incorporating new degradation types, the IB constraint needs to be relaxed. Specifically, by using a smaller β value, since the prompt is expected to capture more information to represent the added complexity.\n\nMoreover, in simpler cases, such as complex degradations composed of combinations of seen degradation types, or unseen intensities within known types, our method can be directly applied without retraining. This is demonstrated in the haze+rain scenario shown in the experimental results provided in response to your first question, as well as in the test results on the RAIN test set in our manuscript, where the rain degradation intensity spans a wide range. In this context, there is no need to adjust the IB constraint."}, {"review_id": "pDtWNiBKFm", "reviewer": "Reviewer_d5Gs", "summary": "The 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.", "questions": "Please answer the questions in the above weaknesses.", "limitations": "The authors do not discuss the limitations and potential negative societal impact of their work.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: 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.", "was_revised": true, "final_justification": "Thank you for the authors' response. However, I remain unconvinced.\n\n1) I still hold my original view: rain and fog are natural phenomena and should be preserved as valid information—even in surveillance scenarios. Regarding the authors' claim that such degradations lead to unstable bitrate, the proper solution is not to remove rain or fog, but to design image encoders that maintain stable bitrate even under such conditions (while preserving rain and fog in the reconstructed image). As for machine analysis tasks, the authors did not include any relevant experiments in the main paper. Instead, all comparisons are based on PSNR-measured rate-distortion performance. This work is not a \"image coding for machine\" approach. The few additional experiments added in the rebuttal lack sufficient depth and credibility, and they are not well integrated into the existing narrative of the paper.\n2) Although the authors highlight some differences from prior works [a, b, c], these differences do not represent fundamental improvements. Therefore, the novelty of this work remains insufficient.\n3) While the authors have supplemented a few comparison experiments with joint denoising-and-compression methods in the rebuttal, the overall experimental setup in the paper still primarily compares with the unfair \"restoration-then-compression\" paradigm. Performing substantial revisions would go beyond the scope allowed for conference papers.\n4) The authors have added some comparisons with other fine-tuning strategies, but these experiments are still too limited in scope and depth.\n\n\nIn summary, the motivation and novelty of this work are unconvincing, and the experimental setup is not fair. Although the authors have attempted to address concerns in the rebuttal, the supplementary data are insufficient, and making substantial revisions to the paper would not be appropriate under the constraints of a conference submission. I maintain my decision to reject the paper and recommend that the authors thoroughly revise it for submission to another conference.", "rebuttal": "Q1: Concerns reading the necessity of combining image restoration with image compression\n\nAns.: Thanks for your comments. In response to your concern, we’d like to summarize the motivation and necessity of developing the joint restoration-compression framework as following: \n\n1)**Maintain stable bitrates for degraded scenarios:** LIC models are trained on pristine images. Compressing degraded inputs such as rainy or noisy scenes therefore often requires a significantly higher bitrate as their statistics differ substantially from those of the training data (as shown in Fig. 1 of our manuscript).  In real time transmission scenarios, such as surveillance, maintaining a stable bitstream is crucial. Fluctuations caused by environmental degradations can lead to network congestion, packet loss, and even the loss of critical frames. By integrating lightweight restoration into compression, our method keeps the bitrate within the expected range. \n\n2)**Necessity for machine vision-oriented scenarios:** Rain and haze severely degrade the accuracy of downstream vision tasks. To demonstrate this, we evaluated Fast R-CNN on real-world traffic scene object detection using the DAWN dataset (*DAWN: Vehicle Detection in Adverse Weather Nature*). Specifically, we selected subsets containing fog (300 images) and rain (200 images) degradations for evaluation.\n\n|Rain|bpp|mAP50|\n|-|-|-|\n|Ours (TIC)|0.177|35.25%|\n|PromptIR+TIC|0.182|30.33%|\n|TIC|0.209|28.11%|\n\n|Fog|bpp|mAP50|\n|-|-|-|\n|Ours (TIC)|0.116|33.62%|\n|PromptIR+TIC|0.124|25.23%|\n|TIC|0.100|21.92%|\n\nAs shown in above table, our joint framework achieves a better rate–analytics performance than both vanilla LIC and a sequential restoration then compression pipeline. Moreover, our joint strategy with its lightweight design offers clear coding latency advantages over the two stage pipeline (see Table 1 of our manuscript), which is crucial for real world deployment.\n\n3)**User controlled flexibility:** Moreover, we acknowledge that in some applications users may wish to preserve rain or haze as part of the scene content. Owing to our plug and play design, users who prefer a fidelity-driven compression process can disable the restoration module and employ the original LIC model without modification.\n\nQ2. Concerns about the novelty over existing prompt-tuning methods for domain and task adaptation in image compression\n\nAns. Thanks for your recommendation of all of these brilliant works, that all involves in deploying plug-and-play modules at the LIC backbones for domain or task adaptation. After a comprehensive study of their papers and open-sourced code, we believe our work demonstrates significant differences and novelty in terms of technical design, theoretical foundation, and implementation flexibility. Specifically,\n\n1)**Task/domain specific *vs*. generalized:** Our IB-LRU scheme is especially designed to handles multiple degradation types in a single training process. By contrast, methods in [a] and [b] insert task-specific modules that must be trained separately for each target, after a comprehensive study of their open-source code. In particular, although these methods demonstrate effectiveness across multiple tasks, each downstream task requires its **own prompt**, a **separate compression pass**, and an **individual bitstream**. Meanwhile, [c] employs an online optimization strategy that uses gradient information from the decoder to refine the latent representation during each image’s test time encoding. This incurs unpredictable latency and requires the encoder and decoder to be co-located, which is impractical for many deployments. For intuitive demonstration, we evaluate its adaptation capacity using the H_Rain test set. Specifically, we use its open-source checkpoint to compress paired pristine and rainy images. The resulting bpp, fidelity performance, and inference speed (s) are listed below.\n\n|bpp(Clean)|bpp(Rain)|PSNR(Clean)|PSNR(Rain)|Inf.speed ([c])|Inf. speed (Ours)|\n|-|-|-|-|-|-|\n|0.274|0.521|29.83|27.41|283s|0.103s|\n\t\t\t\t\t\nAs shown, although [c] incorporates an online optimization strategy at the cost of significantly increased coding latency, it still struggles with open-world degradations, resulting in a substantial increase in bpp and a noticeable loss in fidelity.\n\n2)**Encoder only design:** Works [a], [b], and [c] modify both encoder and decoder. Meanwhile, we only alter the encoder, leaving the decoder unchanged. This design allows our module to be dropped into existing LIC systems without any changes on the client or server side, which is critical for real world adoption. \n\n3)**Information Theoretic Prompt Design:** We emphasize our theoretical contributions in designing the probabilistic prompt and the IB constraint. Unlike existing works, which rely on empirical heuristics, our prompt is grounded in the information bottleneck principle: it encodes a minimal sufficient statistic of the degradation given the image content, then we devote plenty of work to derive variational bounds to realize this design in practice.\nTo illustrate the benefit of our design, we straightforwardly replaced our PPG module with the commonly employed factorized prompt (PromptIR) and retrained it using the R-D criterion. As shown in below table, our probabilistic prompt with the IB constraint outperforms the factorized design in both perceptual quality and computational complexity.\n\n|PSNR|Rain_L(bpp=0.235)|Haze(bpp=0.191)|Noise_50(bpp=0.261)|Para.(M)|\n|-|-|-|-|-|\n|Probabilistic(Ours)|28.01|26.53|25.93|4.6|\n|Factorized|27.95|26.39|25.76|16.0|\n\nQ3. Comparing with other joint denoising and compression works.\n\nAns.: Thanks for your suggestion. As [e]’s source code has not been open sourced, we provide comparison results with [d]. Specifically, since [d] focuses solely on denoising, we retrained both our scheme and [d] (*JDC-CN* version) using the same LIC backbone TIC and the same training set (containing only Gaussian noise). The comparison results regarding the bpp and PSNR (dB)  on the Gaussian noise test set are shown below.\n\n|bpp/PSNR|Noise_15|Noise_25|Noise_50|\n|-|-|-|-|\nOurs|0.271/28.23|0.294/28.07|0.245/26.53|\n[d]|0.254/24.71|0.283/24.20|0.265/23.01|\n\nAs shown, even under single-noise settings, our method still demonstrates notable advantages. This is expected, as [d] primarily focuses on a new training strategy and supervision terms while building upon existing LIC models. While it makes a valuable contribution, its primary goal is methodological insight rather than pushing performance to the SOTA.\n\nQ4. Comparing with other fine-tuning strategies.\n\nAns.: Thanks your suggestion. Due to time and computational resource limitations, we were only able to implement the most recent method, LoRA. For a fair comparison, we applied LoRA (*lora_rank*=16) only on the encoder side of the same LIC backbone checkpoint as ours (TIC), and trained the LoRA module on the same dataset used in our method. The testing results regrading bpp and PSNR (dB) are shown below,\n\n|bpp/PSNR|Haze|Rain_L|Noise_50|\n|-|-|-|-|\nLora(TIC)|0.175/24.12|0.238/26.47|0.283/23.89|\n|Ours(TIC)|0.191/26.53|0.235/28.01|0.261/25.93|\n\nAs shown, although LoRA achieves strong performance when adapting to a specific task or domain, it is not designed to generalize across multiple degradation types within a single training process. In contrast, our PPG and IB constraint are specifically designed for multi-degradation scenarios, offering notable improvements and novelty beyond existing works, while also being supported by a solid information-theoretic foundation."}, {"review_id": "ATo2sOZzJP", "reviewer": "Reviewer_oQak", "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.", "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.\n- 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.\n- 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "### Strength\n\n- The proposed framework is unified for different degradation images.\n- The plug-and play module can be used for different LIC backbones.\n\n### Weakness\n\n- The proposed module, PPG and DAGM seems to have little difference with PromptIR, which is lack of novelty.\n- The results are worse than MOCE-IR in some cases.", "was_revised": true, "final_justification": "Considering the technical reliability and practical performance, I raise my score to 4 Borderline accept.", "rebuttal": "Q1: Concern about the novelty of PPG and DAGM compared to PromptIR\n\nAns.: Thanks for your comments. We realize that your concern may stem from the lack of a direct comparison between our prompt learning strategy and PromptIR. To demonstrate the benefit of our design, we replaced our PPG module with the PromptIR and retrained it under the R-D criterion using identical hyperparameters for the LIC backbone (TIC). The comparsion result at the same compression level and the prompt sizes are presented below, where our approach shows a clear overall improvement.\n|PSNR|Rain_L(bpp=0.235)|Haze(bpp=0.191)|Noise_50(bpp=0.261)|Para.(M)|\n|-|-|-|-|-|\n|Probabilistic (Ours)|28.01|26.53|25.93|4.6|\n|Factorized|27.95|26.39|25.76|16.0|\n\nMoreover, as noted in our manuscript, PromptIR is the most closely related work to ours. Our key contribution is to further improve upon it in prompt design, learning strategy, and information theoretic foundation.\n\nTo address your concern, we highlight our differences and enhancements over PromptIR in the following aspects: 1) *Factorized vs. probabilistic design*: PromptIR employs a set of factorized vectors to capture the characteristics of different degradation types. In contrast, our VAE style prompt generator models each degradation as the parameters of a unified multivariate Gaussian distribution. This fundamental shift lets us represent degradation characteristics with a much smaller prompt size, as shown in the table above. 2) *Training strategy*: PromptIR applies no specific constraints or supervision during prompt learning, and its naive prompt design hinders the learning of clean, discriminative degradation representations. By contrast, we employ the information bottleneck principle to impose a principled constraint on prompt learning, which enhances the prompt’s capacity to capture degradation characteristics. An intuitive comparison is shown in Fig. 1(e) and (f). 3) *Information-theoretic foundation*: Unlike PromptIR, which relies on empirical heuristics, our prompt is grounded in the information bottleneck principle. We design and optimize the probabilistic prompt according to a clear theoretical criterion: it encodes a minimal sufficient statistic of the degradation signal given the image content. To realize this design in practice, we investigate mutual information as a mathematical tool and derive variational bounds for efficient implementation.\n\nQ2. The results are worse than MOCE-IR in some cases. \n\nAns.: Thanks for your comment. Indeed, MOCE-IR is a powerful “many-in-one” image restorer and delivers outstanding restoration results. However, in five of six tested degradation scenarios, our joint framework still outperforms the MOCE-IR. Moreover, as shown in Table 1 of our manuscript, our lightweight plug in offers overwhelming advantages in parameter scale (ours: 4.6 M *vs.* MOCE-IR:25.35 M) and inference speed (ours: 109.5 ms *vs.* MOCE-IR: 483.3 ms), which is critical for real world deployment.\n\nQ3. Concern about whether fine-tuning the LIC model on restored images would outperform the proposed method.\n\nAns.: Thanks for your suggestions. It is true that fine tuning the LIC backbone on restored images can boost performance in certain test scenarios. However, this approach raises two key concerns. First, as noted in our manuscript, restored images exhibit different statistics from natural images. Fine tuning on them can therefore degrade compression performance on pristine inputs. Second, the fine tuning dataset is usually much smaller than the original LIC training set, which risks overfitting and poorer generalization to other scenarios.\n\nTo illustrate these effects, we fine tuned the TIC backbone for 100 epochs using images restored by PromptIR. We then evaluated this model on (1) the Rain_H set from our manuscript and (2) a pristine image dataset RAISE1K (*RAISE: a raw images dataset for digital image forensics*) to assess its performance on normal real world inputs.\n|bpp/PSNR|Rain_L|Raise1K|\n|-|-|-|\n|Ours|0.235/28.01|0.187/31.61|\n|Finetuned|0.223/28.39|0.185/30.42|\n\nAs shown, fine tuning the LIC backbone yields only marginal gains on our test set while inevitably causing overfitting and degrading compression performance on normal images.\n\nMoreover, in real world deployment scenarios, the training loss hyperparameters or even the training script may not be accessible, making the fine tuning strategy impractical. \n\nQ4. Concern about the higher bpp of the proposed method in low bitrate regions.\n\nAns.: According to our analysis, in very low bitrate regions the LIC backbone must discard much of the high frequency content to meet the rate target, which leads to overly smooth reconstructions. In contrast, our method gradually adapts the compressed features to align with the pristine image distribution, restoring richer high frequency details. Encoding these additional restoration cues inevitably requires extra bits, which explains the modest bpp increase at the lowest rates. Thanks again for your comprehensive review in further improving the quality of our manuscript.  We will revise corresponding statement and include this explanation around line 275 in the updated manuscript to improve its accuracy.  \n\nQ5. Incorporate more comparison results regarding perception-oriented metrics\n\nAns.: Thanks for your valuable suggestion. Accordingly, we have provided partial results comparing our method with other baselines regarding LPIPS and FID on haze test set, as shown below. \n\n|LPIPS|bpp=0.143|bpp =0.191|bpp =0.228|bpp=0.243|\n|-|-|-|-|-|\nOurs(TIC)|0.1047|0.0910|0.0856|0.0830|\n|PromptIR+TIC|0.1112|0.1016|0.0954|0.0941|\n|AirNet+TIC|0.1283|0.1016|0.0987|0.0977|\n|MOCE-IR+TIC|0.1230|0.0952|0.0901|0.0867|\n\t\t\t\t\n|FID|bpp=0.143|bpp=0.191|bpp=0.228|bpp=0.243|\n|-|-|-|-|-|\n|Ours(TIC)|76.98|65.54|62.76|60.10|\n|PromptIR+TIC|92.49|68.97|65.14|60.00|\n|AirNet+TIC|90.32|72.47|65.12|63.16|\n|MOCE-IR+TIC|88.74|70.36|64.30|61.28|\n\nAs shown in above table, Regarding both LPIPS and FID, our method also shown an constant advantages against the employed anchors. \nSince we cannot include full rate-perception curves in the rebuttal, we report only the results on *haze* test set here. The comprehensive comparison results would be provided in the supplementary materials of our revised manuscript. We appreciate your understanding."}, {"review_id": "YsMzI6sbtF", "reviewer": "Reviewer_p8XG", "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.", "questions": "see the Weaknesses", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n(1) The article presents its content clearly and understandably.\n(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.\n(3) Employing Information Bottleneck theory for prompt design demonstrates significant novelty.\n\nWeaknesses:\n(1) A performance comparison between the proposed prompt scheme and existing prompt schemes is not yet provided in the paper.\n(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?", "was_revised": true, "final_justification": "The author has addressed all my concerns, and I am willing to increase the score.", "rebuttal": "First of all, we would like to express our sincere appreciation for your responsible and valuable review. Your positive attitude and recognition of our work are a great encouragement to us. We will carefully consider all of your suggestions and do our best to incorporate them into our revised manuscript to further improve its quality.\n\nQ1. Lacking of comparisons with existing prompt schemes.\n\nAns.: Thank you for your valuable suggestions. A direct comparison with existing factorized prompt schemes is indeed necessary and important. As noted in our manuscript, PromptIR is the most closely related work to ours. Our key contribution lies in further improving prompt design, learning strategy, and the underlying information-theoretic foundation. To this end, we replaced our PPG and DAGM modules with PromptIR’s prompt module and retrained the model under the rate–distortion criterion, using identical hyperparameters for the LIC backbone (TIC). The comparsion performance at the same bpp level and the corresponding plugin module sizes are shown below, where our method shows clear overall improvement.\n\n|PSNR|Rain_L(bpp=0.235)|Haze(bpp=0.191)|Noise_50(bpp=0.261)|Para.(M)|\n|-|-|-|-|-|\n|Probabilistic (Ours)|28.01|26.53|25.93|4.6|\n|Factorized|27.95|26.39|25.76|16.0|\n\nDue to the rebuttal limitations, we are unable to present the full bpp–PSNR curves here. However, we will include comprehensive results and ablation studies in the revised manuscript. We appreciate your understanding.\n\nQ2. Questioning whether the performance gain is mainly due to the prompt design rather than the IB loss.\n\nAns.: Thanks a lot for you valuable suggestion.  Herein, we would first like to clarify that the ablation studies in Fig. 6 investigate the influence of different β values from 1e-7 to 1e-2, which reflect the strength of the IB constraint.  While β=1e−7 corresponds to a weak IB constraint, it still provides a non-negligible regularization effect. Thanks again for your comment, which made us realize the necessity of including an additional ablation study that completely removes the IB constraint, *i.e.*, β=0. Herein, we provide comparison results between our full version and the ablated version as shown below. \n\n|bpp/PSNR|Rain_L|Haze|Noise_25|\n|-|-|-|-|\n|Ours|0.235/28.01|0.191/26.53|0.289/27.51|\n|Ablated|0.232/27.61|0.200/26.00|0.289/26.99|\n\nAs shown, completely discarding the IB constraint leads to a notable performance drop. We will incorporate the complete results into the ablation studies section of our revised manuscript.\n\nMoreover, we would like to emphasize that our probabilistic prompt design is tightly coupled with the IB constraint. Specifically, the IB constraint requires estimating mutual information, and our probabilistic prompt represents degradations as a multivariate Gaussian distribution, which enables variational estimation of mutual information via the KL divergence."}]

Author rebuttal:

Meta-review: This paper received mixed initial ratings (1 accept, 2 borderline accepts, 1 reject). There was general appreciation for the problem motivation, technical soundness and overall performance and versatility of the proposed approach in achieving bitrate reduction.

There were several concerns raised -- including potential lack of generalization and comparisons with existing methods --- most of which were addressed by the authors.

There remained a lingering concern around the proposed method "forcibly" removing potential sources of image degradation (e.g., rain). The authors did clarify though that their approach has a flexible option to perform deraining or to keep these cues, depending on task requirements.

There was significant discussion between the authors and reviewers, and also among the reviewers and the AC post rebuttal. While a consensus was not reached, after considering all the points, on balance, an accept decision was reached. 

The authors are urged to carefully consider the reviewer comments (especially those from Reviewer d5Gs) when preparing the camera-ready version.
Decision: Accept (poster)