Content-Aware Mamba for Learned Image Compression
Authors: Yunuo Chen
Zezheng Lyu
Bing He
Hongwei Hu
Qi Wang
Yuan Tian
Li Song
Wenjun Zhang
Guo Lu
Venue: ICLR 2026 Poster (2026)
Keywords: Learned Image Compression
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose Content-Aware Mamba for learned image compression, outperforming previous Mamba-based LIC models.

Abstract: Recent learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global receptive fields with linear complexity. However, the standard Mamba adopts content-agnostic, predefined raster (or multi-directional) scans under strict causality. This rigidity hinders its ability to effectively eliminate redundancy between tokens that are content-correlated but spatially distant.
We introduce Content-Aware Mamba (CAM), an SSM that dynamically adapts its processing to the image content.
Specifically, CAM overcomes prior limitations with two novel mechanisms. First, it replaces the rigid scan with a content-adaptive token permutation strategy to prioritize interactions between content-similar tokens regardless of their location. Second, it overcomes the sequential dependency by injecting sample-specific global priors into the state-space model, which effectively mitigates the strict causality without multi-directional scans.
These innovations enable CAM to better capture global redundancy while preserving computational efficiency. Our Content-Aware Mamba-based LIC model (CMIC) achieves state-of-the-art rate-distortion performance, surpassing VTM-21.0 by 15.91\%, 21.34\%, and 17.58\% in BD-rate on the Kodak, Tecnick, and CLIC datasets, respectively. Code will be released at https://github.com/UnoC-727/CMIC.

Published as a conference paper at ICLR 2026
CONTENT-AWARE MAMBA FOR LEARNED IMAGE COM- PRESSION
Yunuo Chen'
Zezheng Lyu² Bing He
Hongwei Hu Qi Wang"
Yuan Tian Li Song' Wenjun Zhang'
Guo Lu 1x
1 SShnggaai Jiao Tong University Maasaacuseets Institute of Technology
3 Alibaba Group *Shanghai AI Laboratory
ABSTRACT
Recent learned image compression (LIC) leverages Mamba-style state-space mod- els (SSMs) for glohal receptive fields with linear complexity. However, the standard Mamba adopts content-agnostic predefined raster (or multi-directional) scans under strict causality. This rigidity hinders its ability to effectively eliminate redundancy between tokens that are content-correlated but spatially distant. We introduce Content-Aware Mamba (CAM), an SSM that dynamically adapts its processing the image content. Specifically, CAM overcomes prior limitations with two novel mechanisms. First, it replaces the rigid scan with content-adaption token permutation strategy to prioritize interactions between content-similar tokens re- gardless of their location. Second, it overcomes the sequential dependency by injecting sample-specine mitigates the strict causality without multi-directional scans. These innovations enable CAM to better capture global redundancy while preserving computational efficiency. Our Content-Aware Mamba- based LIC model (CMIC) achieves state- of-the-art rate-distortion performance, surpassing VTM-21 10 by 15.91 %, 21 13,4%, and 17.58% in BD-rate on the Kodak, Tecnick, and CLIC datasets, respectively. Code will be released at httpt:p::/// /ttp//gtttt/tt////////////. com/ T
global priors into the state-space model, which effectively
INTRODUCTION
Image compression plays vital role in computer vision and multimedia by reducing storage and bandwidth while preserving visual quality. Learned image compression (LIC) has advanced rapidly
with end-to-end training and differentiable loss functions (Ballé al. 2017 Ballè et al. 2018
Du et al. 2025 Li et al. 2025b). Early LIC methods used convolutional neura networks (CNNs)
for nonlinear analysis and synthesis transforms (Minnen al., 2018 He et al. 2022). Recently, transformer-batt based designs expanded receptive fields to capture long-range dependencies (Zou et al.
2022, Zhu et al. 2022 Liu et al. 2023] Li et al. 2024a). However, transformers achieve global receptive field at the cost of quadratic time complexity. State-space models (Gu & Dao 2023 |023 Zhu
cct al., 2024) offer a compelling alternative: they couple global receptive fields with linear complexity
and have already been adopted in several LIC models (Qin et al. 2024 Zeng et al. 2025).
Yet, Mamba's 's Selective Scan mechanism, originally conceived for one-dimensional sequences, faces two fundamental challenges when adapted for image compression. First, its rigid, content-agnostic scanning order hinders effective redundancy removal. Vanilla Mamba processes tokens based on their fixed spatial proximity (i.e., raster scan), failing to account for feature correlation and content similarities. Effective compression, however, relies on capturing dependencies between semantically related regions, which may be spatially distant. By separating content-corry correlated tokens while grouping unrelated ones, the fixed scan path weakens Mamba's 's ability to model dependencies between tokens that are distant in Euclidean space but close feature space, leading to suboptimal distoritoo (RD) performance. Second, Mamba is a causal sequence model, and the strict causality is misaligned with the non-causal nature images. For SSM, when an image is scanned into sequence, each token is processed based only on the preceding tokens in the raster-scan order. Since a token can only access information from its predecessors, the model ignores the
"Corresponding Author

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Published as a conference paper at ICLR 2026
Dlace
(a) Content-Agnosta Scan
(b) Content-Adupiin Scan
(c) Rate Savings relative to VTM-21.0
Figure (a) Illustration of standard 2D Selective Scan. (b) Illustration of our addative
scan: correltcoreel tokens are scanned consecutively to better eliminate redundancy. (c) Rate savings relative to VTM-21.0 .0 on the Tecnick dataset. CMIC significantly outperforms two SOTA Mamba-based LIC models: MambaVC (Qin et al. 2024) and MambalC Zeng et al. 2025).
context of subsequent tokens. While multi-directione scanning (Liu et al. 2024a Guo et al. 2024b) common solution, this approach quadruples computational complexity and still relies on the content-agnostic, Euclidean- based paths (Fig. [ These limitations collectively reduce the model's ability to capture global dependencies, which are critical for tigh-performance image compression, highlighting the need for more flexible and adaptive scanning mechanism.
To overcome these limitations and enhance the Aware Mamba, a dynamic SSM designed specifically for compression. First, CAM addresses the content-agnostic fixed scan by prioritizing interactions between content-correlated tokens. It achieves this by clustering latent tokens and reorganizing the token sequence to group similar tokens together. This ensures that tokens are processed based on their feature-space proximity rather than their original spatial arrangement. By allowing the scan to follow paths of high content similarity, empower Mamba to capture long-range dependencies more effectively while maintaining its linear complexity.
of Mamba, we introduce Content-
Second, to mitigate Mamba's inherent causality without the cost of introduce a prompting mechanism. We modulate the state-space representation by injecting sample- specific prompts constructed from clustering results and a distribution dictionary. This prompt encodes global statistics to modulate SSM's hidden states, allowing information from the entire image influence the sequence modeling process at every step. Consequently, information retention is no longer determined solely by preceding tokens but is also guided by the global priors. This approach effectively mitigates the strict causal chain without quadrupling the computational load.
scanning,
These two strategies enable efficient, content-aware, and non-causal long-range modeling. Built upon this foundation, our Content- Mamba- based LIC model, CMIC, achieves state-of-the-ar (SOTA) RD performance, surpassing the traditional codec VTM-21.0 by 15.91% %, 21.34%, and 17.58% in BD-rate on the Kodak, Tecnick, and CLIC datasets. As illustrated in Fig. (c). our model also demonstrates a significant performance gain over recent advanced Mamba-based LIC models.
Our main contributions can be summarized as:
Cantent-Adaptive Token Permutation. We propose a novel token permutation mechanism that reorders the scan sequence based feature similarity. This prioritizes feature-space proximity over Euclidean spatial adjacency, significantly strengthening Mamba's 's ability to capture long-range redundancy.
Global-Prior Prompting. We introduce redundancy- prompt dictionary that con- ditions the SSM on sample-specific global priors. This approach relaxes Mamba's strict causality and enhances global modeling capabilities with minimal computational overhead.
End-to-End CMIC Model. We build CMIC, content-aware Mamba- LIC model that surpasses previous Mamba-based LIC models in both RD performance and efficiency.
RELATED WORK
2.1 LEARNED IMAGE COMPRESSION
End-to-end learned image compression (LIC) has rapidly progressed recent years. The original
CNN-based method by (Ballé et al. 2017) and its extension that couples VAEs with hyper-priors
(Ballé et al., 2018) laid the groundwork for LIC. Subsequent studies have mainly advanced RD
performance by: stronger maalsiss-syttheiis transforms and more expressive entropy models (Zafari
et al.) 2023 Mentzer et al. 2022 Ma et al. 2019 Lu et al.) 2022 Zhang et al. 2023 Gao et al. 202T
Fu et al. 2023 Bégaint al. 2020 Li 2025a Liang et al. 2025 Chen al. 2025b).
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Early CNN variants introduced residual blocks (Cheng et al. 2020) and octave residual modules (Chen et al., 2022; Fu & Liang 2023), while et al. (2021 1) explored invertible architectures. INR- based methods have also been explored (Kim et al. 2024 Leguay et al. 2023). Transformer- based architectures have surged in LIC research, including Transformer variants (Zou et al. 2022 Wang & [ing. 2024), hybrid TONN-Tranffrrmrr schemes (Liu et al. 2023) and frequency-awan window attention mechanisms (Li et al. 2024a). Recently, researchers have employed linear attention models, such as state-space model for the non-linear transform networks balance the RD performance and model complexity (Qin et al. 2024; Zeng et al., 2025 Wu et al. 2025). Recently, SegPIC (Liu et al. 2024b) uses semantic masks and dynamic CNNs to achieve region-adapiin transforms. Zhang et al. (2024b) employs clustering scheme to rearrange the feature map, subsequently applying CNNs to the rearranged features. In terms of entropy models, advances include the autoregressive model by Minnen et al. (2018), checkerboard modeling (He et al., (002). channel-wise models (Minnen & Singh 2020). and the space-channel context model of He et al. (2022). More works propose quadtree hierarchies (Li ct al.] 2022b] 2023), Transformer-basee entropy models (Qian et al., 2022).
and multi-reference schemes (Jiang et al. 2023 Jiang & Wang 2023). T-CA enhances channel
autoregression with transformers (Li al. 2024a).
2.2 STATE-SPACE MODELS
State-space models (SSMs) were first developed in control theory (Kalman 1960). They are now popular in deep learning as their sequential updates cost only linear time in sequence length, which is
ideal for modeling long-range relationships (Gu et al. 2021
Smith et al.
2022 Fu et al. 2022).
The newest breakthrough is Mamba (Gu & Dao 2023
Dao & Gul 2024). Researchers have already
applied Mamba beyond language to vision tasks Zhu et al. 2024 Liu et al. [0224a] Yue et al. 2024
Li al. 2024b). Some works focus on low-level tasks: Mambaik (Guo et al. 2024b) tackles local-
pixel forgetting and channel redundancy of Mamba while applying it to single-image super-resolution
FreqMamba (Zhen et al.) 2024) operates in the Fourier domain for deraining; MambaLLIE (Weng
et al. 2024) modifies the state-space equation for low-light enhancement; and further adaptations
address dehazing, deblurring, rain removal, and fusion tasks (Zheng & Wu 2024 Gao et al. 2024
Wu et al.) 2024; Xie et al.] 2024] Qiao et al. 2024). Recently, SSMs are also adopted by learned
image compression models such as Qin et al.
2024 and Zeng et al. (2025).
2.3
CONTENT-ADAPTIVE IMAGE COMPRESSION
Content-adaptive processing is widely recognized as an effective way improve the complexity- performance trade-off in vision models. For example, Routing Transformer (Roy et al., (021) and Adaptive Token Dictionary (Zhang al., 2024a) realize content-adaptive sparse attention, enabling stronger global receptive fields. In learned image compression, adaptivity has also been actively explored. [i et al.] (2022a); Wang et al. (2023); Fu et al. (2024a); Peong et al. (2024) introduce deformable CNNs and Tang et al. (2022); ; Yang et al. (202)); 1); Spadaro et al. (2024) employ Graph Neural Networks (GNNs) to achieve adaptive compression. Zhang et al. (002bb) further enhance CNN-based LIC with coarse, grid-anchored clustering scheme.
Our work aims to make Mamba content-adaptive while preserving its linear-time global modeling, In Mamba- based compression, the key issue is not the Transformer-style complexity-globality trade-off, but that content-agnostic, raster-order scanning poorly matches the redundancy structure among tokens. To address this, we introduce fine-grained, Eunluuiide clustering strategy tailored for Mamba-based compression. More detailed clustering comparisons are provided in Appendix A.2
METHOD
3.1 PRELIMINARY
Fig. (a) illustrates the pipeline of our content-aware image compression model, which primarily
follows standard VAE layout and comprises non non-linear transform networks and an entropy model.
The analysis transform ( ; O.)) encodes an input RGB image æ into latent features y = ga ga O.). where 0 refers to the learned parameters. These features are discretized by uniform quantization Q.
ÿ = Q μ1) + μa, where the mean parameter μ is provided by the entropy model. The synthesis transform g, : O.)) then reconstructs the image & (r: 0.).
The hyper-encode (:: Qa) maps y to side information x, quantized to &. The hyper-decoder (pp(:: (2) predicts mean and scale parameters (μt, rr together with the spatial-chame channel context 4p
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CAM
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(a) Overall Architecture of CMIC
(b) Contant- Aware Mamba (CAM) Block
Figure 2: Overview of the Proposed Method. (a) The CMIC framework. Feature dimensions are set as | IC,, C2 C3. C. and the six non-linear transform stages have depths { (L,, L2, L3. L3. L2, L J. Panel (b) shows the Content-Aware Mamba block, detailing the Content-Aware SSM architecture.
Numbers 1-9 represent the indices of tokens, while letters a-d denote distinct cluster categories.
they define a conditional Gaussian P1 μ., o, () used for entropy coding and the bitrate term R.
z = hpa (y: 2o); = Q=2), ,r a = hpp (.).
R = B log2 Po(0 p. [,,) + E log, Ps(
The network is optimized via the classic rate- distooriooinnn loss LRD. where d denotes image distortion:
PRD = R + X )d(xx
3.2 OVERVIEW OF CONTENT- AWARE MAMBA-BASED LIC
First, we give a quick overview of how our model works with Content-Aware Mamba blocks. We split its non-linear transform into six stages based on feature resolution. For each stage, inspired by Liu et al. 2: Li et al. (2024a), we first utilize window-attention to capture fine-gratd fine-grained local dependencies, while our proposed CAM blocks are introduced to enhance long-range modeling,
As shown in Fig. 2 (b), the core part of CAM block is a Content- Aware SSM. first clusters tokens into several categories, and per- mutes the flattened token sequence to bring similar tokens into prox- imity, enhancing local coherence in the token sequence. Meanwhile, sample-specific prompts are generated from the prompt dictionary. The reordered tokens, infused with these prompts, are processed by 1-D selective scan for efficient long-range modeling. Our ef- ficient entropy model is built upon the SCCTX model (He et al. 2022). As illustrated in Fig. 3 we incorporate depthwise convolu- tion for context modeling and employ gated MLPs for parameter aggregation. More details and ablations are provided in Appendix A.3 We next detail two key components for Content-Aware SSM: Content-Adaptive Token Permutation and Global- Prompting.
Concaleralee
Parsm Anggagaiin
Figure 3: Our Entropy Model
3.3
CONTENT-ADAPTIVE TOKEN PERMUTATION
A key limitation of the standard Mamba model for image compression is its fixed raster-scan sequence, which processes tokens in a predetermined spatial raster-scan order. This rigidity suboptimal for image compression as effective redundancy removal requires interactions between -imilar tokens, regardless of their spatial position. Although clustering provides a direct solution for grouping related tokens, naive implementations like per-sample online K-Means (McQueen 1967) are impractical. They are not only computationally expensive but also highly unstable for training due to the repeated centroid re- re-initialization and sensitivity early-stage features.
Codebook-based Token Clustering. To achieve efficient and stable clustering, we draw inspiration from VQ-VAE (Van Den Oord et al. 2017) and instead employ shared, learnable codebook
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containing all the centroids. Analogous to the vector codebook in VQ-VAE, the centroids in the codebook define a discrete latent space that captures semantically meaningful visual features for fine-graine clustering. This shared codebook, updated via exponential moving average, ensures training stability and robustness to initialization, yielding high-quality clustering results, as evidenced by Fig. [T Crucially, inference time, our method requires no iterative updates, resulting in efficient and deterministic assignments.
Specifically, given the feature map X RR xd the feature at each position is treated as token X;, thus we have X (xx Ji= C Rd. We employed a cosine-based clustering to group tokens into K clusters G1.- G1....g 9K. each with normalized centroids C1..... Cg. To initialize the centroids, the tokens of first batch are divided into K consecutive segments, and each centroid с₁ is computed as the average of the token embeddings within corresponding segment. Each CAM block holds its own cluster centroids, which are shared across all images and are only updated in the training phase.
During training, we update cluster centroids using the K-Means algorithm (Algorithm) IT. For T iterations, each token I; is assigned to its nearest center cj based on the cosine-derived distance matrix Distances.j- yielding assignments {9:). New centroids 6j are then computed as the normalized mean of all tokens assigned to each cluster. Centroids for empty clusters remain unchanged. Finally, the updates are smoothed using an exponential moving average (EMA) with decay A.
Each CAM block maintains and updates its own codebook of cluster centroids. This mechanism enables each block to dynamically adapt to its local feature distribution across the whole training dataset, leading to an effi- cient and precise grouping of content-similar tokens throughout the network. Furthermore, by aggregating statistics across many batches, this non-gradient update process allows the codebook to encapsulate knowledge of the dataset-level feature distribution. More de- tails about this clustering strategy and stability are discussed Appendix AA.
Algorithm 1: Training- Cosine K-Means
Require: tokens x=(x;) ) centroids {c,){*
iterations T, decay À
1: {cj]f +- {c, }
2: for t + 1 to T do
Distancei, < 1 13
4:
9: +- arg max, Distancei.f
5:
for j = 1 to K do
3f g: =j
6:
cj + 1 1,
cj
otherwise
7: end for
Given the updated centroids, final assign- ments 9s E {l...., K} are determined by the cosine similarity between tokens and clus- ter centroids. We build a permutation π {1, ......... N) + {1, .., N) that first groups
8: end for
C2 + Xo, + (1-1)c;
10: return (
all tokens with g; = 1, then those with 9; = 2, and so on. Applying x yields a reordered sequence
x X=X+(>) (-); so tokens belonging to the same cluster become contiguous in 1-D sequence, encouraging interactions between similar tokens. This enables SSM to focus on proximity in feature space. The inverse permutation π is cached and applied to restore the original spatial layout.
During inference, input tokens are deterministically and efficiently assigned clusters using the final updated centroids, without requiring any further K-Means updates.
3.4 GLOBAL- PRIOR PROMPTING
The vanilla Mamba layer models token interactions using a discrete state-space system. Its state is computed recurrently according the following equations:
A = exp(AA), B = (AA) (exp(AA) 1)) AB. h, = A h B x,, O Ch, + Dx;,
where x,, x, E Rdd are the i-th input and output tokens, h; E TRd. is the hidden state, and the state matrices A and B are discretized from continuous-time parameters. The matrices C and D project the hidden state and input back to the output space. The output matrix C acts analogously to the query in self-attention, determining which information is read from the hidden state (Guo al. 2024a
However, the recurrent formulation imposes a strict causal structure. Each token's ' updated state de- pends only its predecessors in the scan sequence, which limits the model's 's awareness of the global context. Even with our content-adapticn scan, which encourages interactions between similar tokens, this inherent one-directional information flow persists. To mitigate this, we propose modulating the
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38
37
PSNR (dE3)
16
CMIC (Ouz)
36
CMC (Ous)
-5
155
PSNR
MunbaVC
MunbaVC
34
FIIC
33
FTC
MLIC-+
TCM-L
TCM-
ELIC
VTM-21
11
V7M-21.8
11
Bitratee (bpp)
Bitrate (bpp)
Figure 4: RD Curves on the Tecnick dataset.
Figure 5: RD Curves on the CLIC dataset.
22
USS-SSIMA (dB)
PSNK (d3)
CMC
ManbelC
ManteVC
CCA
16-
CMIC
TCM-L
TCM-L
VTM-21.0
VTM-21.0
0.1
0.3
0.3
0.4
11.5
0.7
0.9
611
0.3
0.3
014
0.5
666
0.
Bitrate (bpp)
Bitrate (bpp)
Figure 6: Performance evaluation on the Kodak Dataset.
output matrix C with global priors. This prior summarizes the overall token distribution of the input, making it more adaptive to the token distribution of each specific instance.
Redundancy-aware Prompt Dictionary. To this end, introduce distribution-aware dictionary U E R xd, to further exploit the clustering prior, where K is the number of clusters. Each entry in this dictionary is prompt vector corresponding learnalle linear projection A IR Rdd, to each centroid and obtain
semantic cluster. Specifically, we apply
U U=A((Gi A((rr....
R Kxxd
that each row of U serves the prompt vector associated with cluster. This differs from MambalRv2 (Guo et al. 2024a), where the prompt pool is standalone learnable matrix optimized directly by gradient descent without explicit semantic constraints; in contrast, our prompt dictionary clustering centroids. For an imput with N tokens, we form
is explicitly tied to the redundancy- a one-hot assignment matrix I E 10 N representing the cluster membership of each token, as shown in Fig. 2 (b). A sample-specific prompt matrix P E R Xd, is then generated by querying result, the prompt signal reflects how redundancy is distributed across semantic clusters, highlighting clusters with higher or lower redundancy for the current features. Crucially, whereas the centroid codebook is updated via non-gradient K-Means, the mapping A(-) differentiable and trained end-to-end for the final rate-distortion objective.
the dictionary P TU.
Prompt Conditioning Mamba. To enhance global content-awareness, following the Attentive State-Space equation in MambalRv2 (Guo et al. 2024a). the samplc-specific prompt P is injected into the state-space equation by augmenting the output projection matrix C as follows:
O, = (C+ P)h, + Dx,
This mechanism combines global statistical knowledge from the learned dictionary with the sample- specific semantic layout from the clustering. By conditioning the output of SSM on this prompt, the model encodes global priors during the scan process, effectively relaxing the strict causal constraint and enhancing its ability to model long-range dependencies.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We train our CMIC models on the Flickr2W dataset (Liu et al. 2020). optimizing with the Adam optimizer (Kingma 2015) and initial learning rate of 10 Distortion is quantified using two

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BD-rate (%)
Complexity
Method
Kodak
Tecnick
CLIC
Params (M)
TFLOPs
Latency (S)
Peak Mem (GB)
VTM-21.0
0.00
0.00
0.00
ELIC
-3.10
7.41
0.84
33.29
1.74
0.335
1.50
MLIC+
-13.42
16.73
13.94
116.48
2.64
0.547
2.08
WeConvene
-6.85
-10.17
-7.03
105.51
4.82
1.293
4.53
TCM
-10.04
-10.22
-8.60
75.89
3.74
0.542
7.73
CCA
-11.99
-13.53
-11.40
64.89
3.28
0.385
5.04
FTIC
12294
133.89
100.1
69.78
2.38
>10
4.90
S2CFomee
-14.28
117.20
-2.88
79.83
4.17
8.32
HPCM
-14.33
-16.77
-14.54
68.49
2.21
0.304
2.97
MLICV2
-16.16
10..3
-5.779
84.30
2.78
0.520
5.07
DCAE
15.40
19.62
16.46
119.22
2.28
0.478
5.59
LALIC
13.68
17.26
15.01
63.24
2.53
0.385
4.09
ManhaVC
-8.10
-1..8
10.94
47.88
2.10
0.425
14.73
MambaIC
13.01
15.27
15.23
157.09
5.56
0.669
20.32
CMIC (Ours)
-15.91
-21.34
17.58
69.11
2.39
0.405
4.44
Table 1: Quantitative comparisons of SOTA LIC models. BD-rates are computed over VTM-21.0.
FLOPs, peak memory and decoding latency are measured on 2K-resolution images.
metries: mean square error (MSE) and multiscale structural similarity (MS-SSIM). The Lagrangian multipliers À are chosen from {0.0017, 7, 0.0025, 0.0035, 0.0067, 0.0130. 0.0250, 0.050) for MSE and (3, 5, 8, 16, 32, 64) for MS-SSIM. All experiments are carried out on NVIDIA A100 GPUs. We set channel dimensions {C1, C2, C3, C4 } {128, 192, 256, 320} and set the number of non-linear transform blocks {L,, L2, La} (3, 2,2). The cluster count is fixed at 64. The window size the attention modules set 8. The iterative K-Means clustering is updated times per training step, which accounts for only 5% of each step's 's training time, introducing minimal overhead.
4.2 EVALUATION
We test models three image datasets: the Kodak dataset (Kodak) 1993) 768 512, the Tecnick test set (Asuni et al. 2014) at 1200 x 1200, and the CLIC professional validation set (Toderici et al. 2020) at 2K resolution. To assess rate-distortion performance, we report peak signal-to-noise ratio (PSNR), multiscale structural similarity (MS-SSF MMS-SIMM, and bits pixel (bpp). Average bitrate savings are quantified via BD-rate (Bjontegaard 2001I) and average PSNR improvement matched bitrates via BD-PSNR. Model complexity is evaluated by total parameter count, floating-poit operations (FLOPs). peak GPU memory and decoding latency. We also provide detailed encoding latency on A100 and RTX3090 GPUs in Appendix A.14
4.3 RATE-DISTORTION PERFORMANCE
Our CMIC model is benchmarked against the conventional codec VTM-21.0 (Bross et al. 202T) and several SOTA learned methods including ELIC (He al. (022). TCM (Liu et al. 2023). MLIC+
(Tiang & Wang 2023), WeConvene (Fu et al. (024b).
1 FTIC (Li ei al. 2024a), CCA (Han et al. 2024).
S2CFormer CChnn
2025a). HPCM (Li at 2025c). MLICv2 (Ting
2025), DCAE (Lu
ct al. 2025). LALIC (Feng et al.
2025 Mamba (Qin et al. (2024) and MambalC (Zeng et al.
2025). The RD curves shown Fig.
Comprehensive BD-rate comparisons, using VTM-21.0
as the reference, are detailed in Tab. Our CMIC model achieves superior performance, reducing
BD-rate by 15.91% %, 21.34%, and 17.58% on the Kodak, Tecnick, and CLIC datasets.
The proposed CMIC model consistently outperforms leading methods across all evaluated datasets. When measured by BD-PSNR, CMIC surpasses the SOTA Transformer-baat based FTIC with gains of 0.15 dB (Kodak), 0.32 dB (CLIC), and 0.36 dB (Tecnick). Against the hybrid CNN-Transformer model TCM-L, the superiority is even more pronounced, yielding improvements of 0.28 dB, 0.40 dB, and 0.53 dB on the respective datasets. The model's exceptional performance (CLIC and Tecnick) validates the powerful global modeling of our CAM module. Furthermore, CMIC also delivers significant MS-SSIM, improvements (Fig. (6) It outperforms TCM-L and FTIC by -7.34% and -3.87% respectively, which underscores the overall robustness and superiority of our approach for different metrics. Visual comparisons are provided in Appendix A.5
high-resolution images
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ELIC
MLIC
WeConwere
52C
TCK-
FTIC
MarbbaVC
Mambatc
CNIC (Dus)
Figure 7: Effective Receptive Field (ERF) visualization for analysis networks in LIC models. Broader and darker green regions denote larger ERFs, which represents a more global spatial context.
FEC
ICMA
FIR HOWL
FFA
TONE
FTC
Figure Per-image effective receptive field (ERF) visualizations.
Compared with other Mamba- based LIC models, CMIC also demonstrates significant performance gains. It surpasses Mamba VC with BD-rate savings of 7.51% % on Kodak, 6.80% on CLIC, and 10.09% on Tecnick. Similarly, it outperforms MambalC by 2.36%, 2. 2.17% and 6.48% on the three datasets. These consistent advantages across all bitrates on the Tecnick dataset are illustrated in Fig. (c). The rate savings diagrams on the other two datasets are provided in the Appendix AA.4 direct MS-SSIM comparison is omitted because these two competing methods are only optimized for MSE.
4.4 MODEL COMPLEXITY
Our proposed CMIC model achieves substantial BD-rate improvements while maintaining moderate complexity levels. Compared to TCM-L (Liu et al. 2023 with relatively larger parameter scale (75.89M), CMIC is substantially more efficient, reducing FLOPs by 36%, latency by 25%, and peak memory by 43%. In comparison to the SOTA Mamba-based model, MambalC (Zeng al. (025). CMIC also demonstrates clear advantages. Specifically, it reduces parameter count by 56%, FLOPs by 57%, decoding latency by 39%. Notably, it achieves a 78% reduction GPU memory usage, significant improvement attributed to its efficient single selective scan rather than the quadratic 2D scans. This favorable complexity- performance balance is primarily attributed to our efficient Mamba architecture, which efficiently captures global context without incurring excessive computational cost. These characteristics underscore CMIC's practical advantages, making it potentially suitable for applications requiring high compression quality coupled with efficient processing.
caaplexity-perfommnnce
4.5 ABLATION STUDY AND ANALYSIS
CTP GPP
Kodak
Tecnick
CL.C
We first perform ablation studies to isolate the individ- ual contributions of Content- Addatiive Token Permuta- tion (CTP) and Global-Prior Prompting (GPP), detailed in Tab. - Our baseline block, with both components disabled, is equivalent to vanilla single-scan Mamba block. Our ablation studies focus primarily on the CAM blocks in the transform networks. For the entropy model, adding CAM yields negligible performance gains while inereasing latency, indicating a limitation of CAM in enhancing entropy modeling.
13.26
17.74
-14.87
15.21
-20.17
- 116.67
14.27
-19.13
-1.34
15.91
-21.34
-17.58
Table 2: Components ablations.
Detailed results are provided Appendix A.3.2
Method
Throughput
Content-Adaptive Token Permutation. CTP uses codebook- based clustering and sequence reordering group correlated to- kens together. Integrating CTP alone yields significant BD-rate reductions of 2.0%. 2.4%, and 1.8% on the Kodak, Tecnick, and CLIC datasets. Conversely, removing CTP from our final model causes a performance drop of 1.6% 2.2%. This highlights CTP's critical role in overcoming the rigid raster scan, allowing the model to enhance interactions between semantically related tokens and capture long-range redundancy.
TCM-L
17.80
MambaVC
6.55
MambalC
9.35
CMIC wo CTP&GPP
23.19
CMIC (Ours)
22.05
Table 3: Throughput Ablation.
Global-Prior Prompting. Global-Prior Prompting (GPP) mitigates the strict causality of SSM by injecting sample-specific prompts. As shown in Tab. 2 removing GPP from our model reduces the BD-rate gain by 0.7%-1.24 1.2%, while adding it the baseline SSM yields 0.5%-1..4% improvement. Together, GPP and CTP achieve a total BD-rate reduction of 2.7%- 7.6%. demonstrating that the two components are highly complementary.
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OU Original
Od veb P& woG
CoP up GPP
ed CTP& WOP
69 uC CIP& GPPOCMIO
Figure 9: ERF of a single Mamba layer. shows how GPP introduces non-causality beyond raster scan, and how CTP reshapes ERF toward semantic, content-correlated regions.
Throughput and Inference Overhead. Our proposed CTP and GPP modules introduce negligible overhead for both training and inference. As shown in Tab. B training throughput on 256x256 patches (batch size 8) slightly decreases from 23.19 to 22.05 samples/s, remaining faster than other Mamba-based models. For inference on 2K image, decoding time increases by only 4% (from 0.387s to 0.405s), confirming our model's 's high efficiency.
Comparison with Other Structures. To validate the efficacy of our CAM blocks, we substitute them with Conv block and 2D Mamba blocks in stages 3-5 of the model. The results, presented in Tab. 4 demonstrate that CAM blocks yield perior performance over these alternatives while maintaining comparable parameter count. Fur- thermore, we compare against variants built purely with window attention or CAM blocks. Both configurations result inferior performance, underscor- ing the effectiveness of our proposed CMIC, in which CAM blocks are strategically integrated.
BD-rate(%) Params(M) FL.PP(()
Conv Block
12.89
66.47
2.18
2D Mamba
14.13
71.44
2.54
Attention-only
13.06
68.23
2.33
CAM-only
14.68
70.00
2.45
CAM (Ours)
-15.91
69.11
2.39
Table 4: Ablations on network structures.
ERF Visualization. We visualize the Effective Receptive Field (ERF) (Luo al. 2016 of the analysis non-linear transforms of different LIC models in Fig. 7 We calculate the ERF as the magnitude of the gradient of the central latent pixel with respect to the input image, clipping values to [0, 0.20], and averaging over 24 Kodak images. Compared with prior CNN-, Transformer- and Mamba-based LIC models, CMIC shows a significantly larger receptive field across the entire image.
Visualization of Content Adaptivity. We visualize per-image ERFs at fixed query locations (red dots) to further demonstrate the adaptivity of our model. As shown in Fig. S CMIC presents global, content-adaptive ERFs whose high-influence regions align with semantic structures and visual intuition about redundancy (e.g., hair/feathers in Kodim171 1723, shoreline in Kodim16, aircraft in Kodim20). This reveals that local and distant redundancy is effectively captured and eliminated. In contrast, FTIC and TCM-L yield content-agnostic compact, nearly isotropic ERFs that look similar across images, indicating limited ability to modulate spatial dependencies according content. More results and comparisons are provided in Appendix A.6
Visualization of Non-Causality. We compute the ERF between the input and output features of single state-space model layer with soft clustering to analyze the proposed non-causal mechanism and to isolate the effects of our two main components CTP and GPP. As shown in Fig. P when both CTP and GPP are removed (column (b)), the tokens are processed in a standard raster-scan order. Taking the center token as the anchor, we observe that the ERF stops exactly the central position (H//2, W//)); even the subsequent tokens in the same row exhibit zero ERF values. This reflects strict raster-scan causality, where each token conditioned only on the previously scanned tokens.
In column (c), when we enable GPP, we observe non-zero activations even after the scanned sequence. This shows that GPP allows the state-space model to "see beyond" the strietly causal scan and gain stronger global semantic awareness. Moreover, the activated regions are semantically aligned and meaningful, indicating that the prompt encodes effective global semantic conditions that guide the Mamba scan. In columns (d) and (e), once CTP is applied, the ERF maps longer exhibit the characteristic raster-scan pattern. Instead, activation spreads over semantically related locations. This demonstrates that CTP effectively breaks the fixed Fuclidean-neighhor constraint imposed

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Published as a conference paper at ICLR 2026
Kodimo01 Block otivated Hatio 31% (21.53/61)
Crednedd 4G2
Curtrad 4E0
L2
52
Control aEo
Oarzaid 465
Kodim21 Per Block Activated Ratio:
Centrii
(18.07/64)
Chrtaiid ARD
Kodlm22 Pec-Block
Activated Ratin:
38% (21.13/51)
(a) Original Image
(b) Cluster Visualizations
Figure 10: Visualization of clustering results from Stage 2 of the analysis network. Each panel displays binary mask for a single cluster, where white pixels indicate cluster membership.
Dataset
Mean Activation Count Activation Ratio
Variance
values
128
Kodak CLCC
23.27
36.37%
90.91
26.30
41.10%
121.15
BD-rate
14.97%
15.91%
15.96%
Table 5: Activation clusters
Table 6: Ablations on K values.
by pre-defined scan orders, prioritizing feature-space proximity over spatial adjacency and further enhancing redundancy elimination between distant yet content-correlated tokens.
The Adaptivity of K. We view the centroids of each block as codebook. While the codebook size remains fixed, the number of active centroids varies adaptively with the content. The codebook size (64) works as upper bound the cluster centroids. Although each codebook contains 64 centroids, not all of them are activated during assignment; in practice, many remain empty. Most images typically activate only 16-32 centroids, and the centroids that are active vary from image to image. As shown in Tab. [5, the per-image mean number of activated centroids is 23.27 with a variance of 90.91 the Kodak dataset. On the CLIC dataset, the corresponding mean is 26.30 with variance of 121.15. These observations indicate that the exact number of activated clusters depends on image content, K inference is effectively dynamic and adaptive image contents.
Ablations on Cluster Number. We also explore the impact of the cluster number K, with BD-rates reported for the Kodak dataset (Tablo). We identify K 64 as a suitable choice. A larger K does not yield much improvement, likely because 64 per-block clusters are sufficient represent the diversity of most natural images, which also matches our observations about the activated cluster count.
Cluster Visualization. We further visualize the clustering results in Fig. 10 to verify the effectiveness of the token permutation mechanism. Each binary mask corresponds to a distinct cluster, and when compared with the original image, it becomes clear that tokens with similar visual or semantic attributes are grouped together (e.g.. the red doors and windows in Kodim01, the clouds and sky in Kodim21, and the feathers in Kodim23). This demonstrates that content-similaa, and semantic-related sequences
tokens are reorganized into contiguous sequences, enabling the SSM efficiently scan neighboring elements in the feature space rather than depending solely on Euclidean distance. Additionally, although the three sample images exhibit different activation rates, they share subset of activated centroids. We observe that some centroids learn semantically consistent representations: Centroid #10 is mostly triggered by high-gradient edges, #26 tends to respond red/yellow regions with rich textures; and #33 appears predominantly in smooth blue/green background areas. This suggests that each centroid summarizes class of high-dimensional visual patterns that are shared across images.
5 CONCLUSION
In this work, we introduced Content-Aware Mamba for Image Compression (CMIC), a novel ap- proach tailored to align Mamba-style State-Space models with the inherent two-dimensional structure of image compression. By employing Content-adaptive Token Permutation and Global-Prio Prompt- ing, we enhance SSM's content-awarenesss to capture long-range redundancy. Specifically. our content-aa andeett-ddaptvv scanning strategy prioritizes fcature-space proximity rather than spatial arrangement. Embedding global priors via the prompt dictionary relaxes the strict causality without quadrupling the computational load. Consequently, CMIC efficiently eliminates distant redundancy while retaining linear computational complexity. Experimental results demonstrate that CMIC achieves SOTA RD performance and consistently outperforms recent Mamba-based image compression models.
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ACKNOWLEDGMENT
This work was supported by the National Key Research and Development Program of China under Grant 2024YFF0509700,
National Natural Science Foundation of Chin2(9441129,522411155.33333333 and the Fundamental Research Funds for the Central Universities.
ETHICS STATEMENT
All the authors read and adhere to the ICLR Code of Ethics.
REPRODUCIBILITY STATEMENT
We ensure the experiments in this paper are reproducible. We provide detailed settings about datasets and training in Section 4.1
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Yiwei Zhang, Guo Lu, Yunuo Chen, Shen Wang, Yibo Shi, Jing Wang, and Li Song, Neural rate control for learned video compression. The Twelfth International Conference on Learning Representations, 2023.
Zou Zhen, Yu Hu, and Zhao Feng. Freqmamba: Viewing mamba from frequency perspective for image deraining. arXiv preprint arXiv:2404.09476, 2024.
Zhuoran Zheng and Chen Wu. U-shaped vision mamba for single image dehazing, arXiv preprint arXiv:2402.04//3 339 2024.
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. In Forty-first International Conference on Machine Learning, 2024.
Yinhao Zhu, Yang Yang, and Taco Cohen. Transformer-based transform coding. In International Conference Learning Representations, 2022.
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A APPENDIX
A.1 VTM SETTINGS
We provide the corresponding bash commands for encoding and decoding an image using VTM-21.0 (Browne et a The resulting YUV file can then be further processed or converted into RGB format for distortion calculation.
Encoding
VTM-21. 0020100B0 bin Encoder -i tmp. encoder intra vtm cfg -4 61 -o null -b tmp. bin -wdt 768 hgt 512 -fr 1 -f -1npuchrromaForma-4444 -npputBitDepphhas
2/M-21.0/cggl
Decoding
VTM-21.0/ bin DecoderAppStatic -b tmp. bin tmp. yuv -d
A.2 COMPARISON WITH ANOTHER CLUSTERING-BASED LIC MODEL
In comparison with another CNN-based clustering- based LIC model Zhang et al. (2024b), we emphasize that the key distinctions lie primarily in motivation overcome the raster-scan and causality limitations inherent in the Mamba-based LIC model, as well as our use of permutation- equivariant, fine-grained clustering strategy.
A.2.1
OUR MOTIVATIONS AND INSIGHTS
First Insight. Vanilla Mamba's s fixed raster-scan order limits effective redundancy removal, as optimal compression requires interactions among semantically related tokens. Although Mamba has global modeling capabilities, its raster-scan approach places unrelated tokens adjacently, which limits efficient interactions between correlated tokens. To address this, we propose Content-Adaptive Token Permutation (CTP), which clusters and rearranges tokens that Mamba prioritizes interactions among similar tokens. Specifically, CTP:
Clusters tokens into correlated groups (validated by Fig. 10
Reorders tokens to position related ones adjacently, improving redundancy elimination.
Our CAM block thus enhances token interactions and state-space updates, significantly boosting compression efficiency.
Second Insight. Vanilla Mamba's strict causality, where tokens depend only on previously scanned tokens, limits its vision task performance. To address this, propose sample-specific clustering- based prompt to introduce global context and relax causality constraints efficiently. Unlike prior methods that rely on costly multi-directional scanning, we:
Generate a sample-specific prompt from clustering results and distribution-aware prompt dictionary.
Use this prompt to incorporate global statistics into the state-space (e.g., matrix C), capturing sample-specific prior.
Thus, our method provides global context and mitigates Mamba's causality constraints without extra multi-directional scanning.
A.2.2 CLUSTERING COMPARISON
The clustering strategy in Zhang et al. (024b) still partially relies on spatial relationships and is a coarse-level clustering. In contrast, approach introduces fundamentally different clustering methodology for iineggrinee, non- Euclidean grouping. We can summarize the two methods in short follows:
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Uses average pooling to generate 2 cluster centers, relying on spatial relationships and checkerboard mask for even ween-numbered blocks.
Zhang et al. (2024b)'s Method:
Our Method: Uses codebook for center lookup, determining clusters based token content without relying on spatial coordinates.
The two methods demonstrate two main distinct characteristics:
Spatial Anchoring vs. Non-Euclidean Adaptive Clustering Zhang al. (2024b) is grid-anchored (2 x 2 average-pooled centers + checkerboard), so assignments change under pixel permutations or shifts. Our clustering is permutation--qquiaariani and canslation-invariin assignments are unaffected by rearrangements.
Coarse Pooling vs. Fine-grained Clustering The average-pooled centers Zhang al. (2024b) are biased toward homogeneous regions (e.g., background), reducing discriminative power and yielding coarse groups. We use codebook of semantic prototypes (similar to codebooks VQ-VA VAE [2]) queried by CAM blocks, enabling precise, fine-grained grouping needed for compression. As shown in Figure 8, our clustering yields fine-grained groupings, capturing details such as parrot feathers and cloud structures.
C) PERFORMANCE COMPARISON
According to the reported RD curves, Zhang et al. (2024b) achieves BD-rate improvements of -8.75% and -9.64% over VTM-21.0 on Kodak and Tecnick, respectively, which is lower than our BD-rate gains 15.91% (Kodak) and -21.34% (Tecnick).
In summary, acknowledge the contribution of Zhang al. (2024b) to the clustering- -based LIC model. Our work, however, has a totally different motivation and employs a completely different design of clustering.
A.3
MORE DETAILS AND ABLATIONS ABOUT ENTROPY MODEL
A.3.1 DETAILS OF ENTROPY MODEL
The Space-Clue Caace-Channel ConTeXt (SCCTX) model (He et al. 2022) is fast and parallel entropy model x x SCCTX splits the M channels
for learned image compression. Given latent features ý E into K groups, indexed by k. Early groups have fewer channels to focus on high-energy components, while later groups have more channels for low-energy components. For each group and each
position i, SCCTX predicts the entropy parameters ) = (p) as follows Fig. A.3
(k) .o(h)) using three types of context
First of all, the spatial context & (8) in the same group:
(R) is computed by a network gep from previously decoded symbols sp.i
$
(R) sp.i
Dap
(k)
(N)
The channel context o (k) all symbols in previous groups:
is computed by another network ( from
Seata" Chamet
+ (k)
gehi (k)
(g(<<k)
The hyperprior context is obtained by applying a nonlinear trans- form function hp, to the hyperprior latent 2:
T h2)
Concatenate
All three contexts are combined and passed through a simple aggre-
Farm Apregattin
gation network, which outputs the final entropy parameters:
Aggregation
(k)
sp.it
ch
By combining these contexts in parallel, SCCTX both local and global information, which leads to fast decoding and good compression results.
Figure 11: Overview of Our Enhanced Efficient SCCTX Entropy Model.
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Rain Savings VS V7M-11 (86)
-16
-14
Rate Savings ve VTM-21.0
-66
-14
-10
-8
MambelCC
Mamberl:
KarbaVC
Kamtalt
CMI (Gurs)
-10
(Ouis)
PSNR (6B)
PSNR (IB)
Figure 12: Rate Savings the Kodak dataset.
Figure 13: Rate Savings on the CLIC dataset.
A.3.2 ABLATIONS ON ENTROPY MODEL
We provide three more ablation studies about the entropy model to demonstrate the superiority of our CAM block.
First, we compare our enhanced Efficient SCCTX model with the Conv SCCTX baseline (removing (k) both FFN in Ich and gated mechanism in Param Aggregation). The results are detailed in Tab. 7 We emphasize that our entropy model is not intended to be state-of-the-art, is a lightweight variant built trade-off. Despite its simplicity, the model demonstrates a slight improvement over the Conv SCCTX.
on SCCTX with few additional MLP layers, aiming for favorable
Second, we also tried to apply CAM block to replace the combination of Depth-wise convolution and FFNs in the geh This variant is denoted as "CMIC (CAM SCCTX)" Tab. 7 Although this variant offers some advantages on high-resolution datasets, the improvements are modest, and it increases decoding latency from 0.405 to 0.471 s, slowdown about 16%. Accordingly, we use the efficient, enhanced SCCTX variant in our CMIC model, while clustering mechanisms and the Mamba architecture remain promising directions for future work on entropy modeling.
These results demonstrate that, while content-adaptive processing and Mamba's global modeling capabilitics are crucial for the non-linear transform networks, this strong global awareness does not offer significant benefits for the entropy model, which is used to model the distribution of the latents after redundancy removal. In contrast, depth-wise convolution, which emphasizes local relationships, highly efficient and sufficiently powerful operator. Additionally, the extra linear and MLP layers we inserted are also proven to be both efficient and powerful for multi-layer slicing entropy model.
Table Comparison of different SCCTX-based Entropy models.
Model
BD-rate [Kodak]
BD-rate [CLIC]
BD-rate [Teenick]
CMIC (Our Enhanced SCCTX)
11.91%
-17.58%
-21.34%
CMIC (Conv SCCTX)
-15.1 11%
-16.78%
-20.53%
CMIC (CAM SCCTX)
-15.81%
-17.73%
-21.45%
Third, we replace the entropy model in our CMIC with the advanced T-CA entropy model used in FTIC. Despite using the same entropy model, our approach still outperforms the state-of-the-ar FTIC, demonstrating the effectiveness of our transform networks.
Table 8: Performance of CMIC when using the advanced T-CA entropy model from FTIC.
Model
BD-rate [Koda]]
BD-rate [CLIC]
BD-rate [Teenick]
FTIC
-12.94%
-10.21% -17.56%
-13,89%
CMIC (with FTIC entropy model)
-15.67%
-21.42%
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A.4 MORE RATE SAVING COMPARISONS
We also provide plots of Rate Savings PSNR the Kodak and CLIC datasets in Fig. 12 and Fig. Those two figures and Fig. I (c) show that our CMIC model steadily outperforms the Mamba VC
13
Qin al. )024) and MambalC Zeng et al. (2025) models across all three datasets with different resolutions, at different bitrates. This demonstrate the superiority of our CMIC model.
A.5 VISUAL COMPARISONS
We provide more visual results in Fig. 1415 comparing with traditional codec VTM (Browne et al.) and advanced LIC model TCM (Liu et 2023
Origin
VTM-21.
TOM 8PP:0AY3
BPP
BPP: 0.068
PSMR:
Qura SSMM
PSNR: 999
PSNR:: 29
MS SSM0:SMM 0.961
Ms 30---SM-M. 0.973
Figure 14: Visual Comparisons on Kodim03.
Origgn
BPP: 0.238
BPP: 3.24
TOM
POWR
27.709
MS-89M--3
MS HIS-SMM 3.944
BAP 221
BPP: 14t
NS-SSIM
16663
Figure 15: Visual Comparisons on Kodim24.
A.6 MORE VISUALIZATIONS OF PER-IMAGE ERFs
We provide more visualizations of per-image erfs in Fig. 16 Our model demonstrates much more content-and adaative ERFs than CNN-based Transformer- based and even Mamba-based LIC models. While MambalC demonstrates degree of global awareness, its Effective Receptive Field (ERF) exhibits distinct artifacts. Specifically, manifests as cross-shaped regions of high gradients centered on the target pixel (the red dot). This artifact is content-agnostic and exist in every image. It may result from its four-directional scanning mechanism. This causes the model to focus on capturing irrelevant information. In contrast, our model concentrates capturing genuinely redundant information within images, which is more adaptive and effective.
A.7 MORE BASIC BLOCK STRUCTURES
17 both designed to We detail the architectures of the Attention Block and Conv Block in Fig. efficiently capture local contextual information.
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MEICH
Nanhal
Figure 16: More Per-image effective receptive field (ERF) visualizations.
Attention Block
Conv Block
FFN
FFN
LayerNorm
LayerHorm
Window Reverse
Cony 1x11
Deptiwise Convolution
LayerNorm
LeakyReLU
W Window
Conv 1xt
Atteation Block
(b) Conv Block
Figure 17: Details of Attention and Conv block
Attention Block. For an input feature map we first split it into small windows. Each window is normalized (LayerNorm, LN) and processed by Multi-Head Self-Attentin (MHSA). The outputs are then combined back together and added to the original input (residual connection). Finally, we apply another normalization, a -eed-forward network (FFN), and another residual addition.
Conv Block.
Replacing the MHSA stem, 1x1 point-wise conv + depthwise conv + LeakyReLU + x point-wise conv. The remaining LN-FFN sequence and the two residual shortcuts identical to the Attention Block, enabling lightweight yet expressive local-context modeling.
efficient Septhhwse-seppacee convolution stack:
A.8 TRAINING STABILITY
Since K-means clustering and token sorting are non-differentiable, the resulting gradients are biased. However, EMA and codebook-base based K- K-means updates ensure stable convergence when training the CMIC model.
To demonstrate stable training and convergence, plot test-loss curves versus training steps for model under different random seeds and for the ELIC baseline He et al. (2022). As shown in Fig. [8] model is insensitive the choice of random seed and centroid initialization (the centroids initialized simply as the average of token embeddings within each segment from the first batch), and converges reliably.
A.9 RATIONALE FOR PER-BLOCK CLUSTER CODEBOOK
Each CAM block maintains its K cluster centers and does not share the centers.
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ELIC
1.7
CMIC (seed 1024)
CMIC (seed 16)
Lost
1.6
1.5
1.4
0.5
1.5
2
Steps (M)
Figure 18: Test losses vs. Training Steps.
Sharing cluster centers across all blocks can lead to significant alignment challenges, as feature distributions may vary drastically. Specifically, features at different stages often exhibit entirely distinct resolutions, receptive fields, and semantic levels. Additionally, channel widths may differ between stages, necessitating extra projection layers for dimensional alignment- inevitably intro- duces additional parameters. A single shared set of cluster centers would be forced to accommodate heterogeneous feature spaces, potentially compromising performance.
By allowing each block to learn its own dedicated codebook of cluster centers, the centers can specialize according to local feature statistics, resulting in more stable training and higher-quality representations.
Parameter impact. The total parameter count for all eight codebooks of centers is 14688, which accounts for 0.166% of the total parameter count (69. 1M). This marginal overhead suggests the codebooks introduce minimal additional parameters.
A. THE ORDERING OF TOKENS WITHIN A CLUSTER AND THE ORDERING OF CLUSTERS
We utilize stable sorting and:
1. The tokens within single cluster are processed in their original raster-scan order.
The cluster order is determined by the order of centers in the center codebook.
A.11 CODE AVAILABILITY
The full implementation code and checkpoints will be released immediately upon acceptance.
A.12
THE USE OF LARGE LANGUAGE MODELS (LLMs)
We utilized LLMs only for minor linguistic refinement, All the main contributions, including research ideas, experimental design, and data analysis, are the original work of the authors.
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A.13 DETAILED DISCUSSIONS ABOUT MAMBAIRY2
In this section, we provide detailed comparisons and visualization analysis about our CAM block and MambalRv2 (Guo et 2024a).
We emphasize that our method differs from MambalRy2 at fundamental level in both motivation and technical design. Accordingly, we provide detailed comparisons and visual evidence demonstrate why our approach is better suited to learned image compression.
A.13.1 MOTIVATION COMPARISON.
Our goal is to enhance Mamba's 's ability to eliminate redundancy among tokens that are content- correlated yet spatially distant. To this end, our CAM proposes redundancy-aware clustering and redundancy-aware prompts, enabling the model to identify global redundancy and eliminate more effectively.
In contrast, Mvm is developed for image super-resolution; its architecture is not designed to discover model redundancy for compression.
This task mismatch naturally leads to different classification and prompting mechanisms.
A. 13.2 TECHNICAL COMPARISONS.
We make technical comparisons in two aspects as follows.
1. Token classification.
Our CAM: redundancy-avee clustering.
We classify tokens via codebook-based KMeans clustering, which is explicitly tailored for redundancy discovery. Using cosine similarity, we perform iterative mini-batch K.Mann over token features to learn shared codebook of centroids. These cen- troids group content-simin tokens and enable their reordering, so redundancy among semantically similar but spatially distant regions can be reduced effectively.
MambalRy2: unsupervised classification head
Ma employs lightweight linear projection head produce per-token logits, followed by Gumbel-Softmax sampling for discrete assignments. This head is trained without explicit semantic redundancy-oriented supervision, so the resulting clusters lack interpretability and do not reliably reflect token redundancy.
2. Global prompting.
Our CAM: ecdundancy-awar dictionary
While our prompting follows the Attentive State Space equation introduced in Mam- balRv2, our prompts are strictly tied to redundancy- vector projected from a specific cluster centroid and thus inherits clear semantic meaning. As specified in Sec. 3.4 we index the prompt dictionary using the cluster assignments, so the prompt signal explicitly encodes global redundancy distribution over semantic clusters, highlighting heavily redundant clusters for the Mamba block to emphasize.
clustering. Fach prompt
MambalRv2: standalone dictionary
Mv2 learns a prompt pool, which is a standalone learnable matrix optimized directly by gradient descent without explicit semantic constraints and without coupling to any redundancy-aware clustering. Consequently, its prompts do not explicitly represent global redundancy across content clusters.
Summary of the design gap. Our method is purpose-built for the central requirement of learned im- age compression: capturing and eliminating redundancy. Therefore, we introduce (i) a redundancy- aware clustering strategy specialized for redundancy detection, and (ii) cluster-tied prompt con- ditioning mechanism that provides the state-space model with a global redundancy-distribun signal. MambalRv2, designed for super-resolution, does not incorporate these redundancy-oriees oriented components.
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CMIC (Ouz)
Cooediication Read
CNIC (0urs)
Classifccaionn Food
CMIC (Oung)
Clessiforion Read
Centroid RZ
Contrrol MOS
Centrild PRU
Centrndd M577
Ocateed RD
Figure 19: Visualizations of classification results.
CHIC
EMIC
Cloddratii Ihat
Bad
Shel
Figure 20: Per-image effective receptive field comparisons.
A.13.3 EXPERIMENTAL EVIDENCE.
We conduct ablations on the classification and dictionary strategies of MambalRv2 within our framework. Both qualitative and quantitative comparisons provided to highlight the differences between our redundancy-aware token classification and redundancy-aweit dictionary and those of MambalRv2.
1. Classification Results. We first compare the classification strategies: our codebook-based KMeans clustering versus the unsupervised linear classification head in MambalRv2.
As shown Fig. 19 our redundancy-aware clusters align well with semantic structures and visual redundancy patterns, whereas the assignments produced by MambalRv2's classification head not correspond to semantic content or redundancy.
Moreover, Fig. visualizes all 64 clusters produced by the classification head strategy; none exhibits meaningful redundancy or semantic consistency.
These observations indicate that MambalRy2's routing is not designed for compression, while our clustering explicitly tailored capture redundancy.
2. Single-image ERF Visualization. Fig. 20 in the revision further provides single-image ERF visualizations.
Our ERF concentrates sharply on redundant, content-correlated tokens and regions, including spatially distant areas, demonstrating strong content adaptivity.
In contrast, MambalRv2 exhibits more content-agnostic long-range pattern: responses are relatively uniformly spread over the entire image, with much weaker sensitivity to semantic structure and redundancy.
This shows that our CAM captures redundancy more effectively, which in turn leads to improved compression performance.
3. BD-rate Comparison. We implement two variants quantitatively compare the classifica- tion and prompt-pool strategies within our compression framework. The results are in Tab.
The first variant, CMIC (w/ Standalone Dictionary), replaces our redundancy-awann dictionary with the standalone dictionary in MambalRy2.
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The second variant, CMIC (w/ Classification Head), replaces redundancy-aweir awwae clustering with the unsupervised classification head from MambalRv2. Since our redundancy-awaae dictionary relies the clustering centers, for CMIC (w/ Classifica- fion Head) we use the standalone dictionary for prompt conditioning.
The resulting BD-rate comparisons show that our CAM design consistently outperforms the MambalRV2-stylle variants, confirming the benefit of our redundancy-awain classification and dictionary.
Model
Kodak
Tecnick
CLIC
CMIC (CAM)
-15.91
-21.34
-17.58
CMIC (w/ Standalone Dictionary)
-15.02
-20.21
-16.73
CMIC (w/ Classification Head)
-14.46
-19.76
-15.83
Table 9: BD-rate comparison between our CAM block and the MambalRv2 designs.
1.6
17
18
19
20
21
22
25
48
49
50
52
53
54
55
:6
57
58
59
G1
Figure 21: All 64 Cluster Visualizations of Linear Classification Head Strategy.
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Model
A100-Enc-Lat. (s)
A100-Dec-Lat. (8)
3090-Enc-Lat. (S)
3090-Dec-Lat. (S)
ELIC
0,583
0.335
0.671
0.392
MLIC+
0,508
0.547
0.601
0.611
WeConvene
1,264
1.293
1.332
1.373
TCM-L
0.647
0.542
0.701
0.612
CCA
0.526
0.385
0.599
0.418
FTIC
> 10
> 10
> 10
> 10
S2CFormer
0.755
0.362
0.891
0,443
HPCM
0,282
0,304
0.335
0.362
LALIC
0.909
0.385
0,990
0.460
DCAE
0,469
0,478
0,551
0.543
Mamba VC
1.223
0.425
1.390
0.502
MambalC
1.436
0.669
1.572
0.783
CMIC (Ours)
0.618
0.405
0.692
0.464
Table 10: Encoding/decoding latency on A100 and RTX3090 (seconds)
A.14 14 ENCODING LATENCY ON DIFFERENT GPUS
We provide encoding / decoding latency of different SOTA models both A100 and RTX 3090 GPUs. As shown in Tab. [O] our model achieves much lower encoding latency and decoding latency compared previous Mamba-based LIC models (for both A100 and 3090), demonstrating the effectiveness of our high-efficiency adaptive strategies.
26


Reviews:
[{"review_id": "jCiDe8meOR", "reviewer": "Reviewer_72TK", "summary": "This paper presents a mamba-based image compression method that enhances mamba’s adaptability to the input content. In mamba, the predefined raster (or multi-directional) scans are content-independent, which limits the effective elimination of redundancy between tokens that are content-correlated but spatially distant. To address this, the authors introduce a content-adaptive token permutation strategy to enhance interactions between similar tokens. Additionally, they adopt strategies from mambairv2, such as injecting sample-specific global priors into the state-space model. Experiments demonstrate the method's effectiveness, surpassing VTM-21.0 by 15.91%, 21.34%, and 17.58% in BD-rate on the Kodak, Tecnick, and CLIC datasets, respectively.", "questions": "Please refer to weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tThe writing quality is good, and the explanation is clear.\n2.\tThe motivation for improving mamba’s fixed scanning order is reasonable, and enhancing adaptability is a valid direction.\n3.\tThe paper provides a comprehensive evaluation of complexity, including model size, FLOPs, latency, and memory usage.\n4.\tThe ERF visualizations clearly demonstrate the model's global perception ability and content adaptiveness.", "weaknesses": "5. The paper compares only with MambaIC from CVPR 2025, ignoring LALIC and DCAE. Considering that LALIC and DCAE released code several months ago, a comparison with these methods is essential.\n6. The transform module in CMIC appears to be much deeper than in other methods. In Stage 2 and Stage 3, there are four blocks of attention and CAM in total—is this correct?\n7. The proposed Learnable Prompt Dictionary is essentially the Attentive State Space Module from mambairv2. The authors do not mention this in the paper, but since they cite mambairv2, they must be aware of it. I believe this is problematic and undermines the paper’s contribution and originality.\n8. In the supplementary materials, the authors compare their method with Zhang et al.’s approach. Are there quantitative results that align with other modules?\n9. Based on Figure 1, it seems that the clustering only scans within the current class. Does this harm the global nature of the method?\n10. In the ablation study, using just the 2D mamba outperforms MambaIc—what is the reason for this?\n11. According to Tables 2 and 3, removing CTP and GPP results in worse performance than just using 2D mamba. However, using pure mamba + CTP + GPP (CAM) does not match CMIC. What is the reason for this inconsistency?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "r8oKkyjC5V", "reviewer": "Reviewer_GLne", "summary": "This paper proposes a clustering based content based scanning method and a global prompt based non causal modeling approach to address the issues of content independent scanning order and causal characteristics in the Mamba structured learned image codec. Ultimately, while preserving the original linear complexity of Mamba, this approach is effective, ,  reducing BD-rate by 15.91%, 21.34%, and 17.58% on the Kodak, Tecnick, and CLIC datasets over VTM-21.0.", "questions": "- I can understand that the method proposed in this paper can improve the memory consumption of Mamba based image codecs, but further analysis is needed to determine the source of the model's performance improvement, including comparisons with existing Mamba-based methods such as MambaIC, and the benefits of visualizing/analyzing the proposed non causal approach.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Good RD performance while maintaining low complexity\n- Relieve the high memory usage problem of prior Mamba based learned image codec", "weaknesses": "- The methods proposed for both clustering partitioning and global prior modulation are common in prior \"content-adaptive\" studies, such as Rounting Transformer [1], even in the field of learned image compression, there are similar jobs available [2].\n- The main contribution of this paper is to improve the Mamba module in the Mamba-based learned image codec. However, it seems that there are many other differences compared to the previous works, e.g., MambaVC and MambaIC, including the entropy models, etc. The paper lacks detailed performance analysis.\n- Regarding the difference in receptive fields, according to Appendix Fig.16, it seems that this global receptive field and the so-called content aware features are also present in MambaIC, What is the actural difference?\n- The paper lacks a performance comparison with the latest methods listed, e.g., [3].\n\nRef:\n\n[1] Roy A, Saffar M, Vaswani A, et al. Efficient content-based sparse attention with routing transformers[J]. Transactions of the Association for Computational Linguistics, 2021, 9: 53-68.\n\n[2] Zhang Y, Duan Z, Lu M, et al. Another way to the top: Exploit contextual clustering in learned image coding[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(8): 9377-9386.\n\n[3] Li Y, Zhang H, Li L, et al. Learned image compression with hierarchical progressive context modeling[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 18834-18843.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6vWrRKNbC3", "reviewer": "Reviewer_QkwG", "summary": "This paper addresses the issues of standard Mamba-style state-space models (SSMs) in learned image compression (LIC), namely content-agnostic fixed scanning (failing to effectively associate tokens that are spatially distant but content-similar) and strict causality (solved by multi-directional scanning yet with quadrupled complexity). It proposes Content-Aware Mamba (CAM), which relies on two core innovations: content-adaptive token permutation (reordering tokens by feature-space proximity via a shared learnable codebook) and global-prior prompting (injecting sample-specific global priors to relax causality).", "questions": "See the Weaknesses for details.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "1. The overall writing of the paper is clear, and the figures are well-presented. \n2. The proposed method achieves the optimal experimental performance.", "weaknesses": "1. It is necessary to clarify the specific differences between the proposed method and Mambairv2 [1].\nBoth content-aware selective scanning and Learnable Prompt have been proposed in Mambairv2. \nThough I understand that Mambairv2 and the proposed CMIC are applied to two different tasks, the designs in this paper are mostly borrowed from Mambairv2 in a direct manner. There is insufficient technical contribution or further insights beyond Mambairv2 and specifically benefits image compression.\n2. The comparison with the latest approaches, such as MLICv2 [2], is missing.\n\n---\n[1] Guo, Hang, et al. \"Mambairv2: Attentive state space restoration.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[2] Jiang, Wei, et al. \"MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression.\" arXiv preprint arXiv:2504.19119 (2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4Vhkg4q9cc", "reviewer": "Reviewer_ysic", "summary": "The paper proposes Content-Aware Mamba (CAM) for learned image compression. This paper proposes two contributions: (1) content-adaptive token permutation via a codebook/K-means clustering that reorders tokens to put feature-similar patches adjacent before a 1-D selective scan; and (2) global-prior prompting, which injects sample-specific “prompts” derived from cluster assignments into the Mamba output projection to relax strict causality. On Kodak, Tecnick, and CLIC, CMIC claims strong BD-rate gains over VTM-21.0 and recent LIC models, with moderate compute and memory.", "questions": "Please see the weakness part. Overall, I find the paper satisfying. The paper would be further improved if the authors addressed the weaknesses above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear motivation & design: Shows how fixed raster scans and causality in SSMs are misaligned with image structure; proposes permutation + prompting to address both.\n2. Empirical results: Solid BD-rate improvements on three standard datasets; Comparable complexity numbers (params/FLOPs/latency/memory).", "weaknesses": "1. Possible shape mismatch: The prompting equation\n    \\[\n    O_i = (C + P) h_i + D x_i\n    \\]\n    leaves the shape of $P$ ambiguous. Earlier $P \\in \\mathbb{R}^{N \\times d_s}$ (per-token prompt vectors), whereas $C \\in \\mathbb{R}^{d \\times d_s}$. Adding them is dimensionally inconsistent unless $P$ is lifted to $\\mathbb{R}^{d \\times d_s}$ per token or used to gate $C$ via a mapping. Please clarify this precisely (e.g., broadcast).\n\n2. Report encoding latency (not only decoding). Only decoder latency on A100 is provided.\n\n3. The paper does not justify this permutation design choice. It would be strengthened by a comparison to simpler, end-to-end differentiable alternatives for learning a permutation, such as using attention scores for sorting, Gumbel-Softmax sampling, or an optimal transport-based approach.\n\n4. The ablation study in Appendix A.3.2 shows that replacing convolutions with CAM blocks in the entropy model provides negligible gains and increases latency. While the authors conclude that the entropy model benefits more from local context, this finding is interesting and slightly undermines the generality of the CAM block. A brief mention of this limitation in the main paper's ablation study would provide a more balanced perspective.\n\n5. The core idea of clustering tokens for content-adaptive processing is not entirely new. The authors cite Zhang et al. (2024), which also uses a clustering scheme. While the authors provide a good discussion in Appendix A.2, differentiating their fine-grained, non-Euclidean clustering from the coarse, grid-anchored approach of Zhang et al., this critical comparison is relegated to the appendix. For a top-tier conference, this discussion of related work and key differentiators should be integrated into the main paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)