Autoregressive-based Progressive Coding for Ultra-Low Bitrate Image Compression
Authors: Ziyuan Zhang
Yichong Xia
Bin Chen
Tianwei Zhang
Hao Wang
Han Qiu
Venue: ICLR 2026 Poster (2026)
Keywords: lossy image compression
autoregressive model
Primary area: generative models

Abstract: Generative models have demonstrated significant results in ultra-low bitrate image compression, owing to their powerful capabilities for content generation and texture completion. Existing works primarily based on diffusion models still face challenges such as limited bitrate adaptability and high computational complexity for encoding and decoding. Inspired by the success of Visual AutoRegressive model (VAR), we introduce AutoRegressive-based Progressive Coding (ARPC) for ultra-low bitrate image compression, a progressive image compression framework based on next-scale prediction visual autoregressive model. Based on multi-scale residual vector quantizer, ARPC efficiently encodes the image into multi-scale discrete token maps and controls the bitrates by selecting different scales for transmission. For decompression, ARPC leverages the prior knowledge inherent in the visual autoregressive model to predict the unreceived scales, which is naturally the autoregressive generation process. To further increase the compression ratio, we target the VAR as a probability estimator for lossless entropy coding and propose group-masked bitwise multi-scale residual quantizer to adaptively allocate bits for different scales. Extensive experiments show that ARPC achieves state-of-the-art perceptual fidelity at ultra-low bitrates and high decompression efficiency compared with existing diffusion-based methods.

Published as a conference paper at ICLR 2026
AUTOREGRESSIVE-BATE PROGRESSIVE CODING FOR ULTRA-LOW BITRATE IMAGE COMPRESSION
Ziyuan Zhang', Yichong Xia Bin Chen²s, Tianwei Zhang', Hao Wang*,, Han Qiu 'Tsinghua University, China Harbin Institute of Technology, Shenzhen, China Nanyang Technological University, Singapore *Peking University, China PPngg Cheng Laboratory, China 601.A,, China
eiyuan-z23@malll testttinniinn singhua. wacphauaaeduu...o edu. cn, giuhan@tsinghua edu.
ABSTRACT
Generative models have demonstrated significant results in ultra-low bitrate im- age compression, owing to their powerful capabilities for content generation and texture completion. Existing works primarily based on diffusion models still face challenges such as limited bitrate adaptability and high computational complexity for encoding and decoding. Inspired by the success of Visual AutoRegressive model (VAR), we introduce AutoRegressive-basee Progressive Coding (ARPC) for ultra-low bitrate image compression, progressive image compression framework based on next-scale prediction visual autoregressive model. Based on multi-scale residual vector quantizer, ARPC efficiently encodes the image into multi-scale discrete token maps and controls the bitrates by selecting different scales for trans- mission. For decompression, ARPC leverages the prior knowledge inherent the visual autoregressive model to predict the unreceived scales, which is natu- rally the autoregressive generation process. further increase the compression ratio, we target the VAR as probability estimator for lossless entropy coding and propose mosu-d bitwise multi-scale residual quantizer to adaptively allocate bits for different scales. Extensive experiments show that ARPC achieves state-of-the-ar perceptual fidelity at ultra-low bitrates and high decompression efficiency compared with existing diffusion- based methods. All source code is available at httpt:::: //tp://gttttt com/ Joanna 0421/ARB
INTRODUCTION
Neural image compression [Lu et al. 2023) has demonstrated superior performance than traditional methods (Wallace 199T) for ultra-low bit compression tasks (Xia et al. (025). Recently, generative models, such GAN (Goodfellow al.) 2020) and diffusion model (Rombach et al. (022), are ex- plored for image compression at ultra-low bitrates and optimized for deeediittooo-prrepeeeee & Michaeli 2019), preserving texture details to improve human-pent penaa-perceptual performance.
(Blau
Diffusion- based approaches have been proven to outperform GAN-based approaches for low bitrate image compression and achieve significant breakthroughs in the perceptual quality when decompress- ing. However, they still face three issues. 1) Limited bitrate adaptability. Most diffusion based
approaches (Careil al. 2023 Li et al. 2024c Xia al. 2025) follow the one-model-per-wee training paradigm, which makes rate adaptation in real dynamic transmission environments difficult.
2) High encoding and decoding complexity. Although recent works (Theis al. 2022 Vonderfecht & Liu 2025) have explored progressive coding by combining pretrained diffusion models and reverse channel coding, the diffusion models' iterative denoising nature still introduces inevitable complexity. 3) Infinite shared randomness. The reverse channel coding requires that the sender and receiver must share randomness, which may not always be available (Lei et al.) 2025).
In this paper, we aim to address above three issues via exploring using Visual AutoRegressive model (VAR) for image compression. VAR's next-scale prediction utilizes multi-scale residual quantizer to encode an image into discrete, hierarchical visual tokens and later predict tokens progressively
TThe corresponding author

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Published as a conference paper at ICLR 2026
ARPC 0.025bpp
Original
Original
Original
APRC
APRC
0,025bpe
0.025bpp
DiffPC
0.029bpp
0.036bpp
DIMEIC
PerCo
0.031bpp
Figure 1: Qualitative comparison between ARPC and diffusion- based methods. ARPC effectively
reconstructs line-grained textural details, while other methods exhibit noticeable texture loss.
from coarse to fine an autoregressive manner. Thus, our first key insight is that this coarse-to- fine paradigm naturally aligns with perfect bitrate adaptability by transmitting coarse but crucial information first, such as layout, followed by progressively adding fine-grained texture details gradually enhance image quality. Then, compared with diffusion-basee approaches, VAR has two additional advantages including faster generation process and no requirements for sharing randomness between the sender and receiver.
Motivated by the above insights, we explore the potential of VAR for ultra-low bitrate image compres- sion tasks, and propose Aegeesgressvve based Progressive Coding (ARPC), an efficient progressive image compression framework for ultra-low bitrate image compression. When compression, ARPC utilizes the bitwise multi-scale residual quantizer to directly encode the images into bitstreams for transmission (K is the predefined number of scales for residual maps) and controls the bitrates by only transmitting the first bitstreams. To further improve the compression ratio, we utilize lossless entropy coding to re-encode the original bitstream based the probability predicted by VAR and propose the group-man group-masked bitwise multi-scale residual quantizer BMM-MMSRQ,, which further compresses the coarse information into fewer bits. For decoding, ARPC leverages VAR generate other - scale tokens in an autoregressive manner, naturally integrating the decompression task into the generative process of VAR, achieving progressive coding.
We compare ARPC with 13 state-of-the-art baselines on two commonly used image compression testsets, considering 6 perceptual metrics. Extensive experiments prove that ARPC significantly outperforms basclines across different metrics on all datasets at ultra-low bitrates. What's more. ARPC achieves higher decompression efficiency, 2~-6x faster than diffusion-bassee methods. Figure illustrated that with the strong image prior of VAR, ARPC can achieve superior perceptual and statistical fidelity, effectively reconstructing fine-grained texture details with high realism.
RELATED WORKS
2.1 NEURAL IMAGE COMPRESSION METHODS
Learned image compression methods have achieved remarkable rate-distortion performance compared to traditional image compression standards like JPEG (Wallace 1991). Some methods focus on the coding process, considering the probability estimation accuracy to increase the compression ratio, by introducing hyperprior entropy model (Ballé et al. 2018), autoregressive context modeling (Minnen al. 2018), discrete Gaussian mixture model (Cheng et al. 2020), and spatial-channel contextual
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adaptive model (He al. 2022a). Other methods focus on optimizing the model architecture
to improve feature extraction ability through well-designed modules, such as mixed transformer- CNN (Liu al. 2023) and frequency-aware transformer (Li et al., 2023a).
Although the above VAE-based methods have achieved good performance, optimizing solely for mean square error (MSE) can lead excessive image smoothing and low statistical fidelity at low bitrates. [Blau & Michaeli 2019) proposes to add perceptual term in loss function to align well with human perception, which is used in many recent works (Mentzer et al. [220 Muckley al. 2023
He et al. 2022b). combining generative adversarial network and a perceptual loss term. Due to the strong prior of images and robust language understanding, text-to-image diffusion model (Rombach)
et al. 2022) has been explored for image compression tasks (Zhang et al., 2024] 2025b) and further improves the image fidelity and semantic consisteney at ultra-low bitrates. (Leei et al.] 2023) transmits
image caption and image sketch as conditions for ControlNet (Zhang et al. (023), enabling the
image compression at utral-low bitrates. (Li et al., 2024c) and (Xia et al. 2025) utilize pre-trained stable diffusion to reconstruct images under the guidance of latent VAE features or image-level and semantic-level control flow. Although these methods significantly improve the image fidelity, they all suffer from long decoding time due to the large number of denoising steps. Recent works explore accelerating the decoding process by initiating denoising process from compressed latents (Li) et al. 2025). ) dynamically aligning denoising steps with bitrates (Ke et al.. (025) and leveraging the distilled SD-Turbo prior for one-step reconstruction (Zhang et al.] 2025a). All above methods need to train separate models for each bitrate, making rate adaptation difficult. (Theis et al. (2022) proposes DifIC to transmit the intermediate corrupted version of the diffusion process to achieve progressive compression based on reverse channel coding, assuming that shared randomness exists between the encoder and the decoder. (Vonderfecht & Liu 2025) further improves the DiffC algorithm and gives the first complete implementation considering the computational complexity of reverse channel coding, but still suffers from long compression and decompression time.
2.2 DISCRETE VISUAL TOKENIZATION
VQVAE (Van Den Oord et al. 2017) first introduces the concept of discrete latent space, transform- ing the continuous representation into discrete vectors using trainable codebook. With further development, such as LFQ Yu et al. 2023), BSQ (Zhao et al. 2024), and residual vector quantiza- tion (Zeghidour al. 2021). discrete visual tokenization has also been widely used for compression tasks, such as audio compression (Zeghidour et al. 202T), image compression (Careil et al.) 2023
Mao et al. 2024 Guo al. 2025), and video compression (Tian et al., 2023). ). By transmitting
the indices of discrete vectors, the compression methods based on codebooks can not only control the upper bound of bitrates (Careil et al. (023) but also avoid inconsistent probability distribution estimation problems in cross-platform scenarios (Tian et al. (202)). However, to ensure the low bitrate, the vocabulary size of the codebooks often small, which can't 't cover all image features, resulting in suboptimal compression performance and low perceptual fidelity.
2.3 VISUAL AUTOREGRESSIVE MODELS
Driven by the great success of LLMS (Brown al. 2020 Touvron al. 2023), visual autoregressive models utilize discrete visual tokenizers, such as VQVAE (Van Den Oord et al., 2017). to convert
image patches into index-wise tokens, generating images based on next-token prediction (Lee et al.) 2022, Wang et al. 2024) or next-scalc prediction (Tian et al. 2024 Han et al., 2025). Based on next-scale prediction, VAR TTan et al. 2024) makes the autoregressive model surpass diffusion transformers (Peebles& & Xie, 2023) for the first time, considering image quality, inference speed, and scalability. Infinity (Han et al., 2025) further proposes bitwise modeling using BSQ (Zhao) et al. 2024), improving the scaling and visual detail representation capabilities of VAR. VAR has also been further expanded to other tasks, such as conditional generation (Li et al. 2024b). image restoration (Wang et al. 2025), ) and image super-resolution (Qu et al. 2025). demonstrating the potential of VAR for various tasks. However, to the best of our knowledge, the potential of VAR for image compression task remains unexplored.
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Select k to stop tranemission (adjust compression ratio)
VAR output
Bit labels
Ps e Rehentoress
classifier
Image encoder
Arithmelic
0.1
0.9
Causal VAR transformer
Inasteo
Arittmmaii dacoda based
Genoration sampling based
Image decoder
02:0--
CC
Q: Group- bitwise
multi-scale residual quantizer
Lossless decompression
Lossy decompression
Visualization of Fi
Grouptt
Group2:
35
Group3:
Figure 2: Overview of ARPC. ARPC leverages coarse-to-fine generation paradigm VAR to achieve progressive compression (section 3.2). ARPC encodes the image into K scale tokens and applies arithmetic coding for lossless compression based on the probability predicted by VAR (section 3.3) To further improve the compression ratio, ARPC utilizes group masks to enhance the original quantizer to compress image into fewer bits (section 3.4.
3 METHOD
In this section, we first introduce the preliminary of visual autoregressive model (section 3.1 Then, we give the detailed explanation of the ARPC's framework achieve the progressive compression and decompression (section 3.2). To improve the compression ratio, we use lossless entropy coding to re-encode the indices of the tokens (section 3.3). Based on the observation of visualization for
different scale residual token maps, we propose group-masked bitwise multi-scal residual quantizer (GM-MMSRQ) This enhances the original quantizer by achieving a more compact representation at earlier scales, thus further improving the compression ratio at ultra-low bitrates (section 3.4).
3.1 PRELIMINARIES: VISUAL AUTOREGRESSIVE MODEL
We focus on the VAR based on next-scale prediction, where the basic unit of autoregression the entire scale, containing sequence of tokens. For an image the image encoder first encodes x into feature map E hxxxxe and then quantize the feature map F into K scale residual maps, denoted (R1, Re...., Rx). The resolution of RR, (h& X wx). grows larger gradually from 1 + K. Each scale corresponds to hyg X Wp tokens. For simplicity, we use scale tokens to denote the sequence of tokens of each scale. We denote F₂ the cumulative sum of the upsampled ReE F6- Σ upsamplel (h, w)). As increases, F& gradually approximates the continuous feature map F. Such quantization method ensures coarse-to-fine paradigm, with earlier scales presenting the crucial structural information, while the subsequent scales progressively add finer details. We give the visualization of F3. in Figure 2 Inspired by Infinity (Han et al. 2025), we use bitwise multi-scale residual quantizer, where Tt. R((td) J.
In the discrete latent space, the VAR learns to predict the next scale tokens Re+1 conditioned on previous scale tokens Rec and text input t, which can be formulated as:
K P(R1, Rg .... Rs) II P( Re-1,C),
(1)
where C' is the text embedding, which also projected into the beginning token <SOS>, and p E TR xwp xex2 is predicted by binary classifiers, presenting the probability of next scale tokens.
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3.2 PROGRESSIVE COMPRESSION WITH AUTOREGRESSIV FRAMEWORK
The basic idea behind our method is based on the coarse-to-fine paradigm, which prioritizes the transmission of crucial data, the earlier scale tokens of the multi-scale residual quantizer. To achieve this, ARPC only transmits the first k scale tokens and generates other K - k scale tokens by VAR, leveraging the generation capacity to add finer details, reconstructing high realism images. Note that ARPC can achieve progressive coding through selecting different & scales for transmission, which enables the single compression model to support multiple bitrates.
At the sender end, we encode an image x based on bitwise multi-scale residual quantizer, as shown in Figure 2 {(x)
R1 Rg..... Rg). Unlike transmitting the index of codebook in token-based methods (Careil et al. 2023 Guo et al., 2025), bitwise quantizer maps each c-dimensional vector to e-bit binary code (i.e. for positive, O otherwise), which can naturally be considered as bitstreams. The k-th scale tokens, R2, naively needs h2 Wy bits for transmission. What's more, use BLIP2 model (Li et al.] 2023b) to generate an image caption as a global semantic context, transmitted to the receiver end along with the first & scale tokens.
At the receiver end, we get the first scale tokens Rgk, and use them as the prefixed content to predict R>x k through K - k autoregressive processes. All K scale tokens are upsampled into the same resolution and concatenated together, then input into the image decoder 'D to get the reconstructed image vs = D(R,, Ry: fl,.,..., Rg)).
To get better reconstruction quality, we analyze the distortion of the reconstructed image 24 (k denotes only the first & scale tokens transmission between sender and receiver) and demonstrate that the distortion D₃ d(æ, zz) has an upper bound:
Theorem 3.1 Given an input image x, encoder E, decoder D. Let (Rea, Ry5) = Exx) be the ground truth tokens and œœ D(RRe,, Ryk)) be the reconstructed image from ground-truth tokens, with distortion Dx d(z,0k) Let Ry* be the tokens predicted by VAR based on Rea, and I& = D((Rck, R)x)) be the final reconstruction, with distortion D = ((xx.2z) We have:
E [D S E [x] C ERg.. [D (p (Rg))) Il (R>)(Rgt))--
(2)
See the proof in Appendix A.I. It indicates the distortion be optimized by minimizing two key components: 1) the reconstruction error between the original image and the image decoded from ground-truth tokens, and 2) the KL divergence between the true and predicted distributions of the scale tokens. Thus, the training has two stages. First, we train image encoder, decoder, and quantizer to minimize the first distortion term in Equation 2
following:
Lfirst Free + Lper + Ldis + Lcommit + Lentropys
where Free is the L1 loss between reconstructed image JK and input image Sper is the perceptual loss, Lati is the discriminator loss, Leommit is MSE loss in the discrete latent space and Lentropy is the entropy loss used for bitwise quantizer (Zhao et al.) (024). Second, we optimize the VAR to minimize the KL divergence between the true and predicted distribution, following:
K
AV =
Σ
log po ( R4 Rei)
3.3 LOSSLESS RE-ENCODING FOR DISCRETE INDICES
To further improve the compression ratio, (Careil al. 2023) assumes that the token indices satisfy uniform distribution for lossless entropy coding. Different from the vector quantizer, bitwise quantizer index each token using binary code, denoted as y) j) = Σn=d Re(E..n)>00"" where i, j is the spatial coordinate and n the channel coordinate of the scale tokens. However, each element of yv (i. j) doesn't 't satisfy the uniform distribution (i.c., 0.5 for and 0.5 for 0), they represent semantic information of the image and are therefore dependent on the previous context. Interestingly, notice that the learning objective of the VAR is to accurately predict such distribution conditioned on previous scales, as illustrated in section 3.1 This provides the potential for more effective entropy coding, as the distributions are highly accurate.
At the sender end, we target the VAR as probability estimator, which uses c binary classifiers to predict independent distributions for each token, denoted Pk(sJ) E Rex2 We use arithmetic
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coding (the details can be found in Appendix A.3 a lossless entropy coding, to further compress the token indices based on рІ. For compression, we follow the autoregressive process. First, we use the beginning token <SOS> predict the probability of the first scale, Pl, to encode the indices yı- Then, the bit indices of each scale can be encoded by the probability predicted VAR conditioned on previous scales until all & scale indices encoded in autoregressive manner. The decompression can be divided into two stages: the lossless decompression and lossy decompression. The lossless decompression is the same as the compression process, predicting the probability of each scale tokens from the first to the k-th, and using arithmetic decoding for lossless decompression. For lossy decompression, we leverage the generation capacity of VAR and use classifier free guidance on predicted logits to generate K k scale tokens to get the final decompressed image.
3.4 GROUP-MASKED BITWISE MULTI-SCALE RESIDUAL QUANTIZER
ARPC achieves ultra-low bitrate compression by transmitting only the first few scale tokens. The first few scale tokens contain the image's 's most crucial coarse sansse-rained information, such as the image structure and color. Although crucial, they are low-frequency information with less detail, which can be compressed into a smaller feature space. Bitwise multi-scale residual quantizer downsamples the feature maps from spatial dimension to create less tokens. Inspired by the product quantization (Guo et al. 2024), which divides a high- dineeniienii vector into several low- low-dimensional tokens using different quantizers, we propose to enhance the bitwise multi-scale residual quantizer with group masks, compressing the feature space of the first few scales considering both resolution and channel dimension, to further increase the compression ratio at ultra-low bitrates.
First, we observe each scale, noting that they have clear hierarchy and can be broadly divided into three groups, as shown in Figure The first four scales present large color blocks, establishing the macro color layout of the image. The subsequent five scales begin to show the basic outlines and structure of objects, but still lack details, resulting in the blurriness of edges. The last four scales significantly improve the fine textures of the images. Based our observation, into three groups and propose group-masked bitwise multi-scale residual quantizer (GM-BMSRQ), masking the channels of scale tokens. For feature map F E Rhxwxx with the original scale tokens R& E Rh X xc we mask the last channels for the first group, setting to -1 as inactive bits, and the last J for the second group. Such method adaptively creates more compact representation of information based on the content of each scale, while further reducing the number of bits required for the transmission of the first two groups.
divide K scales
Besides reducing the token bit number, smaller value of k leads to a lower bitrate. However, et al. 2024a) reveals that the image semanties are concentrated in the final few scale tokens, which means that if we only transmit the first few scale tokens, the receiver will get little useful information for decompression, resulting in low reconstructed image quality low bitrates. To solve this problem, we apply scale random dropout (SRD) strategy during training to enhance the semantic information preservation in the earlier scale tokens, retaining more semantic information at ultra-low bitrates.
EXPERIMENTS
4.1 EXPERIMENT SETUP
Datasets. We use Coyo-700M (Byeon et al. 2022), large-scale image-text pair dataset, as our training dataset. We curated our training dataset through multi-stage pipeline. We first select images with resolution greater than 1024x x 1024, and then exploit an OCR model to filter undesired images with too much text. Finally, we utilize the advanced Intern VI. 2.0 model (Gao et al. 2024) to re-caption all filtered images to provide more accurate and detailed annotations. Our final training dataset includes 5M highly curated images with detailed captions.
For evaluation, we validate ARPC on two widely used image compression benchmark datasets: DIV2K val (Agustssoon & Timofte 2017) and CLIC2020 (Toderici et al. 2020), which consist of 100 and 428 high-definition images, with the captions generated by BLIP2 model. We reference (Xia et al.) 2025 Guo et al.) 2202)) to center-crop all original images to 1024x 1024 resolution for evaluation. As Kodak lacks extensive validation, we present the experimental results in Appendix A.4

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Diffc DITTEIC
RDEIC ResULIC
OSCAR
VQGAN
StableCodec
ARPC
FID
KID
FID
KID
2
2-12
21
2-16
0.00
0.04
0.06
0.04
0.00
0.06
0.04
0.06
DISTS
LPIPS 1
DISTS 1
LPIPS J
0.6
0.30
0.8
0.25
0.5
0.25
0.5
0.20
0.4
0.20
0.4
0.15
0.15
0.30
0.3
0.10
0.2
0.05
0.00
0.02
0.04
0.06
0.00
0.02
0.04
0.06
0.00
0.02
0.04
0.06
0.00
0.02
0.04
0.06
PIEAPP
CLP Score
PIEAPP
CLIP Score
100
100
3.0
2-
90-
2.5
80
2-
1.5
1.0
0.00
0.02
0.04
0.06
0,00
0.02
0.04
0.06
0.00
0.02
0.04
0.06
0.00
0.00
0.04
0.06
DIV2K validation set
CLIC2020 test set
Figure 3: Quantitative comparisons with SOTA methods on the DIV2K and CLIC2020 datasets.
Model training details. The training has two stages. First, we train the image encoder and decoder with the group- bitwise multi-scale residual quantizer, which is configured with three groups using channel dimensions of 8, 12, and 16, respectively. The training begins with 500k iterations on 256x256 images, followed by an additional 300k iterations on mix of 256x 512x512, and 1024x 1024 resolutions. The scale random dropout strategy used with a probability of 0.2 and applied from the fourth scale. In the second stage, we freeze the image encoder and decoder, use Infinity-2B as the visual autoregressive model, and finetune it for 20k iterations using the AdamW optimizer with batch size of 64 and learning rate of 6 x 10-3 on 1024x 1024 images with text captioned by Intern VL 2.0 model. All experiments are performed on 8x NVIDIA H20 GPUs.
Metrics. We focus more perceptual quality at ultra-low bitrates following previous works Xia ct al.] 2025; Vonderfecht & Liu| 2025) and select 6 perceptual metrics for quantitative measures. For perceptual similarity comparison, we consider LPIPS (Zhang al., 2018) and DISTS (Ding et al. 2020). For human-centric visual quality comparison, we consider PIEAPP (Prashnani et al. 2018). focusing on human preference for perceptual fidelity, and CLIP Score (Hessel al. 2021). focusing on high-level semantic content comparison. For overall distribution comparison, we consider
FID (Heusel et al. 2017) and KID (Binkowski al. 2018), which capture the statistical fidelity between compressed and original images.
Baselines.
We compare ARPC with several state-of-the-arr neural compression schemes, includ- ing VAE-based, diffusion-basee and boken-based methods. For VAE- -b-dded methods, we consider ELIC (He et al. [2022a), optimizing for rate-distortion and MS-ILLM (Muckley et al. 2003),, optimiz- ing for www-ddistottoo-perreppiii
methods, we consider DIMEIC (Li al. al,220444 (022c)). DiffPC (Xia et al., 2025).. RD et al. (2025), ResULIC Ke et al. (2025), StableCodec Zhang et al. (2025a) and OSCAR Guo et al. (2025). ) which are all based on pre-trained diffusion model to reconstruct images conditioned on the decoded content variables. Furthermore, we consider DiffC (Vonderfecht & Liu 2025), which is also progressive compression method by transmitting the corrupted version of diftusion process. We use the DiffC based on the pretrained Flux-dev model. Token-based methods include VQGAN (Mao et al. 2024), GL.C et al. (2025) and DLF et al.)
For diffusion-
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Ground Truth
PerCo/bpp=0.0019
SC/bpp=0.0066
ARPC/bpp=0..019
ARPC/bpp=0.004
Ground Truth
OSCAR/bpp-0.c019
DiftC/bgp-0.0074
ARPC/bpp=0.000
ARPC/bgp=0.0073
Ground Truth
PerCo/bpp-0.007 DiffC/bpo=O.com 0.0000
JRPC//pp=0.0000 0.0022
ARPC/bpp=O.. 0088
Figure 4: Visual comparison of generative compression methods at ultra-low bitrates (<0.01bpp). SC denotes the StableCodec.
Ground Truth
DiffPC bpp=0.068
MS-ILLM bpp=0.093
DiffEIC bpp=0.069
OSCAR bop=0.072
ARPC
bpp=0.068
Ground Truth
RDEIC bpp=0.044
MS-ILLM bpp=0.052
DiffC bpp=0.047
OSCAR bpp=0.043
bpp=0.041
Figure 5: Qualitative illustrations of various methods on DIV2K and CLIC2020 datasets.
For all baselines, we retrain their models with our training dataset using their open-source code and default settings, achieving performance consistent with the original results.
2025).
4.2
MAIN RESULTS
Quantitative comparisons. Figure show the comparison results on DIV2K validation set and CLIC2020 test The results demonstrate that ARPC consistently outperforms most bascline solu- tions across all transmission rates, especially at ultra-low bitrates. Furthermore, ARPC significantly outperforms all baselines measured by the FID and KID metries, demonstrating our approach's 's excellent capacity for preserving statistical fidelity. We attribute this superiority over diffusion- based methods to the powerful generative capabilities inherent in autoregressive-bait Compared to DiffC, diffusion--ased progressive codec, ARPC outperforms it across all bitrates without the need for shared randomness due to latent feature discretization. Compared to token-based methods, which also utilize discrete token indices for transmission, ARPC achieves significantly better performance. This is due to two main factors: ARPC utilizes a larger codebook that covers more comprehensive image features, controlling bitrates by transmitting different numbers of scales,
architecture.
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BD-rate(S)
Methods
Steps
Encoding (8)
Decoding (8)
FID
DISTS
PIEAPP
PerCo Careil et 2023
20
0.20
10.25
1167.35
882.09
2744.75
DiftEI [Li et al.
2024c
50
0.65
15.98
681.76
139.64
100.75
DifiPC Xia et al. 2025
50
0.17
23.66
93,90
20.49
16.52
RDEIC cf. 2025
6
0.31
4.18
523.52
1469.67
761.27
StableCodec iZhang ct
2025a
1
0.42
1.11
0.1547
6,99
254.42
DiffC /Vondeftecht&
2025
3.63
45.22
13.63
37.25
674.37
59.62
117.11
ARPC
13
1.8-6.2
5.39
ARPC (w/o LRE)
13
0.20
5.39
34.64
34.58
34.38
Table Inference efficiency and BD-rate (%) comparison on CLIC2020 dataset (1024x 1024).
but not the vocabulary size of the codebook. What's more, ARPC leverages VAR to more accurately predict the distribution of token indices, further increasing the compression ratio.
Qualitative comparisons. We provide qualitative comparisons in Figure which shows the visual results at ultra-low bitrates (<0.01 bpp), and Figure 5 which shows the image detail comparison at higher bitrates. To ensure low bitrates, PerCo and OSCAR uses vocabulary size of 256, which can only cover simple image features. As illustrated in Figure 4 for simple image, such as a squirrel, PerCo can preserve the basic shape and semanties, but fails reconstruct the original colors. For more complex images, however, PerCo and OSCAR are unable to retain the original semantic information. DifCC and StableCodec successfully reconstructs the basic structure and semantic content of the image, but it suffers from a loss of fine details, such as the face of the human or the pin in the cup. ARPC maintains higher similarity in structure, semantics, and details, resulting in significant visual improvements. At higher bitrates, ARPC demonstrates a superior capability in reconstructing high-frequency details. As shown in Figure competing methods like MS-ILLM and OSCAR exhibit various degrees of blurring or structural distortion. In contrast, ARPC successfully preserves fine-grained textures, such as the delicate patterns and the speckles of light on leaves, demonstrating the remarkably superior visual quality and realism of ARPC with minimal bitrates.
Computational complexity compared with diffusion-basee methods. VAR achieves high genera- tion efficiency by adopting hierarchical generation strategy with small number of iterations. Thus, ARPC significantly reduces the decoding latency compared to the diffusion- based methods, which need longer inference time due multi-step denoising process. As shown in Table [ ARPC only requires 5.39s to decompress an image, which is twice as fast as PerCo and 2~6x faster than DiffC. Compared with one-step diffusion based methods, such StableCodec, although ARPC requires more decoding time, the most significant advantage of ARPC is the progressive coding, which can not be achieved by the one-step diffusion based method. Different from DiffC, which can initiate generation from an arbitrary denoising step, AR-based model predicts the k + 1-th scale depending on the first i scales. Consequently, ARPC starts decoding process from the beginning token and completes predefined number of steps to decode an image, resulting in a deterministic decoding time rather than a variable range. For encoding, due to ARPC targets the VAR as a probability predictor, transmitting ht scales tokens requires predicting the probability distribution of the first k scale tokens for arithmetic coding. Thus, the encoding time is variable, increasing with higher bitrates. Unlike diffusion- based methods, which exhibit consistent computational cost per iteration, the complexity of ARPC' c's encoding process very low at low bitrates with fewer token counts at early scales (refer to Appendix (A.7 for more details).
4.3 ABLATION STUDY
In this section, we conduct the ablation study by removing LRE (lossless re-encoding). SRD (scale random dropout), and GM-BMSRQ (group-masked bitwise multi-scale residual quantizer), and the quantitative results are shown Figure 7 Since both the LRE and the group mask for the BMSRQ are used to improve the compression ratio with little impact on the decompressed image quality, we only present the visual results without SRD, as illustrated in Figure

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Ground Truth
ARPC bpp=0.0025
w/o SRD bpp=0.0025
Ground Truth
ARPC bpp=0.0258
w/o SRD
bpp-0.0264
Figure 6: Visual comparison with and without scale random dropout strategy.
weo SAD
w/ LRE
WS GH-BHERQ
--- ARPC
W/ LRE. To demonstrate that the lossless tropy re-encoding based on the probability pre- dicted by the VAR efficiently reduce the bi- trates, we remove the arithmetic coding and di- rectly transmit the binary code quantized by the GM-BMSRQ. As shown in Figure 7 a bitrate re- duction of approximately 30% can achieved by applying arithmetic coding, without any impact on image quality. Without the computational over- head of autoregressive probability prediction, the encoding time is significantly reduced, requiring only 0.2 seconds, as shown in Table [
FID
KID
0.00
082
ni6e
0.06
0.08
0.00
0.2
0.04
0.06
0.00
LPIPS
DIST5
0.179
0.100
0.55
W/o SRD. To show that scale random dropout strategy enhance the representation capability of earlier scales, improving the fidelity of recon- structed images at low bitrates. We remove such strategy during the first stage training, and results are shown Figure 7 The results show that SRD plays an important role in decompressing images to get better perceptual metrics, especially at low bitrates. As illustrated in Figure [ under 0.01 bpp, SRD is crucial for maintaining the fundamental structure and color information. At higher bitrates, SRD can still help to preserve fine-grained details, such the patterns on the roof.
0.00
0.75
0.00
2002
0.04
0.00
0.03
0.00
0.02
0.24
0.26
0.02
PIEAPP
CLIP Score 7
2.2
2.0
50
18
10
52
12-
1.0
080
0.00
5,2
0.66
0.00
0.03
0.00
0.02
0.34
0.00
0.08
Figure Ablation study on the DIV2K dataset.
W/o GM-BMSRQ. To prove the group mask used for BMSRQ can improve the compression ratio at ultra-low bitrates, train the BMSRQ with the channel dimension of 16 without using group mask. As shown in Figure 7 removing the group mask leads to a noticeable rise in bitrates, demonstrating that group mask effectively improves the compression ratio, especially at ultra-low bitrates.
CONCLUSION
We introduce novel progressive neural compression framework, ARPC, which intuitively utilizes the coarse-to-fine ro-fine generation paradigm of VAR based on next-scale prediction to achieve progressive coding, and leverages the priors of the pre-trained VAR reconstruct images with high realism and visual quality at ultra-low bitrates. Extensive results indicate that ARPC can not only achieve SOTA progressive compression performance but also improve the decompression efficiency. We discussion the LLMs usage in Appendix|A.9 A.9
ACKNOWLEDGEMENT.
This work was supported by the National Natural Science Foundation of China (62576122, 62301189), and the Center of High performance computing, Tsinghua University.
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ETHICS STATEMENT
All the researches in this paper conform the ICLR Code of Ethic Our efforts include, but are not limited to: 1) The method we propose can be used for progressive image compression systems, which meets social needs, and can be broadly accessible, without threats to health, safety, personal security, and privacy. 2) All the datasets, pretrained models and baseline methods are used in ways consistent with their licences, with full respect given to their copyright and contributions. 3) Our experiments conducted with the best efforts to avoid potential biases. This includes using the same training dataset, testing with the same settings, and comprehensive set of metrics for evaluation. 4) Transparency and reproducibility are prioritized. We will release the source code and model checkpoints upon acceptance, and the detailed description reproduce method is given in the manuscript.
REPRODUCIBILITY STATEMENT
Experiments are described detail in section 4.1 including the pretrained model, the dataset, all the hyperparameters, and the model training details. The source code and model checkpoint will be released upon acceptance with detailed instructions including data access and preparation, the exact command and environment needed.
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A APPENDIX
A.1 PROOFS
Theorem 3.1 Given an input image æ, encoder &, decoder D. Let (Rg,, (()) be the ground truth tokens and æ "D( (ReA. R>*)) be the reconstructed image from ground-truth tokens, with distortion DR d(z, xx). Let Ry* be the tokens predicted by VAR based on Rgg, and I& D(k R>x)) be the final reconstruction, with distortion D d(x We have:
E [ E [Dx] + C. ERg, [DrL. (p (Ryx (Bgk)
l| (R>x|
Proof. According to the triangle inequality:
d (z, mx) S (z, xk) + d d
(6)
Dx - Dx + ( (D( Rox)), (Res,
(7)
Assuming that the decoder D satisfies the Lipschitz continuity:
d (D(((Re) Ryk)) D ((Rg)) L. (Rsk:Rs)
(8)
E [Da) < E [Dx] + I. B Rosi Rs))
(9)
D + L. Brgg
Σ PR(RE)
Σno ro (Ree|REa) (Rs,, (Bets
(10)
Rsk
Ass
< [D| + C. ERSE
DDL (p (Rsk(Re)))
llpa (Raa(Re)))
(11)
A.2 MORE DETAILS OF ALGORITHM PROCEDURE
We supplement the explanation of the encoding and decoding processes of ARPC through pseudocode. The detailed encoding and decoding processes are shown in algorithm and algorithm
Algorithm 1: Encoding process of ARPC.
input Input image x, image caption output: : Encoded bitstream Parameters:
Image encoder with group-masked bitwise multi-scale residual quantizer E, arithmetic codec A, select the first k scales for transmission.
(R,, R2. Rx) E(z);
SOS > = text projection (t);
Bilstreamy = A(R1.Po [Ri < SOS >,();
for i=2...,k do
Bitstream, A(R,, pe RR Ri.... R,-1, < SO8 >,t)); end
transmission Bitstream, Bitstream, and image caption
A.3 ARITHMETIC CODING
Arithmetic coding is form of lossless entropy coding. The core idea of arithmetic coding is to map an entire message to a single floating point number within the interval [0, Based on the probabilities of each symbol, the current interval is partitioned into multiple sub-intervals. Through an iterative process, a very small interval is obtained, and its lower bound is used as the encoded result. The pseudocode of arithmetic coding is shown in algorithm and algorithm 4
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Algorithm 2: Decoding process of ARPC.
input : :Bitstream,,... ,... Bitstreams. ..ntsrrram image caption
output: : Reconstructed image 2k
Parameters: Image decoder 'D arithmetic codec A
< SOS > text projection (t);
2 Bitstream; = A(R Pe [11 SOS >,t));
/* Lossless decompression
R1 = A(Bitstreaml,
< 50S >,)));
for i=2...,k do
R; = A (Bllstream; R2 Ri R3-1, < 5OS >,();
end
/* Lossy decompression
for I=.+ do
RG = Sampling(pul (R; Ri,... Ra; Pu+1, S0S 2,0);
end
10 FA D((RI,.... RR: R Rk)):
Algorithm 3: Arithmetic Encoder
Input: A binary sequence S ($1, 82, 82,..,,,.. SL), the probability symbol '0', denoted as po Output: The encoded value
// mmitialize the interval [low, high) to [0, 1)
low + 0.0;
range <- 1.0;
for each symbol in S do if's = '0' then
range + range po:
// Narrow the interval for 'o'
else
low low range Po:
// Shift the interval for '1' range range- (1-po): // Narrow the shifted interval
return low;
A.4 FURTHER EXPERIMENT RESULTS
We further consider the distortion metrics, PSNR and MS-SSIM, for comparison. The results are shown in Figure and Figure The last point the rate-distortion curve represents the case where all K scales of tokens are transmitted from the sender to the receiver. In this scenario, the receiver only takes the VAR a probability estimator for lossless decompression, compression performance at this bitrate is equivalent to the VAE's reconstruction performance. Since the reconstruction performance
for generation. Therefore, the
discrete VAE is worse than that of a continuous VAF, ARPC's compression performance shows slight drop compared DiffPC higher bitrates. However, it still achieves SOTA results on FID and KID, demonstrating ARPC's superior statistical fidelity.
Furthermore, we present the comparison results Kodak dataset, shown in Figure 10 We don't 't report the FID and KID score due to the limited size of Kodak dataset. The results show that ARPC maintains outstanding performance on the Kodak dataset well. We provide the qualitative comparisons in Figure [T The results demonstrate that ARP C can reconstruct images with complex and correct textures, such the color around the eye.
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Algorithm 4: Arithmetic Decoder
Input:
The encoded value v, the probability of symbol '0', Po, the length of the original sequence. L
Output: The decoded sequence Sout
low t- 0.0;
range <- 1.0:
Initialize an empty sequence Souti
for - to do
threshold low + range- Poi // Calculate the split point if v < threshold then 8 8 '0;;
range range po:
// Select the interval for '0'
else
st low t threshold; range range (1- (1-Po). po): // Select the interval for '1'
11
13
Append 8 to Souti
14 return Soati
4.2
2.5
2.5
3.4
24
3.5
3.45
3.2
2.5
3.5
1.4
16
1.00
0.32
100
0.22
077
6.15
50
4.0
5.55
2000
1000
Figure 8: Quantitative comparisons across both perception and distortion metrics on DIV2K.
A.5 FURTHER ABLATION EXPERIMENT
A.5.1 VOCABULARY SIZE OF BITWISE MULTI-SCALE SCALE RESIDUAL QUANTIZER
Improving the vocabulary size of the bitwise multi-scale residual quantizer can improve the recon- struction capability of the VAE, but increase the bitrates at the same time. We further explore the effect of increasing the vocabulary size on compression performance. We increase the channel dimension of bitwise multi-scale residual quantizer to e 32, and configure to be 16, 24, and 32, respectively. As shown in Figure [3] low bitrates, the decompressed image quality is similar for both models. However, the model with a channel dimension of 32 can achieve higher bitrates and exhibits better performance at higher bitrates. This is mainly due to the bitwise VAE has a better reconstruction performance with a larger vocabulary size.
channels for the three groups
A.5.2 ABLATION RESULTS OF BITWISE VISUAL TOKENIZER
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ELIC
VQGAN
DITC
DITEK: LMPS
MSILLM
Perco
DIYFCC
ARPC
1-111
VAE
PIEAPP
FID
PSNR
3.
3
3.55
3.50
1.3
0.2
1.4
3.000
6.055
0.000 3,0X 3100 0325
1U068
0.25
DOYS 0.000 3125
2.000 5005
3.70
2.7%
5.000
0.125
0.000
0.005
0,000
2.0%%
110 0128
DISTS
KID
CUP Score
M5 SSIM
5.25
0.22
6.5
4.5
3200
0.05 odte
3,00
3o0
0325
0.66 0525 0.5e
oet os a
2.000 8605 ad
3675
6.00
6125
0.000 0.00s
a aons 100 0.2s
Figure 9: Quantitative comparisons across both perception and distortion metries on CLIC2020.
orEC OFFC
nocc
DISTS
LPPS
PEAPP
CLI Score
2.0
044
6.1
0.3
70
0.50
0.11 602
0.00
666
6.5 3.0
341
0.2
aio
olt
0ss
000
651
3.00
6.3
ab4
0.00
6.1
3.0
300
Figure 10: Quantitative comparisons with SOTA methods on Kodak dataset.
We divide K scales into three groups based on the observation of each scale
Method
IN-512
IN-512 PSNR
and apply different group mask for each group, retaining the bitwise visual tok- enizer to adaptively allocate bits for dif- ferent scales. What's more, we apply scale random dropout strategy to enhance the se- mantic information preservation the ear- lier scales. We show the effect of the above two strategies on reconstruction quality of bitwise visual tokenizer in T We use the validation set of ImageNet for evaluation with the input resolution of 512x512. Since these strategies are all designed to improve the compression performance at ultra-low bitrates, either by reducing the vocabulary size of earlier scales or by focusing on earlier scales to retain more semantic information, they inevitably have a slight impact on the overall reconstruction capability of the VAE.
Original
0.31
22.6
W/ BMM-MMSRQ
0.56
22.12
W/ GM-BMSRQ
0.67
21.5
W/ SRD
Table 2: The effect of GM-BMSRQ and SRD on visual tokenizer.
A.5.3 ABLATION STUDY OF IMAGE CAPTION LENGTII
We use the BLIP2 model to generate the image caption in this paper. And we further evaluate if more detailed image caption bring better compression performance, as illustrated in Figure [4] We use DIV2K validation set as our testset and use the advanced Intern 2.0 model to provide more detailed captions. The results show that captions generated by BLIP2 and InterVL 2.0 yield similar compression performance. For CLIP Score, using the caption from Intern VL 2.0 model can achieve higher score ultra-low bitrates. This is mainly because the CLIP Score measures semantic similarity, and more detailed captions can provide more detailed semantics for the generation process. What's 's more, we evaluate the compression performance without image caption. As shown in Figure 14] at ultra-low bitrates, the absence of image caption has severe impact the quality of the decompressed image. The tokens of earlier seales primarily encode structural layout, lacking finer semantic details of the original images, making the decompression heavily dependent the guidance of a caption. As the number of transmitted scales increases, at higher bitrates, comparable compression performance is achievable even without caption.
18

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Published as a conference paper at ICLR 2026
Ground Truth
GLC bpp=0.030
DifIPO bpp=0.035
DIFfEIC
DiffC
ARPC
bpp=0.033
bpp=0.028
bpp=0.026
Ground Truth
RDEIC bpp=0.051
DiftPC bpp=0.060
DIFfEIC bpp=0.073
DiffC bpp=0.085
ARPC
bpp=0.050
Figure 11: Qualitative illustrations of various methods on Kodak dataset.
bpp=0 031
bpp=C.030
bop=0.030
bpp=0.024
bpp=0.082
Large face
bpp=0.00 031
bpp=0 032
bpp=0. 028
bpp=0 028
bop=0.031
Medium face size
bpp=0.031
bpp=0.040 0.000
bpp=0.022 0.026
bpp=0.034 0.040.034
bpp=0000 020
Small face size
Original
(b) PerCo
(a) DiFEEI
(a) DifCC
(a) DIAPC
(a) ARPC
Figure 12: The image containing human face.
A.5.4 ABLATION STUDY OF DIFFERENT MASK CONFIGURATION
Based observation, divide 13 scales into three groups of sizes 4, 5, and 4, respectively. We further give two different choices for masking, dividing 13 scales into (5,6.2) and (2,2.9). the BD-rates on DIV2K dataset with the default configuration (i.c., 4.5,4) the baseline. The results show that configuration 1, (5,6,2), compresses more information into low-dimensional feature space, which can achieve lower bi- trates. However, it severely compromises the representation capability of GM-BMSRQ, resulting degradation in reconstructed image. While configuration 2, (2,2.9), effectively preserves the representation capability, it leads to significant reduction in the compression ratio, especially at ultra-low bitrates.
BD-rate (%)
Configuration
DISTS
FID
(4,5,4)
0
(5,6,2)
25.69
64.19
(2,2,9)
17.29
29.87
Table 3: BDD-rate of different mask configuration for GM-BMSRQ.
A.5.5 COMPARISON WITH VANILLA VAR
To demonstrate the superiority of ARPC over the vanilla VAR, we report the compression performance of directly using VAR for encoding and decoding, and compared with the SOTA com- pression method, StableCodec, as shown in Table 4 The BD- rate is calculated on DIV2K dataset with ARPC as the baseline. In vanilla VAR, the image semantics are concentrated in the final few scale tokens, which is suboptimal for progressive compression. Moreover, the earlier scales represent coarse, low-frequency information with low information density, but they share the same high- sigh-dimensional feature space and code- book as later scales with high information density. This results in representation redundancy, leading to inefficient information transmission, particularly at ultra-low bitrates.
BD-rate (%)
Methods
DISTS
FID
Vanilla VAR
63.32
79.51
StablcCodec ARPC
10.78
22.47
Table 4: BD-rate comparison with vanilla VAR.
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Published as a conference paper at ICLR 2026
2
4
6
8
10
11
12
13
Encoding time
1.86
1,90
1.94
1,99
2.03
2.07
2.15
2.27
2.46
2.77
3.35
4.34
6.21
BPP
7.3e-6
3.4e-5
14c-
3.8e-4
9.6e-4
2.20-3
4.60-3
8.1o-3
0.013
0.025
0.042
0.064
0.108
Table 5: The encoding time and bitrate when selecting different k.
Image size
Encoding (s)
Decoding (s)
256 x 256
0.09 0.45
0.44
512 x 512
0.28 ~ 1.39
1.33
512 768
0.46 2.40
2.29
1024 1024
1.86 6.21
5.39
Table Inference efficiency with different input image size.
A.6 VISUAL RESULTS FOR SPECIAL SCENARIOS
We further present visual results for some challenging scenarios, such the images containing face and text. We categorized text and faces into three classes following DiffPC based on their proportions within the entire frame large, medium and small, shown in Figure 12 and Figure [5] For images containing face, ARPC can reconstruct high realism images even at low bitrates. While other diffusion-basee methods exhibit significant artifacts or texture loss. PerCo shows noticeable color incorrect. DifFEIC and DiffPC present oversmoothing of the skin for large and medium faces. For images containing lext, AREC can also achieve good image reconstruction quality, even for the small text, such as "TESLA.COM" under the license plate.
A.7 SELECTION OF k
A.7.1 COMPRESSION EFFICIENCY
As we illustrated in section 4.2 ARPC utilized the probability predicted by VAR to losslessly re- encode the token indices. As a result, the encoding time increases with higher bitrates, a large value of k. However, unlike the diffc, which has consistent computational cost per iteration, the encoding complexity low at low bitrates as fewer token counts at early scales. We further provide the specific encoding times and bitrates for different numbers of transmitted scales k. As shown in Table 5 when transmitting tokens from fewer than 10 scales, the encoding time is under 3 seconds and the bitrate is below 0.025 bpp. As the number of tokens in the last 3 scales increases, it leads increase in compression time. Furthermore, we provide the encoding and decoding times for different input image sizes, shown in Table 6 demonstrating the low decoding complexity of ARPC
significant
A.7.2 VISUAL RESULTS OF THE PROGRESSIVE DECOMPRESSION.
We further present the visual results of transmitting different scales from == to h: 13 (all discrete tokens are transmitted), as shown in Figure 16 For smaller (low bitrates), the decompressed image is similar to the original image only in terms of semantics and image layout. Therefore ARPC relies more on the VAR's generative capacity to reconstruct a high-realism image. As the value of increases, more tokens are transmitted, and the details of the image gradually completed.
A.8 LIGHTWEIGHT DEPLOYMENT FOR COMPUTATIONAL SCENARIOS
To demonstrate the potential for practical applications, we conduct further experiments using BLIP (a lightweight caption model) and consider lightweight deployment strategy. We give the parameter counts and BD-rate comparison in Table and Table g
Replacing BLIP-2 with BLIP: We replaced the large BLIP-2 model with the more lightweight BLIP model. As shown in the BD-rate table below, utilizing BLIP (w/ LRE) yields compres- sion performance very similar to the baseline BIIP-2.
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Published as a conference paper at ICLR 2026
PIEAPP
LPIPS
FID
2.00
30
1.75
15
1.00
0.125
DISTS
KID
CLIP Score
0.200
510
0.275
0.0025
0:330
0.0020
0.0015
0.300
0,0010
90
0.075
0.0005
0.050
0.000
8.255
0,063
3,075
0:100
0.125
0.000
0.025 0.050
a.dss
01000
0.125
0.000
5.025 0,050
5,075 0.100
0.125
Figure 13: Quantitative ablation study of vocabulary size for group-masked bitwise multi-scale residual quantizer. Tested DIV2K validation
InterVLL
PIEAPP
LPIPS
FID
2.5
2.0
2'
24
0.2
0.075
0.100
0.000
0.025
0.050
0.775
0.000
0.025
0.075
DISTS
KID
CUP Score
0.30
0.25
0.20
85
0.15
2-
80
0.30
2-1
75
70
Figure 14: Quantitative ablation study of image captions with different length.
Lightweight deployment w/o LRE: For scenarios strictly constrained by computational resources at the sender's s end (c.g., mobile devices), we can directly transmit token indices without lossless re-encoding (LRE). This eliminates the need deploy the VAR model the sender, requiring only the VAE encoder and the lightweight caption model. Such strategy reduces the parameters at sender's s end from 2.2B to 268.81 8... Although removing LRE results in BD-rate increase (~ 25%) due to the lack of probability estimation, it offers viable solution for edge devices.
Table 8: BD-rate comparison for lightweight deployment using BLIP and w/o LRE. The BD-rate is calculated on DIV2K dataset with using BLIP-2 cap-
Table 7: Model parameters for lightweight deployment using BLIP and w/o LRE.
tion model and LRE as the bascline.
Model
Sender Params
Receiver Params
BLIP
223.9N
Methods
BD-rate (%)
VAR (w/ VAE)
2.2B
2.2B
DISTS
FID
VAE
44.9M
65.0M
BLIP-2 + w/ LRE
0
Total (w/ LRE)
2.2B
2.2B
BLIP + w/ LRE
0.12
0.08
Total (w/o LRE)
268.8M
2.2B
BLIP w/o LRE
25.93
25.56
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Published as a conference paper at ICLR 2026
bpp-0.031
bpp=0 053
bpp-0.024
2.00=0.043
bpp-0.027
Large text aize
bpp=0.031
bpp=0.028
bpp-0.022
0.0p00002
bpp=0.022
Medium
text
bpo=0 031
bpp-0.016
bop=0.022 0.022
bop=0.014 0104
bpp=0.000 0.024
Small text size
(a) Original
(b) PerCo
(a) DIfEII
(a) DifiC
(a) DiffPC
(a) ARPC
Figure 15: The image containing text.
A.9 THE USE LARGE LANGUAGE MODELS
In this paper, we only use the LLMs for grammar checking and writing refinement as general- purpose assist tool. All scientific content, including methodologics, data, and conclusions, is proposed by the authors and not influenced by the use of LLMs. All usage of LLMs is conducted with careful oversight by the authors to ensure accuracy.
22

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Published as a conference paper at ICLR 2026
k=5
k=6
k=7
k=8
k=9
k=10
k=11
k=12
k=13
Ground truth
Figure 16: Visual comparison for different value k.
23


Reviews:
[{"review_id": "WLQ1zUmwLf", "reviewer": "Reviewer_joRB", "summary": "This paper introduces ARPC (AutoRegressive-based Progressive Coding), which leverages a Visual Autoregressive Model (VAR) based on next-scale prediction to for image compression. The VAR also serves as a probability estimator for near-lossless entropy coding. To further improve compression efficiency and semantic representation, the authors propose a Group-Masked Bitwise Multi-Scale Residual Quantizer (GM-BMSRQ) and a Scale Random Dropout (SRD) strategy. Experiments on DIV2K-val and CLIC2020 demonstrate that ARPC surpasses both diffusion-based and VQ/GAN-based baselines in perceptual metrics.", "questions": "(1)\tAre the bits used for text encoding included in the reported bpp statistics? How much bitrate do the captions contribute to the total bpp?\n(2)\tThe paper states that scale random dropout (SRD) is applied with a probability of 0.2 from the fourth scale during training—why is the fourth scale chosen as the starting point for SRD, and how does adjusting the dropout probability (e.g., 0.1 or 0.3) affect the model’s ability to preserve semantic information in earlier scales?\n(3)\tFor images with complex textures (e.g., dense text or fine-grained patterns), does ARPC require adjusting hyperparameters (e.g., number of scales K, channel dimensions of GM-BMSRQ groups) to maintain reconstruction quality, and if so, what guidelines exist for such adjustments?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "By transmitting only the first k scales and autoregressively completing the rest with VAR, it enables progressive transmission and adaptive bitrate control. The VAR module serves both as a generator and a probability estimator, reducing overall bitrate while supporting lossy and lossless compression. GM-BMSRO further enhances the bitwise multi-scale residual quantizer by group masking to early scales, while SRD encourages these scales to capture richer semantic information. Extensive evaluations across diverse datasets and metrics demonstrate the robustness and practical effectiveness of these techniques.", "weaknesses": "(1) The structural diagram (Figure 2) fails to explicitly show the image caption’s presentation form and functional mechanism. While the text states captions (generated via BLIP2) act as global semantic context to guide VAR’s autoregressive prediction of unreceived scales, Figure 2 lacks labels for the caption generation module.\n(2) The grouping logic and parameter settings of GM-BMSRQ lack sufficient justification. The paper proposes dividing the K scales into three groups and masking the last c/2 channels of the first group and the last c/4 channels of the second group to reduce bit cost. However, the rationale behind these key design choices is not explained: (i) the basis for selecting the specific channel masking numbers (e.g., c/2) is unclear, as no experiments compare different masking configurations; (ii) the logic for dividing scales into three groups rather than two or four, and the criteria for allocating scales to each group, is not discussed; (iii) the effect of different channel configurations (e.g., 8, 12, 16 channels) on compression performance is only partially explored through comparisons of c=32 and c=16, leaving the module’s design insufficiently validated and its optimality unproven.\n(3) The paper exhibits notable deficiencies in evaluating decoding efficiency and comparing with baseline methods. On one hand, although the ARPC decoding time is reported as 5.39s and claimed to be 2–6× faster than diffusion-based methods, the breakdown of this runtime is not provided—key steps such as VAR autoregressive prediction, arithmetic decoding, and image reconstruction are not individually profiled, making it difficult to identify efficiency bottlenecks and guide further optimization. More importantly, this decoding time is not compared against the current state-of-the-art in ultra-low bitrate image compression. On the other hand, the selection of baseline methods is limited and does not include recent mainstream approaches. To fully validate ARPC’s performance, comparisons should be extended to methods such as DLF[1] (Extreme Image Compression with Dual-generative Latent Fusion), GLC[2], and single-step diffusion methods like StableCodec[3], thereby providing a more comprehensive evaluation and clarifying ARPC’s position relative to the current research frontier.\n\n[1] N. Xue, Z. Jia, J. Li, B. Li, Y. Zhang, and Y. Lu, “DLF: Extreme Image Compression with Dual-generative Latent Fusion,” ICCV, 2025.\n[2] Jia, Zhaoyang, et al. \"Generative latent coding for ultra-low bitrate image compression.\" CPVR, 2024.\n[3] Zhang, Tianyu, et al. \"StableCodec: Taming One-Step Diffusion for Extreme Image Compression.\" ICCV, 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": "fH2faphSjU", "reviewer": "Reviewer_gRQE", "summary": "This paper proposes ARPC (Autoregressive-based Progressive Coding) for ultra-low bitrate image compression. An image is quantized into K multi-scale residual token maps; the encoder transmits only the first k (coarse→fine), and a visual autoregressive (VAR) model predicts the untransmitted scales at the decoder (next-scale generation). ARPC also treats the VAR as a probability estimator for arithmetic (lossless) entropy coding of the transmitted tokens and introduces a group-masked bitwise multi-scale residual quantizer (GM-BMSRQ) to reduce bits at coarse scales.", "questions": "- In your introduction, I noticed the term \"Infinite shared randomness.\" What exactly does this refer to? Is it about the impact of random seeds in diffusion models on the denoising process? This doesn’t seem to have a very significant impact.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Dual use of VAR . Using VAR both to compress transmitted bits losslessly (arithmetic coding) and to generate untransmitted scales is technically neat and principled.\n- Good progressive design. Encoding into hierarchical residual scales and stopping transmission at k aligns naturally with bitrate adaptation; the decoder’s next-scale VAR completes missing scales, yielding progressive reconstruction. The pipeline (lossless arithmetic decode → VAR generation → image decoder) is clearly illustrated.", "weaknesses": "- Complexity accounting. The claim of 2–6× faster decompression than diffusion is compelling, but a fuller wall-clock/compute-memory breakdown across image sizes and k values would strengthen the efficiency story. \n- The impact analysis of missing text on reconstruction is lacking. The paper mentions using BLIP2 to extract image captions to assist reconstruction, but it does not analyze how the absence of text affects the final results, which is an incomplete approach.\n- A more detailed baseline comparison is needed. Recently, numerous diffusion-based image compression methods have emerged, such as StableCodec[1] and ResULIC[2]. These methods have significantly improved decoding speed and performance, particularly StableCodec, which can complete image generation in a single step. This demonstrates that multi-step denoising is no longer a common limitation of diffusion-based methods. The authors need to conduct a more detailed performance and complexity comparison with these methods to highlight the significance of VAR-based approaches.\n\n[1] Zhang, Tianyu, et al. \"StableCodec: Taming One-Step Diffusion for Extreme Image Compression.\" arXiv preprint arXiv:2506.21977 (2025).\n\n[2] A. Ke, X. Zhang, T. Chen, M. Lu, C. Zhou, J. Gu, and Z. Ma, “Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion,” in Proc. Int. Conf. Mach. Learn. (ICML), 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": "YOywPK1IJi", "reviewer": "Reviewer_1Hec", "summary": "This paper proposes an ultra-low bitrate image compression method based on the VAR model. Specifically, it introduces group-masked bitwise multi-scale residual quantization (GM-BMSRQ) and lossless re-encoding (LRE) techniques to improve compression ratio. The scale random dropout (SRD) strategy enhances the representation capability of earlier scales. The proposed ARPC method achieves superior perceptual and statistical fidelity on benchmark datasets.", "questions": "See the part of weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "This paper proposes an innovative extreme image compression method based on VAR. The manuscript is well-organized and original.", "weaknesses": "1.The Kodak dataset is a commonly used benchmark in image compression. However, the authors do not provide quantitative or qualitative comparisons of it. Please add these comparisons.\n2.The paper does not compare the ARPC method with representative methods, such as token-based and one-step diffusion methods. The former includes the GLC [1] and DLF [2] methods, while the latter includes the RDEIC [3], OSCAR [4], and StableCodec [5] methods. Please add these comparisons for a comprehensive evaluation.\n[1]Jia Z, Li J, Li B, et al. Generative latent coding for ultra-low bitrate image compression[C]. CVPR 2024.\n[2]Xue N, Jia Z, Li J, et al. DLF: Extreme Image Compression with Dual-generative Latent Fusion[J]. ICCV 2025.\n[3]Li Z, Zhou Y, Wei H, et al. RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion[J]. TCSVT2025.\n[4]Guo J, Ji Y, Chen Z, et al. OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates[J]. NeurIPS2025.\n[5]Zhang T, Luo X, Li L, et al. StableCodec: Taming One-Step Diffusion for Extreme Image Compression[J]. ICCV2025.\n3.The related work lacks one-step, diffusion-based extreme image compression methods.\n4.What is the bitwise multi-scale residual quantizer? Please explain how it works in detail.\n5.Please explain how the scale random dropout strategy work and introduce it in detail.\n6.Please explain the symbol d() in line 221 of the manuscript. Is it the MSE function?\n7.For qualitative comparisons, the authors should select results for which the ARPC has the smallest BPP values compared to other competing methods. Please modify it (Fig. 4 and 5.).\n8. Would you release the source code and pretrained models? We hope you can also release them to help us understand the ARPC.", "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": "Ls94primQv", "reviewer": "Reviewer_t8Sb", "summary": "This paper proposes a novel framework for ultra-low bitrate image compression , which utilizes a Visual AutoRegressive model (VAR) to encode images into multi-scale discrete tokens. Decompression is achieved by autoregressively generating (predicting) the unreceived scales.", "questions": "see weakness. My main point of interest is whether VAR itself is already sufficiently effective when used as a codec, and what the performance improvement of this paper's method is compared to a baseline VAR.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "This paper achieves variable bitrate by combining VAR with lossless compression across different scales, effectively integrating image compression with existing pre-trained models.\n\nThe paper is well-organized and easy to understand.", "weaknesses": "1. The encoding time increases with the bitrate. Additionally, the encoding process requires a caption model to generate captions, making the encoder relatively \"heavy.\" Could the authors provide the parameter counts for the models used at both the encoder and decoder ends?\n\n2. The ablation study in Figure 7 shows that while each proposed module contributes some improvement, the individual gains are relatively modest. Could it be that directly using VAR for encoding and decoding already offers a performance advantage over other generative compression methods?\n\n3. Further experiments are needed to investigate the impact of different specific mask choices for the Group-Masked (GM-BMSRQ) method.", "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)