Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation
Authors: Anqi Li
Feng Li
Yuxi Liu
Runmin Cong
Yao Zhao
Huihui Bai
Venue: ICLR 2025 Poster (2025)
Keywords: image compression
vqgan
generative compression model
multi-grained representation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A unified image compression model capable of fine-grained variable bitrate adaptation with VQGAN.

Abstract: Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaptation to diverse compression necessities and scenarios. To overcome this challenge, this paper proposes a $\textbf{Control}$lable $\textbf{G}$enerative $\textbf{I}$mage $\textbf{C}$ompression framework, $\textbf{Control-GIC}$, the first capable of fine-grained bitrate adaptation across a broad spectrum while ensuring high-fidelity and generality compression. We base Control-GIC on a VQGAN framework representing an image as a sequence of variable-length codes ($\textit{i.e.}$ VQ-indices), which can be losslessly compressed and exhibits a direct positive correlation with bitrates. Drawing inspiration from the classical coding principle, we correlate the information density of local image patches with their granular representations. Hence, we can flexibly determine a proper allocation of granularity for the patches to achieve dynamic adjustment for VQ-indices, resulting in desirable compression rates. We further develop a probabilistic conditional decoder capable of retrieving historic encoded multi-granularity representations according to transmitted codes, and then reconstruct hierarchical granular features in the formalization of conditional probability, enabling more informative aggregation to improve reconstruction realism. Our experiments show that Control-GIC allows highly flexible and controllable bitrate adaptation where the results demonstrate its superior performance over recent state-of-the-art methods.

Published as a conference paper at ICLR 2025
CONTROLLABLE GENERATIVE IMAGE COMPRESSION WITH DYNAMIC GRANULARITY ADAPTATION
ONCE-FOR-ALL:
Anqi Li 1.2 Feng Li 3.* Yuxi Liu 1.2 Rummin Cong Yao Zhao 1.2 Huihui Bai 1.2,*
Institute of Information Science, Beijing Jiaotong University
2 Beijing Key Laboratory of Advanced Information Science and Network Technology
3 School of Computer Science and Engineering. Hefei University of Technology
School of Control Science and Engincering, Shandong University {lianqi, yuxil lill yzhao, hhbai abbjuueedu cn fengli@hfut, edu, en, zmconge sdu. edu, on
ABSTRACT
Although recent generative image compression methods have demonstrated impressive potential in optimizing the dindistottionnperepttoon trade-off, they still face the critical challenge of flexible rate adaptation to diverse compression necessities and scenarios. To overcome this challenge, this paper proposes a Controllable Generative Image Compression framework, termed Control-GIC, the first capable of fine-grained bitrate adaptation across a broad spectrum while ensuring high-fidelity and generality compression. Control-GICC is grounded in VQGAN framework that encodes an image as a sequence of variable-length codes (i.e. VQ-indices), which be losslessly compressed and exhibits direct positive correlation with bitrates. Drawing inspiration from the classical coding principle, we correlate the information density of local image patches with their granular representations. Hence, we can flexibly determine proper allocation of granularity for the patches achieve dynamic adjustment for VQ-indices, resulting desirable compression rates. We further develop a probabilistic conditional decoder capable of retrieving historic encoded multi-granularrty representations according to transmitted codes, and then reconstruct hierarchical granular features in the formalization conditional probability, enabling more informative aggregation to improve reconstruction realism. Our experiments show that Control-Gle allows highly flexible and controllable bitrate adaptation where the results demonstrate its superior performance recent state-of-the-art methods.
INTRODUCTION
Lossy image compression complies with the rate-distortion criterion in Shannon's theorem (Shannon
al. 1959), which aims to pursue minimal storage of images without quality sacrifice. Traditional
standardized codecs (Wallace 1990, TTubman & Marcellin] 2001 Bellard) adhere to typical hand-
crafted trransforminng--uaniiatorrr onttoop coding" rule, showing substantial performance on generic
images. Learnable compression algorithms (Ballé et al. 2017 2018: Minnen et al. 2018 Minnen &
Singh 2020) follow a similar pipeline that parameterizes it as convolutional neural networks (CNNs)
operating latent variables with end-to and-to-end R-D optimization. Recent works (Santurkar et al. 2018
T'scannenn et al. 2018 Agustsson et al. 2019 Mentzer et al. 2020) leverage generative adversarial
networks (GANs) (Goodfellow al. 2014) to deal with the compression task, known as generative
image compression, which minimizes the distribution divergence between original and reconstructed images, producing perfect perceptual quality. However, these methods train the models separately for specific R-D points with Lagrange multiplier (A)-weighted R-D loss, each corresponding to an individual X. In this way, multiple fixed-rate models are necessitated vary bitrates, leading to
* Corresponding Authors

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Figure 1: Illustration of the key motivation behind our approach. @ Generative methods train separate models for each distinct compression ratio, which achieves promising perceptual quality but overlooks the flexibility. Variable-rate methods modify the compression model by introducing truncated quantization parameters, which only support a limited range of bitrates and cannot balance the perceptual quality and compression efficiency. @ Our proposed Control-GIC enables the generation of controllable-length bitstream following different granularity decisions image patches, which achieves an excellent trade-off among flexibility, perceptual quality, and compression efficiency.
dramatic computational costs and inefficient deployment to cater to diverse bitrates and devices. Some CNN-based models propose to learn scalable bitstreams (Johnston et al. 2018 Toderici et al. [017
Bai et al. 2021; Mei et al. 2022 Zhang et al. 2024a Jeon et al.] (023b) or truncated quantization
parameters to control the bitrates (Toderici al. 2016 Choi al.) 2019 Yang et al. [021 Cui
ct al. 202I). On the one hand, these models typically support a limited range of bitrates with substantial variance distribution, thus constraining their adaptability finer bitrate adjustments. On the other hand, they mostly quantify the distortion using mean square error (MSE), which is inconsistent with human perception and often yields implausible reconstruction, particularly at low bitrates (Blau & Michaeli] 2019; Mentzer et al. 2020). Several methods introduce scalable (Iwai al.) 2024) or variable-rate (Guo al., 2023) designs into generative models. While achieving remarkable improvements in perceptual quality, they are still constrained by finite compression rates (see Figure
In light of the preceding discussion, this work proposes innovative generative image compression paradigm, dubbed Control-GIC, which accommodates highly flexible and fine-grained controls on a broad range of bitrates and perceptually realistic reconstruction with solely one set of optimized weights. Motivated by that VQ-based models (Esser et al. 2021] Zheng et al. 2022) enable to encode images into discrete codes representing the local visual patterns, Control-GIC hybridizes the classical coding principle in the architecture with VQGAN to relax the typical R-D optimization and provide controllable unified generative model. Specifically, Control-GIC first characterizes the inherent information density and context complexity of local image patches as the information entropy. We devise the granularity-informed encoder that determines the gramularity of these patches based on their entropy values. These are further represented by sequential variable-length codes (i.e., VQ-indices) based on learned codebook prior. One can flexibly control the statistics of the granularities to adjust the VQ-indices of patches dynamically adapting to diverse desirable bitrates. As correlated the regional information of images, the VQ-indices are spatially variant to adapt to the local contents. We then develop no- parameetric statistical entropy coding module, which captures the code distribution in the codebook prior across a large-scale natural dataset to approximate generalized probability distribution. This enables lossless and more compact encoding of VQ-indices during inference, improving the compression efficiency. On top of entropy coding, probabilistic conditional decoder is further developed, which formalizes the reconstruction of granular features in a conditional probability manner with historic encoded multi muutt--rraullaiig representations given entropy- decoded indices. Our comprehensive experimental results demonstrate the outstanding
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adaptation capability of Control-GIC, which achieves superior performance from perceptual quality, flexibilityy, and compression efficiency over three types of recent state-of-the-an methods including generative, progressive, and variable-rate compression methods using only a single unified model.
The main contributions of this work are three-fold:
We propose Control-GIC, a unified generative compression model capable of variable bitrate adaptation across broad spectrum while preserving sigh-perepptul fidelity reconstruction. To our knowledge, this is the first that allows highly flexible and controllable bitrate adaptation.
We propose a granularity-infomm encoder that represents the image patches of sequential spatially variant VQ-indices to support precise variable rate control and adaptation. Besides, non-parametric statistical entropy coding is devised to encode the VQ-indices losslessly.
We design probabilistic conditional decoder, which aggregates historic encoded multi- granularity representations reconstruct hierarchical granular features in a conditional probability manner, achieving realism improvements.
2 RELATED WORK
Neural Image Compression Transformation, quantization, and entropy coding are three key components in neural image compression (NIC). Since Ballé et al. (Ballé et al. 2017) propose the pioneering learnable NIC method using convolutional neural network (CNN), later methods make improvements in transformation to learn a more compact and exact representation with efficient architecture designs (Cheng et al. 2020: Xie al. 202T Zou et al., 2022). Some researchers are dedicated to improving the entropy coding by designing hyperprior and context models (Ballé et al. 2018 Lee et al. 2018 Qian et al., 2022) with entropy model, which can capture more precise spatial dependencies in the latent, helping probability distribution estimation. In recent years, the integration of generative models like GANs (Goodfellow al. 22144; Wang et al., 2018 Karras et al., 2019) and Diffusion Model (DM) (Ho et al., 2020] Zhang et al. [024bb] Wu et al. 2024)
into NIC has shown promising results. For instance, Agustsson et al. (Agustsson al. (019) uses
GAN loss along with R-D loss to achieve end-to-end full-resolution image compression while giving dramatic bitrate savings. Mentzer el al. (Mentzer et al. 2020) incorporates GAN with compression
architecture systematically and generates robust perceptual evaluation. Yang et al. (Yang & Mandi 2024) propose an end-to-end DM-based compression framework and reconstruct images through the reverse diffusion process conditioned with context information, outperforming some GAN- based methods. VQGAN (Esser et al.] 2021)-based techniques (Mao et al. 2023 Xue et al. 2024 Jia et al. 2024) have demonstrated strong codebook priors for discrete visual feature representation in image synthesis, offering new insights for compression. Mao et al. (Mao et al. 2023) introduce VQ-indices compression for simple yet efficient compression, markedly improving the compression ratio. Building on these findings, we aim to harness the potential of VQ and customize VQGAN designs for controllable generative compression across various bitrates with unified model.
Rate-Adaptation NIC The aforrmennioned methods often face the challenge of deployment in resource-limited devices they are trained as separate models for specific bitrates, which increases the complexity overhead to support multiple bitrates. Current research solving such rate adaptation
problem can be roughly divided into two categories: variable-rate compression (Chen & Ma 2020
Lu et al. 2021 Cui al. 2021 Guo-Hua et al.) (023) and progressive compression (Toerici et al.
2017, Mei et al.) 2022] Lee el al. 2022a Jeon et al. 20235 Zhang et al. 2024a). Variable-rate
methods, such as those by Theis er al. (Toderici et al. 2017) and Choi et al. (Choi al. 2019), adjust
scalar parameters or use conditional convolutions to adapt to different quality levels. Others, like
Cai et al. (Cai et al. 2019) and Yang et al. (Yang et al. 202T), employ multi-scale representations or slimmable networks for content-adaptive rate allocation. Cui et al. (Cui et al. (021) and Lee er al. (Lee et al. 2022b) introduce gain units and selective compression, respectively, to further refine bitrate control. Progressive compression(T) Toderiic al. 2017 Johnston et al. 2018) develops scalable bitstreams for bitrate flexibility. Zhang et al. (Zhang et al. (0244) propose to explore the receptive field with uncertainty guidance for both quality and bitrate scalable compression. Lee et al. (Lee) jet al. 2022a) propose to encode the latent representations into a compressed bitstream trip-plane to support fine-granular progressive compression. Jeon al. (Jeon et al. 2023b) further improve it with
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Granularity-Inforne Encoder
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Figure 2: The overall framework of our Control-GIC. In the figure, all components cooperate for efficient compression with end-to-end training, and dashed lines represent the unparameterized entropy coding module. The symbols in the diagram are defined as: m: the binary mask; (-)4; average pooling operation; @r element-win wise multiplication; f()): the frequency distribution.
context-based trit-plane coding, increasing the R-D performance. In contrast to these methods, this work leverages VQGAN, integrated with classical coding principle (Huffman, [952). design controllable generative compression framework. Our method allows highly flexible and controllable bitrate adaptation while generating plausible results with solely one optimized weight set.
METHOD
Our goal is learn a unified generative compression model capable of compressing an image for flexible and continuous bitrates while ensuring high perceptual fidelity. To this end, we propose Control-GIC, where the overview architecture illustrated Figure Control-GIC contains three components: 1) granularity -informmed encoder to encode the image into variable-length codes; 2) statistical entropy coding module for bitrate reduction; and 3) probabilistic conditional decoder to reconstruct perceptually plausible results.
3.1 GRANULLRIIT-INFFRNEER ENCODER
Given input image E #xxx3 as illustrated in Figure Control-GIC considers the entropy of local patches as the basis the information density distribution (See Appendix AA. for proof of correlation) of the image, and divides it into multiple non-overlapped patches sorted by their entropy value from low to high. Then, the granularity-iffrmeed encoder distills these patches into hierarchical features of three granularities: fine-grained
E R # x - xd ceedium-grainee 22 R $ xd and coarse-grainct 23 IR M W. xd Supposing there is target bitrate corresponding the ratios (r1, r2, r3) E [0, 1] of three granularities, each ratio specifies the proportion of elements with the lowest entropy to be retained from each 2i 1, 2, 3) and yield binary masks m, {0, 1}. Here, m; aligns with the spatial dimensions of 2; to localize the retained elements in 2;. This process is executed from coarse to fine, progressively and finely assigning the multi multi-grained representations.
Subsequently, the determined features in each { are matched to the codes the codebook C and quantized, producing é; and of discrete VQ-indices Ind, that represent the closest matches
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in C' based on Euclidean distance. This quantization step q(-) is mathematically formalized
2 = (22) arg min || -cll Ind; = k.
exEC
(I)
where o denotes the k-th code in the codebook. With three quantized counterparts { can construct the hybrid representation 2 to match the spatial scale of the finest granularity as follows:
(&1 @ mi) + ($2 3 m2)ra + (Eg @ ma)t..
where (.) 14 and (+) 12 signify upsampling operations that amplify the spatial dimensions by factors of 4 and 2, respectively. We use nearest neighbor interpolation as it employs replicates values of feature points along both the width and height, ensuring the preservation of the original local structure integrity for each feature point. @ is element- wiiee multiplication along the spatial dimension.
3.2 PROBABILISTIC CONDITIONAL DECODER
Based on the VQGAN pattern, our decoder receives the indices of 2; and correspond masks m, from the encoder, to reconstruct the features of the encoder by searching for the codebook. However, directly feeding& into the decoder layers is sub-optimal as many non-linear transformations in the decoder can cause information loss. While the upsampled components &2 and êg maintain their local structure through direct value duplication (Eq. 2). their global structure is inevitably changed.
To address this problem, introduce probabilistic conditional decoder, which formalizes the reconstruction through the conditional probability. Specifically, two downsampling operations (-) L4 and (.) J2 are first employed downscale back the {4 O m losslessly. We provide (3) as the initial decoder input 811 which contains the same &3 @ my as the encoder output to ensure the accuracy of the imput. & @ my and & @ m2 are provided as conditions to y/2 and ys, respectively. These conditions serve as additional guidance for the reconstruction process:
y/2 ~ p(U2 31, (3)42 @ m2), 1/3 p(y3 y2, 1/; $ @ mi).
Consequently, the decoder D begins with the (-) 4a operation on 2 to produce y1 that is fed into the first decoder layer Dj to generate y/2 Then, 3/2 @ m2 are deliberately replaced with the medium-
grained representation (3) m2 (equal @ m2). After that, Da condition with the 2 my and replace the unexact ? (1 -m1), ensuring the precision of features in deep layers:
1/2 = (1n) @ (1 - m2) + (8)42 @ m2, 1/3 (2(22) @ (1 m1) + m1-
(4)
This systematic replacement of representations at varying granularities with increasingly precise conditions progressively refines the latent space representation, which facilitates the decoder to diminish information loss and substantially elevates the accuracy of the reconstructed images. It should be noted that, compared to conventional compression methods (Ballé et al. 2017; Mentzer et al.) 2020), our method effectively avoids the information loss between the encoder and decoder features except the quantization, thereby achieving nearly minimal-los reconstruction.
3.3 STATISTICAL ENTROPY CODING STRATEGY
As analyzed in Sec. 3.2 and Eq. (4). the decoding process requires bitstreams comprising the features of three granularities and their corresponding masks. Since &; can be retrieved by searching from the codebook based on the indices Ind,, what we need actually are the (Tnd, } and the masks km1,m2) These elements can be encoded via lossless encoding algorithms as they are integers. Specifically, the mask consists of 0 and I which can be encoded directly into a binary stream. As for indices, one promising solution is the prefix Huffman coding algorithm (Huffman 1952). which can generate the shortest average code length for given symbol set. However, this ad vdnatae often comes from the frequency statistics of each element. In this work, directly applying this algorithm is suboptimal as it needs to count the indices frequency used in each independent image and acquire codebook that maps the indices the binary codes. This can introduce significant bit overhead
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when reconstructing the image based on the Huffman codebook during the decoding. A simplified approach is to treat all indices equally, i.e. assuming the frequency in the codebook is uniform. Nevertheless, the indices, which point to codebook entries, exhibit uneven frequency distribution, with a minority of codes used for quantization (Zhang et al. 2023).
To address this problem, we introduce statistical entropy coding strategy that captures the frequency distribution of indices usage across a natural dataset during training. Each index is initialized with frequency count of 0, and the frequency is updated each time the index is matched during vector quantization. Here, we utilize the frequency statistics at the endpoint of the training process to construct a frequency table tailored for Huffman coding as it is more stable and close the overall data distribution. We denote the bitrate after coding as R(-), then the total bitrate of the entire image can be formulated as:
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Σ R(Ind,) + R(m1) + R(m2).
(5)
i=1
Note that model does not optimize network parameters for specific bitrate. During inference, we control the bitrate using different multi gualiiirannlarity allocation ratios. For example, we can set a group of hyperparameters {ri, 72, rs} to represent the allocation ratios of the masks {m1, m2g my} at three granularities. Since the bitrate depends solely on the granularity representations of local patches, we can flexibly determine the granularities based the entropy values of local patches, achieving dynamic adaptation in a target of the quality-bitran adaptive manner in a unified model without any post-training.
3.4 Loss FUNCTION
In our experiments, the overall loss function C for training Control-GIC contains the loss associated with the VQVAE architecture and GAN component in VQGAN (Esser et al.] 202T). The optimization
objective of LvQuAr E, G, C') is two-fold: 1) minimizing the distortion between the original inputs and their reconstructions é; and 2) constrain the divergence between the continuous representations Fax)) and their quantized versions 2, as follows:
LVQV AE (E, d(z, 2) + d(2, 2)
[das dr) (x, ||>][=] 2 + |sg[ =112-
(6)
where we use MSE (dar) and LPIPS (dp) to measure the reconstruction distortion. sg[-] denotes the stop-gradient operation widely utilized in VQ-based models to overcome the non-differentiable nature of quantization. sg[:] enables the quantized representations to propagate gradients directly for optimizing the codebook C' and allows the continuous representations 2 to receive gradients for the refinement of the encoder E. Our Control-GIe deviates from the conventional R-D optimization paradigm with no-parametric indices compression. This enables the model to adapt flexibly to various data types and quality levels, transcending the fixed R-D trade-off in most generative methods.
For GAN loss, we use the patch-based discriminator in (Isola et al. 2017) to differentiate between original and compressed images:
LGAN ((E, C,C), Eewpla) (log D(x) + log(1 -og(1-
(7)
Therefore, the total loss L is formulated by
= LVQVAE + ALGAN.
(8)
We use the hyperparameter À to control the trade-off between VQVAE and GAN losses.
4 EXPERIMENT
4.1 EXPERIMENTAL SETUP
Our method is based MoVQ (Zheng et al. 2022 which improves the VQGAN model by adding spatial variants to representations within the decoder, avoiding the repeat artifacts in neighboring patches. We leverage the pre- pre-trained codebook in MoVQ and carefully redesign the architecture.
Training& & Inference. We randomly select 300K images from the OpenImages (Krasin et al. 2017)
dataset training set, where the images are randomly cropped uniform 256 x 256 resolution.

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Figure 3: Comparisons with existing methods the Kodak. The lines with forks represent generative compression methods and the lines with rhombus represent variable-rate and progressive methods.
Within our model, we take three representation granularities: x 4, 8 x 8, and 16 x 16. The codebook C' e Rexd comprises & = 1024 code vectors, each with dimension of d = 4. We train the model for 0.6M iterations with the learning rate of 5 x 10-5 on NVIDIA RTX 3090 GPUs. Throughout the training, we maintain the ratio setting of (50%, 40%, 10%) for the fine, medium, and coarse granularity, respectively. During inference, our Control-GIC can process images of any resolution and allow fine bitrate adjustment using a unified model.
Evaluation. We evaluate our method on the Kodak (Kodak 1993). DIV2K (Agustsson & Timofte 2017). and CLIC2020 (Toderici et al. 2020) datasets. Kodak contains 24 high-quality images at 768 x 512 resolution. DIV2K and CLIC2020 contain 100 and 428 high-resolution images, respectively, with resolutions extending up to 2K. We carry out multi-dimensional evaluation and utilize a comprehensive set of evaluation metrics including perceptual metrics: LPIPS (Zangg et al. 2018). DISTS (Ding et al.] 2020), distortion metric: PSNR, generative metrics: FID (Heusel et al.
2017).
KID (Binkkoskki et al. all]|||0111 2018)) as well as the no-reference measurement: NIQE (Mittal et al. to thoroughly assess the performance of our method. More details are in the Appendix A.
2012
4.2
PERFORMANCE COMPARISON
We compare the proposed Control- with four types of state-of-the-ar art (SOTA) image compression methods: (1) Generative compression methods. HiFiC (Mentzer et al.) 2020) and MRIC (Agustsson
al., 2023) leverages conditional GAN purse the rate-distortion perrepptionnnn trade-off. MS-
ILLM (Muckley et al., (023) improves statistical fidelity using local adversarial discriminators.
CDC YYagg & Mandi 2024) is a representative diffusion- lossy image compression approach;
(2) Variable-rate and progressive compression methods. SCR (Lee et al. 2022b) proposes
3D important map adjusted by quality level to select the representation elements for variable rates.
CTC (Jeon al. 2023a) progressively decodes the bit stream truncated at any point to regulate the
bitrate; (3) Classical NIC method. M&S (Hyperprior) (Ballé et al. (2088) trains separate models for different bitrates; (4) Traditional codecs BPG (Bellard) and VVC (VTM10.0) (Bross et al. (021).
In Figure 3 we first provide the R-D curves for all methods, evaluated using four metries: LPIPS, DISTS, PSNR, and NIQE, on the Kodak dataset. Our Control-GIC surpasses most methods across 4 distinct metrics and achieves comparable performance with the most state-of-the-art methods MS-ILLM and MRIC in LPIPS and NIQE, even though they are trained separately for specific R-D points. Compared to SCR and CTC, Control-Gi achieves finer granularity flexibility for bitrate control while preserving obvious preferable perceptual quality. Besides, since conventional CNN-based NIC methods, e.g. M&S (Hyperprior) and SCR, optimize for the R-D trade-off using pixel-wise MSE loss, it can be observed that they produce relatively higher PSNR than generative methods.
R-D Performance.
Then, we conduct comparisons on the DIV2K (Agustsson & Timofte, 2017) dataset, As illustrated in Figure 4 in addition to the four metries in Figure 3 we include FID and KID to provide more comprehensive evaluation. The results demonstrate that our Control-GIC maintains promising competitiveness against existing methods in almost all the metrics across wide spectrum of bitrates. We also investigate the effectiveness of our method on the CLIC2020 dataset, where the results are provided in Figure 10 (see Appendix A.5
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Figure 4: Compression performance on the DIV2K with compared methods. The lines with forks represent GIC methods, and the lines with rhombus represent variable-rate and progressive methods.
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Figure 5: Comparison of model efficiency for all methods based on four terms: encoding time (s), decoding time (s), BD>rate saving (%), and training steps (M) the Kodak dataset. The diamond icons represent variable-rate and progressive methods, and the triangles represent GIC methods, with the number of iterations required for model training and the encoding/decoding time multiplier compared to our method labeled in parentheses after each method name.
Model Efficiency. To demonstrate the efficiency of the proposed method, in Figure 5 we analyze existing state-of-the-ard methods and our Control-GIC four terms: encoding time (sec.), decoding time (sec.), BD-rate saving
(%) and training steps (M). For fair comparisons, all the methods are evaluated using their original public code and pre-trained models on the same NVIDIA 3090 GPU, We calculate the average encoding and decoding time on the Kodak dataset. The BD-rate saving is evaluated by quantifying the Bpp-LPIPS results, using VVC as the anchor. As shown in Figure MRIC, MS-ILLM, and Control-GIC obtain very close BD-rate saving and are superior to others. For the inference speed, M&S, CTC, and HiFiC suffer from critical time costs both encoding and decoding. CDC applies a lightweight diffusion variational autoencoder, which benefits the encoding process but struggles with more decoding time due to its iterative reverse process. Our method achieves the fastest encoding/decoding time, which is 7x faster than MS-ILLM and 4x faster than MRIC encoding, and 3x faster than MS-ILLM and 1.5x faster than MRIC decoding. Moreover, the single-poin training methods (e.g., M&S, HiFiC, CDC, MRIC, MS-II.LM) require independent
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Original Bpp LPIPS
BPG 0.288 0.059
VVC 0.280 / 0.042
M&S 0.304 0.055
SCR 0.266 0.0899
СTC 0.285 0.051
HiFIC 0.288 0.038
CDC 0.275 0.040
MS-ILLM 0.274 / 0.045
Control-GIC 0.275 / 0.038
Figure 6: Reconstructed Kodak images by existing state-of-the-ar methods and our Control-GIC
Original
Rpp0 0
Ropa4477 LPIPS
LPI25-600222
40=40%
32=35%
T2=33%
T2=32.9%
32=32.88%
Target hpp
Figure The fine- onainrd control over the bitrate by Control-GIC. As diminishes, the proportion of fine-gra tne-grained features correspondingly increases, leading to higher total count of codes, and consequent increase in the bitrate (measured in bpp) and reconstruction quality (the lower left corner visualizes the difference maps between the original image and reconstructed ones).
training of n models for R-D points. The proposed model requires only a single training session that enables compression across various bitrates, with the total training steps being substantially reduced to 0.6 million steps. Although SCR and CTC support multiple compression rates single model, they still involve many more training steps, especially SCR which is more than 15x that of ours. By comparison, our Control-GIC can achieve promising balance among training costs, inference speed, and BD-rate saving.
Qualitative Comparison. see, VVC, M&S, SCR, and CTC produce the results with noticeable blurs and
Figure we visualize the reconstructed images by all compared
methods. As artifacts. While HiFiC, CDC, and MS-ILLM can yield clearer details, their images contain some misleading textures and artifacts present in the original ones. By comparison, our method excels at preserving texture integrity and image sharpness. More visual results are in Appendix A.7
4.3 ABLATION STUDY
Fine-grained Control of Bitrate. As analyzed Sec. Sec.3.1 the proposed Control-GIC can flexibly control the bitrates through the granularity ratios (r1,72, r3). To validate the effects, in Figure 7 we visualize the reconstructed samples of our method, where VVC is adopted as a reference. We fix the ratio of the coarse- grained features r3 50% and adjust the ratio of medium-graine features r2, thus one can directly obtain the ratio r T2 Tg for the fine-grained features. can be observed that, ra diminishes, increases, resulting in higher total count of codes and, consequently,

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0.16
wio, meed, inn
wemedd
0.15
0.13
w/ med
60
wio, med, fén
w/ med
0.12
w/ fin Ours
w/ firs
50
w/
0.10
DISTSI
40
Ours
0.08
30
0.06
0.07
20
0.04
0.08
10
0.00
0.6
0.44
0.2
0.3
0.4
0.5
0.6
Hpp
Bpp
Figure 8:
The ablation experiments on multi coalti-graindd conditions for the probabilistic conditional decoder. All experiments are performed on the DIV2K dataset.
Table I:
Bit comparison between our statistical entropy coding and Huffman coding with uniform frequency for each index on the Kodak dataset.
Granularity Ratio
Huffman w. Uniform Frequency
Statistical Entropy Coding (Ours)
Bit Saving (%)
100%,
0.625
0.594
5.0%
60%, 30%, 10% 20%, 50%. 30%
0.445
0.432
2.9%
0.235
0.234
0.4%
higher bitrate (measured in bpp), where the perceptual quality gradually improves. Furthermore, the continuous controllable compression in Figure inndicates that method enables precise regulation of the bpp. allowing for minute adjustments within range as narrow as 0.001 (as exemplified by the change from 0.4171 in the third-to-last column to 0.4172 in the second-to-last column).
Impacts of Probabilistic Conditional Decoder. In this work, we propose the probabilistic conditional decoder which formalizes the reconstruction through the conditional probability. Here, we investigate the contributions of the conditions: the poodium-grainne (2)12 @ my (denoted as med) and fine-gra aine-grained 8 @ mi (denoted as fin) to the decoder in Eq. 44) to reveal the impacts of the proposed probabilistic conditional decoder, where their R-D performance is illustrated in Figure X We can observe that the model without the med and fin produces the worst results in three metrics. Besides, med and fin present significant improvement in model performance, and conditioning upon both presents the best results, especially on DISTS. Moreover, the results also validate that adding the fine-grained fín to the model brings more benefits than adding med. This is because the fine-gras greingriiedd fin can correct the features in deeper layers of the decoder, thereby improving the accuracy after multiple non-linear transformations within the decoder.
Efficiency of Statistical Entropy Coding. In Table we compare the proposed statistical entropy coding strategy Huffman coding with uniform frequency for each index. The superiority of our statistical entropy coding strategy becomes increasingly evident as more codes are employed (i.e., higher bpp) in the coding process. Notably, at the highest bpp, our approach achieves nearly 5,0% bit saving than Huffman coding with uniform frequency distribution thereby verifying its efficiency.
5 CONCLUSION
In this work, we propose Control-GIC, an innovative controllable generative image compression framework that addresses the critical challenge of flexible rate adaptation By leveraging a VQGAN foundation and correlating local image information density with granular representations, Control- GIC achieves fine- ainengraeeed bitrate control across a wide range while maintaining high-fidelity compression performance. We propose a granularity-informed encoder that represents the image patches of sequential spatially variant VQ-indices to support precise variable rate control and adaptation. Then, non-parametric statistical entropy coding is devised to encode the VQ-indices losslessly. In addition, we develop a probabilistic conditional decoder, which aggregates historic encoded multi-granule granularity representations to reconstruct hierarchical granular features in conditional probability manner, achieving realism improvements. Experiments validate the superior effectiveness, compression efficiency, and flexibility of our method.
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ACKNOWLEDGMENTS
This work was supported in part by National Natural Science Foundation of China (No. 62331003, 62120106009, 62302141, 62471278), Beijing Natural Science Foundation (L223022). the Fundamental Research Funds for the Central Universities (I22024HTB02255) and the Taishan Scholar Project of Shandong Province under Grant tsqn202306079.
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A APPENDIX
A.1 EVALUATION METRICS
We adopt comprehensive set of evaluation metrics thoroughly assess the performance of our image compression and reconstruction techniques. Our selection encompasses perceptual metrics, distortion metrics, generative metrics, and a no-reference metric, ensuring a multifaceted evaluation. The perceptual metries include LPIPS (Learned Perceptual Image Patch Similarity) (Zhang et al. 2018), which measures the perceptual difference between images, and DISTS (Deep Image Structure and Texture Similarity) (Ding et al. 2020). which evaluates the structural dissimilarity. These metries are crucial for understanding how closely compressed and reconstructed images resemble their original counterparts in terms of human visual perception. We also include the widely recognized distortion metric PSNR (Peak Signal-to-Noise Ratio), which quantifies the pixel-level differences between the original and reconstructed images. PSNR is standard in the field, providing straightforward measure of image fidelity. For generative metrics, we employ FID (Fréchet Inception Distance) (Heusel al. 2017) and KID (Kernel Inception Distance) (Binkowski et al., 2018) to offer statistical assessments of the similarity between the distributions of original images and those of reconstructed images, which is particularly valuable in the context of generative models. NIQE (Natural Image Quality Evaluator) (Mittal et al.) (012) stands out as no-reference metric, capable of evaluating image quality without requiring an original reference image. This feature renders NIQE exceptionally beneficial in applications such super-resolution where an original high-resolution image may not be available. For comparison with other methods on FID and KID, we divide the DIV2K dataset into 6,573 patches, and the CLIC2020 dataset into 28,650 patches, each of size 256.
A.2 CORRELATION BETWERN ENTROPY AND INFORMATION DENSITY
Inspired by Celik (Celik 2014), we measure the information density of image regions based on non-parametric spatial entropy algorithm. Unlike feature-level entropy models based on neural networks (Ballé et al. 2017 2018). our Control-GIC does not rely on neural entropy model and is not optimized for entropy during training. Instead, we adopt non- non-parametric algorithm to reduce computational overhead while maintaining robust performance in granularity selection. The mathematical formulation of this process is described below.
Consider a pixel x E Ω with value Ps within the interval [-1,1], where Ω denotes patch of the image. We define the bin of each pixel value as i = -1 + 24 the number of bins n is set to 32. The Gaussian distance fa,: between the pixel x and each bin is computed as:
--I where = 0, 1, 1, n 1, and
fz.z= = exp (- (p= (-111-00 1)²
exp
99)
20
where a is the standard deviation. Thus, fz.5 exhibits an unnormalized, truncated discrete Gaussian distribution over the bins i. This implies that fz,; models the probability of ps being associated with bin i, reflecting the likelihood of pixel value distribution across bins.
Next, compute the average of fz,i across all pixels within the patch Ω to obtain the probability distribution fo,; for this region. This is normalized to yield fa.:- The average operation is denoted as mean, and the mathematical expression is as follows:
weft
SaL
Ja
where fa,: = mean Ja,a-
(10)
E,
#ef
Finally, the spatial entropy ( of the patch Ω is computed as:
(( = ((0))=->)
E fa, log Ja.i.
(11)
Control-GIC considers the spatial entropy of local patches to model the information density distribution of the image. The image is divided into multiple non-overlapping patches, which are sorted by their entropy values from low to high, allowing the assignment of multi-grained features.
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A.3 DETAILS FOR GRANULARITY DIVISION
As illustrated in Figure given an input image z partitioned into a series of patches, the encoder E first computes the entropy values for all patches and sorts them in ascending order. We categorize these patches into three distinct granularity levels: fine, medium, and coarse. Upon completion of training, an index frequency table is generated, which facilitates the assignment of codes to each index during the entropy encoding process. Through empirical analysis, for a codebook comprising 1024 codes, the average code length of all indices is determined to be I, 10.3875. For an input image of size H x W, the range of assigned indices spans from H W to H x W corresponding to fully coarse-grained and fine-grained patch divisions, respectively. For any given combination of granularity ratios (r1, r3), the theoretical bit-per-pixel (bpp) values for indices and mask are derived as follows:
16
L
BppIndices
(16r1 + 472 + r3).
(12)
256
1
Bppaask 256
(4r1 + r2).
(13)
where F1, 72, and r'y represent the ratios of fine, medium, and coarse patches, respectively.
Based on these equations, query table mapping granularity ratios to theoretical bpp values is constructed. When user-specified hpp is provided, the model automatically searches for the closest theoretical bpp in the query table and assigns the corresponding granularity ratios. In our experiments, the discrepancy between the theoretical and actual bpp values is consistently less than 0.05. Specifically, at high bit rates, the actual bpp tends to be slightly lower than the theoretical value, whereas at low bit rates, the actual bpp is marginally higher. To address this, our model allows users to adjust the granularity ratios to minimize the error and achieve optimal results. Table 2 provides simplified query table illustrating the relationship between granularity ratios and theoretical bpp values. This approach enables precise control over bitrates, ensuring high flexibility and accuracy in compression.
Table 2:
Partial query table of granularity ratios and hpp values.
Granularity Ratio
Bpp
F1
F2
r3
0
23%
77%
0.070
10%
67%
23%
0.187
37%
46%
17%
0.330
61%
30%
9%
0.460
90%
10%
0
0.616
A.4 VISUALIZATION OF DIFFERENT GRANULARITY RATIOS
In our method, the compression performance is highly dependent on the combination of granularity ratios rı, 72, and r3. which correspond to fine, medium, and coarse patches, respectively. To investigate their impact, we compress images using different combinations of these ratios and visualize the qualitative results on the Kodak dataset in Figure (a). Generally, the visual quality improves as 71 and r2 increase. This is because higher proportion of fine- and medium- granularryyy patches provides more local texture cues, which are crucial for detail recovery. However, increasing 12 and r3 also leads to higher bitrate costs.
To further analyze the effect of granularity ratios, we conduct experiments on images containing small faces, comparing our results with those CDC (Yang & Mandi 2024) and MS-ILLM (Muckley et al., 2023). As shown in Figure 9 (b), small faces pose significant challenge for image compression. Both CDC and MS- IISI struggle to recover fine details in these regions. When coarse- and medium- granularity ratios are excessively high in the face area (i.e., large patch sizes), our model also fails to reconstruct facial details effectively. However, by assigning fine- -raanuraarttty patches to the face region and reducing their patch size, artifacts significantly alleviated, resulting in clearer details. For
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Original (Bpp LPIPS)
0.104 0.144 (0, 50%, 50%)
0.138 0.097 (0, 80%, 20%)
(f1, T2, ra)
0.351 00.43 (40%, 50%, 10%)
0.490 0.033 (70%, 30%, 0) (a)
0.606 0.032
(100%, 0, 0)
Original / Bpp/ L.PPS (r), r2, 73)
CDC 0.362 0.088
MS-ILLM /0.353 0.043
Ours 0.139 0.090 (0, 80%, 20%)
Ours 0.226 0.061 (15%, 65%, 20%) (b)
Ours 0.339 0.045 (40%, 40%, 20%)
Figure 9: Compression results with different gramularity ratios. (a) Building images; (b) Small face images. Here, Ti, 72, and ra denote fine, medium, and coarse granularity ratios, respectively.
instance, our model with granularity ratios (40%, 40%, 20%) achieves better reconstruction quality at lower bitrate. Despite these improvements, the complexity of small faces highlights the need for more targeted design in future work.
A.5 EFFECTIVENESS ON THE CLIC2020 DATASET
Here, we evaluate the proposed method on the CLIC2020 dataset (Toderici et al. 2020) which contains 428 images. In Figure TO we provide the R-D curves for Control-GIC and existing state-
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BPG
VVC
M&S (Hyperprios)
- SCR (NewrIPS22)
CTC (CVPR23)
--- HIFC (NcurIPS20)
-#- CDC (NeurIPS23))
MS-ILLM (ICML23) - MRIC (CVPR23)
Ours
60
DJSTS
0.15
0.10
0.05
0.05
0.00
0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.1
0.3
Hpp
Bpp
Hpp
0.05
0.04
0.03
PSNR1
NIQE:
$6.02
25
3.0
0.1
0.3
0.5
Bpp
Hyp
Bpp
Figure 10: Compression performance on the CLIC2020 dataset with compared methods. The lines with forks represent GIC methods, and the lines with rhombus represent variable-rate and progressive methods.
of-the-art methods, which are measured in six metrics: LPIPS, DISTS, FID, KID, PSNR, and NIQE. be seen that Control-GIC achieves superior performance in most metrics over conventional codecs BPG, VVC, and variable-rate and progressive methods SCR, CTC. Compared to generative compression methods which are trained separately for multiple R-D points, our Control-GIC still maintains competitive performance, which validates that our method can achieve optimal trade-off between flexibility and effectiveness.
A.6 EXTENSION TO EXTREMELY LOW BITRATE COMPRESSION
As described in Section our method take three representation granularities: 4 x 4, 8 x 8, and
16 x 16. The codebook C' E &xd consists of & 1024 codes, each with dimension of d = 4. The lowest bitrate of our method corresponds to a fully coarse-gras coarse-grained partition, i.e. (r1, F2, (3) (0, 0, 100%). In this section, investigate the performance of method for extremely low bitrate compression (< 0.05 bpp), comparing with Mao et al. (Mao et al. 2023), a VQGAN-based method designed for very low bitrate compression. As shown in Table our method achieves the best LPIPS on both the Kodak and CLIC2020 datasets while maintaining lower bpp. validating its superiority. Furthermore, Figure 11 provides visual comparisons of compressed images, illustrating that method generalizes well to extremely low bitrates and produces vivid reconstructionss.
Table 3: Quantitative performance at the extremely low bitrate on the Kodak and CLIC2020 datasets.
Dataset
Kodak
CLIC2020
Method
Ours
Mao et al.
Ours
Mao el al.
Bppl
0.0381
0.0391
0.0372
0.0389
LPIPS)
0.115
0.1.6
0.086
0.112
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Published as a conference paper at ICLR 2025
Original / Bpp / I.PIPS
Mao et al. / 00.091 0.234
Ours / 0.0381 / 0.203
Original Bpp LPIPS
Mao et al. / 0.0389 0.204
Ours 0.0382 / 0.168
Figure 11:
The visual results compressed by Mao er al. (Mao et al. 2023 and our Control-GIC at the extremely low bitrate on the Kodak (top) and CLIC2020 (bottom) datasets.
A.7 ADDITIONAL VISUALIZATION
Original (Bpp / LPIPS)
VVC (0.261 0.117)
M&S (0.265 / 0.204)
SCR (0.256 / 0.234)
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Published as a conference paper at ICLR 2025
CTC (0.279/01 0.199)
HiFIC (0.309 / 0.064)
CDC (0.314/ 0.009
Ours (0.258/ 0.054)
Figure 12: Rconstructed images of Kodim22. Bitrate (bpp) and LPIPS are below each image.
Original (Bpp/ / LPIPS)
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Published as a conference paper at ICLR 2025
VVC (1.069 / 0.093)
HIFI (0.452/ / 0.115)
Ours (0.448/ 0.066)
Figure 13: Reonstructed images of DIV2K0807. Bitrate (bpp) and LPIPS are below each image.
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Reviews:
[{"review_id": "ugBm9ovdl4", "reviewer": "Reviewer_o1HZ", "summary": "The paper titled \"ONCE-FOR-ALL: CONTROLLABLE GENERATIVE IMAGE COMPRESSION WITH DYNAMIC GRANULARITY ADAPTION\" introduces Control-GIC, a framework for controllable generative image compression. It addresses the challenge of flexible rate adaption in image compression by leveraging a VQGAN framework that encodes images as variable-length codes . The framework correlates local image patch information density with granular representations, allowing for fine-grained bitrate control. It includes a granularity-informed encoder, a statistical entropy coding module, and a probabilistic conditional decoder. The experiments demonstrate that Control-GIC outperforms state-of-the-art methods in terms of flexibility, perceptual quality, and compression efficiency.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Control-GIC combines classical coding principles with VQGAN to achieve controllable generative compression across various bitrates with a unified model.\n2. The framework allows for highly flexible and controllable bitrate adaption, which is a significant advancement over existing methods.\n3. Unlike other methods that require training multiple models for different bitrates, Control-GIC can adapt to various bitrates with a single model, reducing computational costs.", "weaknesses": "1. More comparion with other GIC methods need to be provided.\n2. The novelty is limited compared to other VQGan based GIC 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": {}}, {"review_id": "6mDRAfwW9Q", "reviewer": "Reviewer_qmzM", "summary": "This paper introduces Control-GIC, a controllable generative image compression framework aimed at addressing the limitations of existing generative image compression methods in achieving flexible bitrate adjustment. Built upon the VQGAN framework, Control-GIC incorporates multi-granularity encoding mechanisms and a probabilistic conditional decoder to achieve flexible bitrate control and high-quality image reconstruction. Both qualitative and quantitative results demonstrate the effectiveness of the proposed method.", "questions": "1. Why is the mask ratio set to 50%, 40%, 10% for fine, medium, and coarse features in the training setting? Please provide further justification for this choice.\n2. In Mao et al.'s papers [2, 3], the bitrate used in VQGAN is significantly lower, ranging from <0.05 bpp to ≤0.03 bpp, while in GIC, the bpp ranges from 0.1 to 0.6 bpp. Could you explain why the bitrate range cannot be lower in Control-GIC, and if it is possible to extend the model to support lower bitrates?\n\n\n\nReferences:\n[1] Qi Mao, et al, Extreme image compression using fine-tuned vqgan models.DCC, 2024.\n[2] Naifu Xue, Qi Mao, et al, Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer.ICME.2024.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed Control-GIC introduces a unified model that allows dynamic bitrate adjustment, which effectively solves the inefficiencies faced by existing models that need multiple fixed-rate versions.\n2. The granularity-informed encoder and probabilistic conditional decoder are well-designed to achieve efficient encoding and high perceptual fidelity.\n3. Experimental results show superior performance over state-of-the-art methods, demonstrating both flexibility and effectiveness in compression.", "weaknesses": "1. The paper lacks sufficient details on how the features are divided into different granularities in Section 3.1.\n2. The DIV2K comparisons in Figure 4 do not include evaluations against important baselines like VVC, M&S, and other methods (presented in Figure 3), which limits the completeness of the analysis.\n3. The paper does not compare Control-GIC with other VQ-based methods, such as GLC [1], Mao et al. [2], and UIGC [3], which would provide a better context for understanding the model's relative performance.\n4. It is suggested that the authors add more comparisons with results from other datasets featuring different image sizes to enhance the robustness, such as CLIC-dataset.\n\n\nReferences:\n[1] Jia Z, Li J, Li B, et al. Generative Latent Coding for Ultra-Low Bitrate Image Compression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 26088-26098.\n[2] Qi Mao, et al, Extreme image compression using fine-tuned vqgan models. DCC, 2024.\n[3] Naifu Xue, Qi Mao, et al, Unifying Generation and Compression: Ultra-low bitrate Image Coding Via Multi-stage Transformer.ICME.2024.", "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": "ZL2GQCqvVU", "reviewer": "Reviewer_P96B", "summary": "This paper targets at addressing the challenge of rate adaption proplem for generative image compression and proposes a controllable generative image compression framework. The paper represents the image patches of sequential spatially variant VQ-indices to support precise variable rate control and adaption. A non-parametric statistical entropy coding is devised to encode the VQ-indices losslessly.\nA probabilistic conditional decoder is proposed to aggregate historic encoded multi-granularity representations, achieving realism improvements.", "questions": "see the weakness part", "limitations": "", "rating": 3, "confidence": 5, "soundness": 1, "presentation": 1, "contribution": 1, "strengths": "The paper proposes to flexibly determine a proper allocation of granularity for patches, supporting dynamic adjustment for VQ-indices and make the framework capable of fine-grained bitrate adaptation.", "weaknesses": "- In Figure 3, the proposed method shows worse performance than CDC for DISTS, and is also worse than CDC at the high bitrate range. Could you give some analysis? \n\n- In Figure 4, why do you compare only with BPG, CTC, and HiFiC, instead of aligning with the methods used in Figure 3?\n\n- In Figure 3 and Figure 4, most metrics are nearly reaching saturation when the bpp increases. Is there obvious difference for the visualization quality when the bpp increases at the high bitrate range? For example, in Figure 6, when the bpp increases, I do not see obvious enhancement between r2=40%(bpp=0.3864) and r2=32.9%(0.4171).\n\n- Visualization analysis about the influence of the granularity of image patches. Will increasing the image patch size lead to block artifacts in the reconstructed images? And can making the image patch size smaller solve some artifacts in VQ based methods, such as the unsatisfactory artifacts for small faces?", "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": "q8GVUZD43R", "reviewer": "Reviewer_WUkA", "summary": "The paper Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption introduces Control-GIC, a flexible framework for high-quality image compression that allows fine-grained bitrate control without needing multiple models. Built on a VQGAN foundation, Control-GIC encodes images as variable-length sequences, adapting bitrate based on local image information density, which allows for effective compression adjustments to meet different content complexities. The model includes a granularity-aware encoder that assigns varying levels of detail across image patches, and a probabilistic conditional decoder that reconstructs images with high perceptual quality by aggregating multi-scale features. Experimental results show that Control-GIC outperforms recent state-of-the-art methods in perceptual quality, flexibility, and efficiency, achieving this with a single unified model across a wide bitrate range.", "questions": "1. How are the masks $m_1$, $m_2$, and $m_3$ obtained? The main text does not seem to provide a clear calculation method for these.\n2. If compression is required at a specific bitrate, how should $r_1$, $r_2$, and $r_3$ be determined?", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The authors’ granularity-informed encoder effectively leverages the information density distribution to distill image patches into a hierarchical structure with three levels of granularity. This approach enables compression with a controllable encoding rate, providing a novel solution for adaptable image compression.\n\n2. The paper’s codec framework is lightweight and demonstrates relatively fast encoding and decoding speeds, making it more suitable for practical applications where computational efficiency is a priority.", "weaknesses": "1. The experimental results show mixed outcomes compared to existing baselines like HiFiC, without demonstrating a clear advantage.\n2. The paper should consider comparing with MS-ILLM [1], which uses a similar VQ-VAE structure, to better position its contributions.\n3. Tests on the CLIC 2020 dataset are missing, which would help validate the model's robustness across diverse datasets.\n4. Section 3.2 is overly detailed and could be streamlined for clarity and conciseness.\n5. Section 3.3 appears to be a direct application of existing methods, which could benefit from additional elaboration or innovation.\n\n[1] Muckley M J, El-Nouby A, Ullrich K, et al. Improving statistical fidelity for neural image compression with implicit local likelihood models. International Conference on Machine Learning. PMLR, 2023: 25426-25443.", "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": "A5gzM3PNAJ", "reviewer": "Reviewer_bPMe", "summary": "This paper presents a novel framework for controllable generative image compression (Control-GIC). The proposed Control-GIC integrates a granularity-aware encoder to enable precise variable rate control and adaptation, a non-parametric statistical entropy coding method for lossless encoding of VQ-indices, and a probabilistic conditional decoder to reconstruct hierarchical granular features. Experimental results on the Kodak and DIV2K datasets demonstrate that Control-GIC not only delivers strong perceptual compression performance but also provides highly flexible and controllable bitrate adaptation.", "questions": "Please see the above weakness.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. A comprehensive explanation on adapting VQ-GAN into a rate-adaptive perceptual codec.\n\n2. Highly flexible and controllable bitrate adaptation.\n\n3. Well-conducted ablation study.\n\n4. Open-sourced code! This is fantastic, and I want to thank the authors for their commitment to sharing this.", "weaknesses": "1. Lack of comparison with the latest state-of-the-art (SOTA) perceptual codec, MS-ILLM [1]. It appears that MS-ILLM outperforms both HiFiC and Control-GIC in terms of compression performance.\n\n2. Control-GIC utilizes a mask for multi-scale encoding, but the method for generating the masks $m_1, m_2$ and $m_3$ during training and inference is not clearly explained. Please provide a more detailed description of this process.\n\n3. Misuse of capitalization: 'The Encoding and decoding' on line 432 should be corrected to 'The encoding and decoding.'\"\n\n[1] MJ Muckley, A El-Nouby, K Ullrich, H Jegou, J Verbeek. Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. In ICML, 2023.", "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)