Learned Image Compression

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Learned image compression trains neural encoders and decoders to represent images compactly, then uses quantization and learned entropy models to code those representations efficiently. Common approaches include convolutional or transformer-based codecs with hyperpriors and autoregressive context models, alongside methods that adapt bit allocation to image regions or downstream tasks. On benchmarks such as Kodak and CLIC, learned codecs are compared with standards like VVC using rate-distortion measures, perceptual metrics, and task accuracy; diffusion-based decoders also target realistic reconstructions at extremely low bitrates. A central tension is balancing source-faithful detail against perceptual realism or machine-task performance, while keeping encoding, decoding, and model costs practical.

Latest papers

120 papers16 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Apr 14, 2026

CoD-Lite: Real-Time Diffusion-Based Generative Image Compression

Recent advanced diffusion methods typically derive strong generative priors by scaling diffusion transformers. However, scaling fails to generalize when adapted for real-time compression scenarios that demand lightweight models. In this paper, we explore the design of real-time and lightweight diffusion codecs by addressing two pivotal questions. First, does diffusion pre-training benefit lightweight diffusion codecs? Through systematic analysis, we find that generation-oriented pre-training is less effective at small model scales whereas compression-oriented pre-training yields consistently better performance. Second, are transformers essential? We find that while global attention is crucial for standard generation, lightweight convolutions suffice for compression-oriented diffusion when paired with distillation. Guided by these findings, we establish a one-step lightweight convolution diffusion codec that achieves real-time

60

~FPS encoding and

42

~FPS decoding at 1080p. Further enhanced by distillation and adversarial learning, the proposed codec reduces bitrate by 85% at a comparable FID to MS-ILLM, bridging the gap between generative compression and practical real-time deployment. Codes are released at https://github.com/microsoft/GenCodec/tree/main/CoD_Lite

Zhaoyang Jia, Naifu Xue, Zihan Zheng +7

University of Science and Technology of China · Communication University of China · Microsoft Research Asia

Generative Image Compression Learned Image Compression Diffusion Model Inference Acceleration

Apr 11, 2026

What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters

Parameter-efficient fine-tuning of pre-trained codecs is a promising direction in image compression for human and machine vision. While most existing works have primarily focused on tuning the feature structure within the encoder-decoder backbones, the adaptation of the statistical semantics within the entropy model has received limited attention despite its function of predicting the probability distribution of latent features. Our analysis reveals that naive adapter insertion into the entropy model can lead to suboptimal outcomes, underscoring that the effectiveness of adapter-based tuning depends critically on the coordination between adapter type and placement across the compression pipeline. Therefore, we introduce Structure-Semantics Co-Tuning (S2-CoT), a novel framework that achieves this coordination via two specialized, synergistic adapters: the Structural Fidelity Adapter (SFA) and the Semantic Context Adapter (SCA). SFA is integrated into the encoder-decoder to preserve high-fidelity representations by dynamically fusing spatial and frequency information; meanwhile, the SCA adapts the entropy model to align with SFA-tuned features by refining the channel context for more efficient statistical coding. Through joint optimization, S2-CoT turns potential performance degradation into synergistic gains, achieving state-of-the-art results across four diverse base codecs with only a small fraction of trainable parameters, closely matching full fine-tuning performance. Code is available at https://github.com/Brock-bit4/S2-CoT.

Shaobo Liu, Haobo Xiong, Kai Liu +1

Xidian University

Entropy Modeling Learned Image Compression Adapter Tuning Parameter-Efficient Fine-Tuning

Apr 8, 2026

A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression

With the great success of diffusion models in image generation, diffusion-based image compression is attracting increasing interests. However, due to the random noise introduced in the diffusion learning, they usually produce reconstructions with deviation from the original images, leading to suboptimal compression results. To address this problem, in this paper, we propose a Noise Constrained Diffusion (NC-Diffusion) framework for high fidelity image compression. Unlike existing diffusion-based compression methods that add random Gaussian noise and direct the noise into the image space, the proposed NC-Diffusion formulates the quantization noise originally added in the learned image compression as the noise in the forward process of diffusion. Then a noise constrained diffusion process is constructed from the ground-truth image to the initial compression result generated with quantization noise. The NC-Diffusion overcomes the problem of noise mismatch between compression and diffusion, significantly improving the inference efficiency. In addition, an adaptive frequency-domain filtering module is developed to enhance the skip connections in the U-Net based diffusion architecture, in order to enhance high-frequency details. Moreover, a zero-shot sample-guided enhancement method is designed to further improve the fidelity of the image. Experiments on multiple benchmark datasets demonstrate that our method can achieve the best performance compared with existing methods.

Zhenyu Du, Yanbo Gao, Shuai Li +3

Shandong University, Jinan, China · University of Electronic Science and Technology of China, Sichuan, China

Learned Image Compression Generative Image Compression Diffusion Model Compression Diffusion Models

Apr 7, 2026

CI-ICM: Channel Importance-driven Learned Image Coding for Machines

Traditional human vision-centric image compression methods are suboptimal for machine vision centric compression due to different visual properties and feature characteristics. To address this problem, we propose a Channel Importance-driven learned Image Coding for Machines (CI-ICM), aiming to maximize the performance of machine vision tasks at a given bitrate constraint. First, we propose a Channel Importance Generation (CIG) module to quantify channel importance in machine vision and develop a channel order loss to rank channels in descending order. Second, to properly allocate bitrate among feature channels, we propose a Feature Channel Grouping and Scaling (FCGS) module that non-uniformly groups the feature channels based on their importance and adjusts the dynamic range of each group. Based on FCGS, we further propose a Channel Importance-based Context (CI-CTX) module to allocate bits among feature groups and to preserve higher fidelity in critical channels. Third, to adapt to multiple machine tasks, we propose a Task-Specific Channel Adaptation (TSCA) module to adaptively enhance features for multiple downstream machine tasks. Experimental results on the COCO2017 dataset show that the proposed CI-ICM achieves BD-mAP@50:95 gains of 16.25

\%

in object detection and 13.72

\%

in instance segmentation over the established baseline codec. Ablation studies validate the effectiveness of each contribution, and computation complexity analysis reveals the practicability of the CI-ICM. This work establishes feature channel optimization for machine vision-centric compression, bridging the gap between image coding and machine perception.

Yun Zhang, Junle Liu, Huan Zhang +3

School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-Sen University, Shenzhen 518107, China. · School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China. · School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China. +2

Learned Image Compression

Mar 31, 2026

Prompt-Guided Prefiltering for VLM Image Compression

The rapid progress of large Vision-Language Models (VLMs) has enabled a wide range of applications, such as image understanding and Visual Question Answering (VQA). Query images are often uploaded to the cloud, where VLMs are typically hosted, hence efficient image compression becomes crucial. However, traditional human-centric codecs are suboptimal in this setting because they preserve many task-irrelevant details. Existing Image Coding for Machines (ICM) methods also fall short, as they assume a fixed set of downstream tasks and cannot adapt to prompt-driven VLMs with an open-ended variety of objectives. We propose a lightweight, plug-and-play, prompt-guided prefiltering module to identify image regions most relevant to the text prompt, and consequently to the downstream task. The module preserves important details while smoothing out less relevant areas to improve compression efficiency. It is codec-agnostic and can be applied before conventional and learned encoders. Experiments on several VQA benchmarks show that our approach achieves a 25-50% average bitrate reduction while maintaining the same task accuracy. Our source code is available at https://github.com/bardia-az/pgp-vlm-compression.

Bardia Azizian, Ivan V. Bajic

School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

Vision-Language Models Learned Image Compression

Mar 31, 2026

End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines

Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy and lossless) compression. The segmentation module accurately identifies the blade region, after which our region-of-interest (ROI) compressor encodes it at superior quality compared to the rest of the image. Unlike conventional ROI schemes that merely allocate more bits to salient areas, our framework integrates: (i) a robust segmentation network (BU-Netv2+P) with a CRF-regularized loss for precise blade localization, (ii) a hyperprior-based autoencoder optimized for lossy compression, and (iii) an extended bits-back coder with hierarchical models for fully lossless blade reconstruction. Furthermore, our ROI framework removes the sequential dependency in bits-back coding by reusing background-coded bits, enabling parallelized and efficient dual-mode compression. To the best of our knowledge, this is the first fully integrated learning-based ROI codec combining segmentation, lossy, and lossless compression, ensuring that subsequent defect detection is not compromised. Experiments on a large-scale wind turbine dataset demonstrate superior compression performance and efficiency, offering a practical solution for automated inspections.

Raül Pérez-Gonzalo, Andreas Espersen, Søren Forchhammer +1

Wind Power LAB, 1150 Copenhagen, Denmark · Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 08028 Barcelona, Spain · Department of Photonics Engineering, Technical University of Denmark (DTU), 2800 Lyngby, Denmark

Learned Image Compression Entropy Coding Industrial Inspection

Mar 30, 2026

RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression

Raw images preserve linear sensor measurements and high bit-depth information crucial for advanced vision tasks and photography applications, yet their storage remains challenging due to large file sizes, varying bit depths, and sensor-dependent characteristics. Existing learned lossless compression methods mainly target 8-bit sRGB images, while raw reconstruction approaches are inherently lossy and rely on camera-specific assumptions. To address these challenges, we introduce RAWIC, a bit-depth-adaptive learned lossless compression framework for Bayer-pattern raw images. We first convert single-channel Bayer data into a four-channel RGGB format and partition it into patches. For each patch, we compute its bit depth and use it as auxiliary input to guide compression. A bit-depth-adaptive entropy model is then designed to estimate patch distributions conditioned on their bit depths. This architecture enables a single model to handle raw images from diverse cameras and bit depths. Experiments show that RAWIC consistently surpasses traditional lossless codecs, achieving an average 7.7% bitrate reduction over JPEG-XL. Our code is available at https://github.com/chunbaobao/RAWIC.

Chunhang Zheng, Tongda Xu, Mingli Xie +2

School of Electronics, Peking University, Beijing, 100871, China · Institute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China · the University of Chinese Academy of Sciences, Beijing, 100190, China

Learned Image Compression

Mar 27, 2026

HyVIC: A Metric-Driven Spatio-Spectral Hyperspectral Image Compression Architecture Based on Variational Autoencoders

The rapid growth of hyperspectral data archives in remote sensing (RS) necessitates effective compression methods for storage and transmission. Recent advances in learning-based hyperspectral image (HSI) compression have significantly enhanced both reconstruction fidelity and compression efficiency. However, existing methods typically adapt variational image compression models designed for natural images, without adequately accounting for the distinct spatio-spectral redundancies inherent in HSIs. In particular, they lack explicit architectural designs to balance spatial and spectral feature learning, limiting their ability to effectively leverage the unique characteristics of hyperspectral data in RS. To address this issue, in this paper, we aim to study the effects of spatio-spectral feature learning on the rate-distortion (RD) performance of variational HSI compression as a first time in RS. To this end, we propose to use configurable spatial and spectral feature learning blocks within variational HSI compression. To achieve this, we introduce spatio-spectral variational hyperspectral image compression architecture (HyVIC), a configurable variational autoencoder (VAE) for HSI compression. Extensive experiments on two benchmark datasets demonstrate that the trade-off between spatial and spectral feature learning is crucial for the reconstruction fidelity. Motivated by this, we also present a metric-driven strategy to systematically select the hyperparameters of the proposed model. In detail, HyVIC achieves high spatial and spectral reconstruction fidelity across a wide range of compression ratios (CRs) and improves the state of the art by up to 4.66dB in terms of BD-PSNR. Our code and pre-trained model weights are publicly available at https://git.tu-berlin.de/rsim/hyvic .

Martin Hermann Paul Fuchs, Behnood Rasti, Begüm Demir

Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany

Learned Image Compression Hyperspectral Imaging Variational Autoencoders

Mar 26, 2026

Adaptive Learned Image Compression with Graph Neural Networks

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standard CNN kernels and window-based attention mechanisms impose fixed receptive fields and static connectivity patterns, which potentially couple non-redundant pixels simply due to their proximity in Euclidean space. This rigidity limits the model's ability to adaptively capture spatially varying redundancy across the image, particularly at the global level. To overcome these limitations, we propose a content-adaptive image compression framework based on Graph Neural Networks (GNNs). Specifically, our approach constructs dual-scale graphs that enable flexible, data-driven receptive fields. Furthermore, we introduce adaptive connectivity by dynamically adjusting the number of neighbors for each node based on local content complexity. These innovations empower our Graph-based Learned Image Compression (GLIC) model to effectively model diverse redundancy patterns across images, leading to more efficient and adaptive compression. Experiments demonstrate that GLIC achieves state-of-the-art performance, achieving BD-rate reductions of 19.29%, 21.69%, and 18.71% relative to VTM-9.1 on Kodak, Tecnick, and CLIC, respectively. Code will be released at https://github.com/UnoC-727/GLIC.

Yunuo Chen, Bing He, Zezheng Lyu +4

Shanghai Jiao Tong University · Massachusetts Institute of Technology · Alibaba Group +1

Learned Image Compression

Mar 24, 2026

Viewport-based Neural 360° Image Compression

Given the popularity of 360° images on social media platforms, 360° image compression becomes a critical technology for media storage and transmission. Conventional 360° image compression pipeline projects the spherical image into a single 2D plane, leading to issues of oversampling and distortion. In this paper, we propose a novel viewport-based neural compression pipeline for 360° images. By replacing the image projection in conventional 360° image compression pipelines with viewport extraction and efficiently compressing multiple viewports, the proposed pipeline minimizes the inherent oversampling and distortion issues. However, viewport extraction impedes information sharing between multiple viewports during compression, causing the loss of global information about the spherical image. To tackle this global information loss, we design a neural viewport codec to capture global prior information across multiple viewports and maximally compress the viewport data. The viewport codec is empowered by a transformer-based ViewPort ConText (VPCT) module that can be integrated with canonical learning-based 2D image compression structures. We compare the proposed pipeline with existing 360° image compression models and conventional 360° image compression pipelines building on learning-based 2D image codecs and standard hand-crafted codecs. Results show that our pipeline saves an average of

14.01\%

bit consumption compared to the best-performing 360° image compression methods without compromising quality. The proposed VPCT-based codec also outperforms existing 2D image codecs in the viewport-based neural compression pipeline. Our code can be found at: https://github.com/Jingwei-Liao/VPCT.

Jingwei Liao, Bo Chen, Klara Nahrstedt +1

George Mason University, Fairfax, USA · University of Illinois Urbana-Champaign, Urbana, USA

Learned Image Compression

Mar 22, 2026

DepthTCM: High Efficient Depth Compression via Physics-aware Transformer-CNN Mixed Architecture

We propose DepthTCM, a physics-aware end-to-end framework for depth map compression. In our framework of DepthTCM, the high-bit depth map is first converted to a conventional 3-channel image representation losslessly using a method inspired by a physical sinusoidal fringe pattern based profiliometry system, then the 3-channel color image is encoded and decoded by a recently developed Transformer-CNN mixed neural network architecture. Specifically, DepthTCM maps depth to a smooth 3-channel using multiwavelength depth (MWD) encoding, then globally quantized the MWD encoded representation to 4 bits per channel to reduce entropy, and finally is compressed using a learned codec that combines convolutional and Transformer layers. Experiment results demonstrate the advantage of our proposed method. On Middlebury 2014, DepthTCM reaches 0.307 bpp while preserving 99.38% accuracy, a level of fidelity commensurate with lossless PNG. We additionally demonstrate practical efficiency and scalability, reporting average end-to-end inference times of 41.48 ms (encoder) and 47.45 ms (decoder) on the ScanNet++ iPhone RGB-D subset. Ablations validate our design choices: relative to 8-bit quantization, 4-bit quantization reduces bitrate by 66% while maintaining comparable reconstruction quality, with only a marginal 0.68 dB PSNR change and a 0.04% accuracy difference. In addition, Transformer--CNN blocks further improve PSNR by up to 0.75 dB over CNN-only architectures.

Young-Seo Chang, Yatong An, Jae-Sang Hyun

Learned Image Compression

Mar 18, 2026

Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression

Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each target bitrate, resulting in substantial computational overhead and hindering practical deployment. Meanwhile, recent studies have shown that joint super-resolution can serve as an effective approach for enhancing low-bitrate reconstruction. However, when moving toward ultra-low bitrate regimes, these methods struggle due to severe information loss, and their reliance on fixed super-resolution scales prevents flexible adaptation across diverse bitrates. To address these limitations, we propose ASSR-EIC, a novel image compression framework that leverages arbitrary-scale super-resolution (ASSR) to support variable-rate extreme image compression (EIC). An arbitrary-scale downsampling module is introduced at the encoder side to provide controllable rate reduction, while a diffusion-based, joint degradation-aware ASSR decoder enables rate-adaptive reconstruction within a single model. We exploit the compression- and rescaling-aware diffusion prior to guide the reconstruction, yielding high fidelity and high realism restoration across diverse compression and rescaling settings. Specifically, we design a global compression-rescaling adaptor that offers holistic guidance for rate adaptation, and a local compression-rescaling modulator that dynamically balances generative and fidelity-oriented behaviors to achieve fine-grained, bitrate-adaptive detail restoration. To further enhance reconstruction quality, we introduce a dual semantic-enhanced design. Extensive experiments demonstrate that ASSR-EIC delivers state-of-the-art performance in extreme image compression while simultaneously supporting flexible bitrate control and adaptive rate-dependent reconstruction.

Xinning Chai, Zhengxue Cheng, Xin Li +2

School of Information Science and Electronic Engineering, Shanghai Jiao Tong University · Department of Electronic Engineer and Information Science, University of Science and Technology of China · School of Information Science and Electronic Engineering, Shanghai Jiao Tong University and the MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China

Learned Image Compression Generative Image Compression Image Super-Resolution Diffusion Models for Image Restoration

Mar 16, 2026

A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression

Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution drone imagery offers valuable structural details for urban monitoring and disaster assessment, but large-area datasets can easily reach hundreds of gigabytes, creating significant challenges for storage and long-term management. In this paper, we propose a PPO-based bitrate allocation Conditional Diffusion Compression (PCDC) framework. PCDC integrates a conditional diffusion decoder with a PPO-based block-wise bitrate allocation strategy to achieve high compression ratios while maintaining strong perceptual performance. We also release a high-resolution drone image dataset with richer structural details at a consistent low altitude over residential neighborhoods in coastal urban areas. Experimental results show compression ratios of 19.3x on DIV2K and 21.2x on the drone image dataset. Moreover, downstream object detection experiments demonstrate that the reconstructed images preserve task-relevant information with negligible performance loss.

Yuming Han, Jooho Kim, Anish Shakya

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843 USA · Institute for a Disaster Resilient Texas, Texas A&M University, College Station, TX 77843 USA · Department of Marine & Coastal Environmental Science, Texas A&M University, Galveston, TX 77554 USA

Learned Image Compression Generative Image Compression

Mar 16, 2026

Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors

We present a novel paradigm for ultra-low-bitrate image compression (ULB-IC) that exploits the ``temporal'' evolution in generative image compression. Specifically, we define an explicit intermediate state during decoding: a compact anchor frame, which preserves the scene geometry and semantic layout while discarding high-frequency details. We then reinterpret generative decoding as a virtual temporal transition from this anchor to the final reconstructed image. To model this progression, we leverage a pretrained video diffusion model (VDM) as a temporal prior: the anchor frame serves as the initial frame and the original image as the target frame, transforming the decoding process into a next-frame prediction task. In contrast to image diffusion-based ULB-IC models, our decoding proceeds from a visible, semantically faithful anchor, which improves both fidelity and realism for perceptual image compression. Extensive experiments demonstrate that our method achieves superior rate-distortion performance. On the CLIC2020 test set, our method achieves over 50% bitrate savings across LPIPS, DISTS, FID, and KID compared to DiffC, while also delivering a significant decoding speedup of up to

\times

5. Code will be released at https://github.com/UnoC-727/NeFIC.

Yunuo Chen, Chuqin Zhou, Jiangchuan Li +5

Shanghai Jiao Tong University, Shanghai, China · Beijing University of Posts and Telecommunications, Beijing, China

Learned Image Compression Generative Image Compression

Mar 13, 2026

DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression

Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high memory usage. Most existing diffusion codecs employ U-Net architectures, where hierarchical downsampling forces diffusion to operate in shallow latent spaces (typically with only 8x spatial downscaling), resulting in excessive computation. In contrast, conventional VAE-based codecs work in much deeper latent domains (16x - 64x downscaled), motivating a key question: Can diffusion operate effectively in such compact latent spaces without compromising reconstruction quality? To address this, we introduce DiT-IC, an Aligned Diffusion Transformer for Image Compression, which replaces the U-Net with a Diffusion Transformer capable of performing diffusion in latent space entirely at 32x downscaled resolution. DiT-IC adapts a pretrained text-to-image multi-step DiT into a single-step reconstruction model through three key alignment mechanisms: (1) a variance-guided reconstruction flow that adapts denoising strength to latent uncertainty for efficient reconstruction; (2) a self-distillation alignment that enforces consistency with encoder-defined latent geometry to enable one-step diffusion; and (3) a latent-conditioned guidance that replaces text prompts with semantically aligned latent conditions, enabling text-free inference. With these designs, DiT-IC achieves state-of-the-art perceptual quality while offering up to 30x faster decoding and drastically lower memory usage than existing diffusion-based codecs. Remarkably, it can reconstruct 2048x2048 images on a 16 GB laptop GPU.

Junqi Shi, Ming Lu, Xingchen Li +3

School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

Learned Image Compression Generative Image Compression Diffusion Model Inference Acceleration Diffusion Transformer

Mar 10, 2026

ARCHE: Autoregressive Residual Compression with Hyperprior and Excitation

Recent progress in learning-based image compression has demonstrated that end-to-end optimization can substantially outperform traditional codecs by jointly learning compact latent representations and probabilistic entropy models. However, many existing approaches achieve high rate-distortion efficiency at the expense of increased computational cost and limited parallelism. This paper presents ARCHE - Autoregressive Residual Compression with Hyperprior and Excitation, an end-to-end learned image compression framework that balances modeling accuracy and computational efficiency. The proposed architecture unifies hierarchical, spatial, and channel-based priors within a single probabilistic framework, capturing both global and local dependencies in the latent representation of the image, while employing adaptive feature recalibration and residual refinement to enhance latent representation quality. Without relying on recurrent or transformer-based components, ARCHE attains state-of-the-art rate-distortion efficiency: it reduces the BD-Rate by approximately 48% relative to the commonly used benchmark model of Balle et al., 30% relative to the channel-wise autoregressive model of Minnen & Singh and 5% against the VVC Intra codec on the Kodak benchmark dataset. The framework maintains computational efficiency with 95M parameters and 222ms running time per image. Visual comparisons confirm sharper textures and improved color fidelity, particularly at lower bit rates, demonstrating that accurate entropy modeling can be achieved through efficient convolutional designs suitable for practical deployment.

Sofia Iliopoulou, Dimitris Ampeliotis, Athanassios Skodras

Dept. of Electrical and Computer Engineering, University of Patras, Patras, Greece · Dept. of Digital Media and Communication, Ionian University, Argostoli, Greece

Learned Image Compression Autoregressive Modeling

Mar 6, 2026

HiDE: Hierarchical Dictionary-Based Entropy Modeling for Learned Image Compression

Learned image compression (LIC) has achieved remarkable coding efficiency, where entropy modeling plays a pivotal role in minimizing bitrate through informative priors. Existing methods predominantly exploit internal contexts within the input image, yet the rich external priors embedded in large-scale training data remain largely underutilized. Recent advances in dictionary-based entropy models have demonstrated that incorporating external priors can substantially enhance compression performance. However, current approaches organize heterogeneous external priors within a single-level dictionary, resulting in imbalanced utilization and limited representational capacity. Moreover, effective entropy modeling requires not only expressive priors but also a parameter estimation network capable of interpreting them. To address these challenges, we propose HiDE, a Hierarchical Dictionary-based Entropy modeling framework for learned image compression. HiDE decomposes external priors into global structural and local detail dictionaries with cascaded retrieval, enabling structured and efficient utilization of external information. Moreover, a context-aware parameter estimator with parallel multi-receptive-field design is introduced to adaptively exploit heterogeneous contexts for accurate conditional probability estimation. Experimental results show that HiDE achieves 18.5%, 21.99%, and 24.01% BD-rate savings over VTM-12.1 on the Kodak, CLIC, and Tecnick datasets, respectively.

Haoxuan Xiong, Yuanyuan Xu, Kun Zhu +2

Hohai University · Nanjing University of Aeronautics and Astronautics · Nanjing Audit University +1

Learned Image Compression Entropy Coding

Mar 4, 2026

TextBoost: Boosting Scene Text Fidelity in Ultra-low Bitrate Image Compression

Ultra-low bitrate image compression faces a critical challenge: preserving small-font scene text while maintaining overall visual quality. Region-of-interest (ROI) bit allocation can prioritize text but often degrades global fidelity, leading to a trade-off between local accuracy and overall image quality. Instead of relying on ROI coding, we incorporate auxiliary textual information extracted by OCR and transmitted with negligible overhead, enabling the decoder to leverage this semantic guidance. Our method, TextBoost, operationalizes this idea through three strategic designs: (i) adaptively filtering OCR outputs and rendering them into a guidance map; (ii) integrating this guidance with decoder features in a calibrated manner via an attention-guided fusion block; and (iii) enforcing guidance-consistent reconstruction in text regions with a regularizing loss that promotes natural blending with the scene. Extensive experiments on TextOCR and ICDAR 2015 demonstrate that TextBoost yields up to 60.6% higher text-recognition F1 at comparable Peak Signal-to-Noise Ratio (PSNR) and bits per pixel (bpp), producing sharper small-font text while preserving global image quality and effectively decoupling text enhancement from global rate-distortion optimization.

Bingxin Wang, Yuan Lan, Zhaoyi Sun +2

Department of Mathematics, The Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Sai Kung District, Hong Kong SAR, China · Huawei Hong Kong Research Center, 8/F, 2 Science Park West Avenue, Hong Kong Science Park, Sha Tin, Hong Kong SAR, China

Learned Image Compression

Mar 4, 2026

Parallax to Align Them All: An OmniParallax Attention Mechanism for Distributed Multi-View Image Compression

Multi-view image compression (MIC) aims to achieve high compression efficiency by exploiting inter-image correlations, playing a crucial role in 3D applications. As a subfield of MIC, distributed multi-view image compression (DMIC) offers performance comparable to MIC while eliminating the need for inter-view information at the encoder side. However, existing methods in DMIC typically treat all images equally, overlooking the varying degrees of correlation between different views during decoding, which leads to suboptimal coding performance. To address this limitation, we propose a novel

\textbf{OmniParallax Attention Mechanism}

(OPAM), which is a general mechanism for explicitly modeling correlations and aligned features between arbitrary pairs of information sources. Building upon OPAM, we propose a Parallax Multi Information Fusion Module (PMIFM) to adaptively integrate information from different sources. PMIFM is incorporated into both the joint decoder and the entropy model to construct our end-to-end DMIC framework,

\textbf{ParaHydra}

. Extensive experiments demonstrate that

\textbf{ParaHydra}

is

\textbf{the first DMIC method}

to significantly surpass state-of-the-art MIC codecs, while maintaining low computational overhead. Performance gains become more pronounced as the number of input views increases. Compared with LDMIC,

\textbf{ParaHydra}

achieves bitrate savings of

\textbf{19.72%}

on WildTrack(3) and up to

\textbf{24.18%}

on WildTrack(6), while significantly improving coding efficiency (as much as

\textbf{65}\times

in decoding and

\textbf{34}\times

in encoding).

Haotian Zhang, Feiyue Long, Yixin Yu +7

The National Engineering Laboratory for Video Technology, School of Computer Science, Peking University · Institute for AI Industry Research (AIR), Tsinghua University · Tsinghua Shenzhen International Graduate School, Tsinghua University

Cross-View Attention Learned Image Compression Cross-Attention

Mar 3, 2026

ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization

Recent advances in generative image compression (GIC) have delivered remarkable improvements in perceptual quality. However, many GICs rely on large-scale and rigid models, which severely constrain their utility for flexible transmission and practical deployment in low-bitrate scenarios. To address these issues, we propose Progressive Generative Image Compression (ProGIC), a compact codec built on residual vector quantization (RVQ). In RVQ, a sequence of vector quantizers encodes the residuals stage by stage, each with its own codebook. The resulting codewords sum to a coarse-to-fine reconstruction and a progressive bitstream, enabling previews from partial data. We pair this with a lightweight backbone based on depthwise-separable convolutions and small attention blocks, enabling practical deployment on both GPUs and CPU-only devices. Experimental results show that ProGIC attains comparable compression performance compared with previous methods. It achieves bitrate savings of up to 57.57% on DISTS and 58.83% on LPIPS compared to MS-ILLM on the Kodak dataset. Beyond perceptual quality, ProGIC enables progressive transmission for flexibility, and also delivers over 10 times faster encoding and decoding compared with MS-ILLM on GPUs for efficiency.

Hao Cao, Chengbin Liang, Wenqi Guo +2

Tsinghua University · State Key Laboratory of Space Network and Communications

Generative Image Compression Learned Image Compression Residual VQ

Feb 25, 2026

OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data

Lossless compression is essential for efficient data storage and transmission. Although learning-based lossless compressors achieve strong results, most of them are designed for a single modality, leading to redundant compressor deployments in multi-modal settings. Designing a unified multi-modal compressor is critical yet challenging, as different data types vary largely in format, dimension, and statistics. Multi-modal large language models offer a promising resolution but remain too complex for practical use. Thus, we propose OmniZip, a unified and lightweight lossless compressor for multi-modal data (like image, text, speech, tactile, database, and gene sequence). Built on a lightweight backbone, OmniZip incorporates three key components to enable efficient multi-modal lossless compression: a modality-unified tokenizer that reversibly transforms diverse data into tokens, a modality-routing context learning mechanism that enables flexible multi-modal context modeling, and a modality-routing feedforward design that further enhances the model's nonlinear representation flexibility. A reparameterization training strategy is used to enhance model capacity. OmniZip outperforms or matches other state-of-the-art compressors on multiple modalities, achieving 42%, 57%, 62% and 42%, 53% higher compression efficiency than gzip on CLIC-M, TouchandGo, enwik9, LibriSpeech, and WikiSQL datasets, respectively. It also supports near real-time inference on resource-constrained edge devices, reaching about 1MB/s on MacBook CPUs and iPhone NPUs. Our code is released at https://github.com/adminasmi/OmniZip-CVPR2026.

Yan Zhao, Zhengxue Cheng, Junxuan Zhang +4

Shanghai Jiao Tong University · Ant Group

Lossless Data Compression Unified Multimodal Models Learned Image Compression

Feb 25, 2026

CADC: Content Adaptive Diffusion-Based Generative Image Compression

Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential lies in making the entire compression process content-adaptive, ensuring that the encoder's representation and the decoder's generative prior are dynamically aligned with the semantic and structural characteristics of the input image. However, existing methods suffer from three critical limitations that prevent effective content adaptation. First, isotropic quantization applies a uniform quantization step, failing to adapt to the spatially varying complexity of image content and creating a misalignment with the diffusion model's noise-dependent prior. Second, the information concentration bottleneck -- arising from the dimensional mismatch between the high-dimensional noisy latent and the diffusion decoder's fixed input -- prevents the model from adaptively preserving essential semantic information in the primary channels. Third, existing textual conditioning strategies either need significant textual bitrate overhead or rely on generic, content-agnostic textual prompts, thereby failing to provide adaptive semantic guidance efficiently. To overcome these limitations, we propose a content-adaptive diffusion-based image codec with three technical innovations: 1) an Uncertainty-Guided Adaptive Quantization method that learns spatial uncertainty maps to adaptively align quantization distortion with content characteristics; 2) an Auxiliary Decoder-Guided Information Concentration method that uses a lightweight auxiliary decoder to enforce content-aware information preservation in the primary latent channels; and 3) a Bitrate-Free Adaptive Textual Conditioning method that derives content-aware textual descriptions from the auxiliary reconstructed image, enabling semantic guidance without bitrate cost.

Xihua Sheng, Lingyu Zhu, Tianyu Zhang +3

City University of Hong Kong · University of Science and Technology of China · Central Media Technology Institute, Huawei

Generative Image Compression Learned Image Compression

Jan 26, 2026

Advances in Diffusion-Based Generative Compression

Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success in visual media applications. In particular, diffusion methods have enabled new approaches to data compression, where realistic reconstructions can be generated at extremely low bit-rates. This article provides a unifying review of recent diffusion-based methods for generative lossy compression, with a focus on image compression. These methods generally encode the source into an embedding and use a diffusion model to iteratively refine it during decoding, so that the reconstruction approximately follows the true data distribution. The embedding can take various forms and is typically transmitted via an auxiliary entropy model, and recent methods also explore the use of diffusion models themselves for information transmission via channel simulation. We review representative approaches through the lens of rate-distortion-perception theory, highlighting the role of common randomness and connections to inverse problems, and identify open challenges.

Yibo Yang, Stephan Mandt

Chan Zuckerberg Biohub · University of California Irvine

Learned Image Compression Generative Image Compression Diffusion Models Diffusion Model Compression

Jan 11, 2026

Enhancing Low-resolution Image Representation Through Normalizing Flows

Low-resolution image representation can be regarded as a special form of sparse representation that retains only low-frequency information while discarding high-frequency components. This property reduces storage and transmission costs and benefits various image processing tasks. However, a key challenge is to preserve essential visual content while maintaining the ability to accurately reconstruct the original images. This work proposes LR2Flow, a nonlinear framework that learns low-resolution image representations by integrating wavelet tight frame blocks with normalizing flows. We conduct a reconstruction error analysis of the proposed network, which demonstrates the necessity of designing invertible neural networks in the wavelet tight frame domain. Experimental results on various tasks, including image rescaling, compression, and denoising, demonstrate the effectiveness of the learned representations and the robustness of the proposed framework. Code is available at https://github.com/Evanescentlove/LR2Flow-main.

Chenglong Bao, Tongyao Pang, Zuowei Shen +2

Yau Mathematical Sciences Center, Tsinghua University, Beijing, China, and Beijing Institute of Mathematical Sciences and Applications, Beijing, China · Yau Mathematical Sciences Center, Tsinghua University, Beijing, China · Department of Mathematics, National University of Singapore, Singapore +2

Learned Image Compression Normalizing Flows Visual Representation Learning

Dec 20, 2025

SLIM: Semantic-based Low-bitrate Image compression for Machines

In recent years, the demand of image compression models for machine vision has increased dramatically. However, the training frameworks of image compression still focus on the vision of human, preserving unnecessary details and limiting bitrate efficiency for machine vision tasks. In this paper, we propose Semantic-based Low-bitrate Image compression for Machines, termed SLIM. This proposed framework consists of two key components: RoI-focused latent compression and semantic latent enhancement with dual cues. First, the latent compressor is trained to focus on Region-of-Interest (RoI) areas that are semantically and functionally important for machine vision tasks in the latent space, while requiring no explicit masks during inference. Second, to compensate for severe information loss at extremely low bitrates, a diffusion-based semantic decoder enhances the compressed latent with a pretrained diffusion model guided by two complementary cues provided by an RoI-focused latent and text-based semantic guidance. This design preserves RoI details that are critical for machine perception while maintaining semantically coherent and artifact-suppressed non-RoI regions under extremely low bitrates. Experimental results show that SLIM consistently achieves strong performance across diverse machine vision tasks under the same bits per pixel condition, compared to conventional image compression models for machines. Our code is available at https://github.com/mcml-official/SLIM

Hyeonjin Lee, Jun-Hyuk Kim, Jong-Seok Lee

School of Integrated Technology, Yonsei University, Republic of Korea · Department of Artificial Intelligence, Chung-Ang University, Republic of Korea

Learned Image Compression Generative Image Compression

Dec 6, 2025

SDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates

Generative image compression has recently shown impressive perceptual quality, but often suffers from semantic inconsistency at ultra-low bitrates (bpp < 0.05), limiting its reliable deployment in bandwidth-constrained scenarios such as 6G semantic communications. This inconsistency stems from incomplete guidance information, which introduces semantic ambiguity into the generation process and may lead to natural-looking but source-inconsistent content. In this work, we propose a Semantic-Disambiguation-Guided Generative Image Compression (SDGIC) framework to constrain diffusion-based reconstruction at ultra-low bitrates. Specifically, SDGIC compresses the source image into three compact and complementary guidance streams: a concise text caption for global semantics, a highly compressed image (HCI) for dense visual evidence, and Reconstruction-Aware Semantic Residual Tokens (RSRTs) for reconstruction-relevant residual semantics that remain ambiguous under the text caption and HCI conditions. The RSRTs are directly optimized toward the downstream denoising objective, enabling them to provide source-specific semantic constraints for disambiguating diffusion-based reconstruction. To inject these three guidance streams into the generation process effectively, we design a Dual-Path Conditioned Diffusion Decoder (DPCD), which uses cross-attention for semantic conditions and ControlNet residuals for dense visual guidance. Extensive experiments demonstrate that SDGIC improves semantic consistency at ultra-low bitrates while maintaining favorable perceptual quality, with a 23.4% reduction in AFINE on the CLIC2020 dataset.

Kaile Wang, Lijun He, Haisheng Fu +2

School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China · Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, BC, Canada

Diffusion Model Guidance Learned Image Compression Conditional Diffusion Models Generative Image Compression

Oct 16, 2025

Real-Time Neural Video Compression with Unified Intra and Inter Coding

Neural video compression (NVC) technologies have advanced rapidly in recent years, yielding state-of-the-art schemes such as DCVC-RT that offer superior compression efficiency to H.266/VVC and real-time encoding/decoding capabilities. Nonetheless, existing NVC schemes have several limitations, including inefficiency in dealing with disocclusion and new content, interframe error propagation and accumulation, among others. To eliminate these limitations, we borrow the idea from classic video coding schemes, which allow intra coding within inter-coded frames. With the intra coding tool enabled, disocclusion and new content are properly handled, and interframe error propagation is naturally intercepted without the need for manual refresh mechanisms. We present an NVC framework with unified intra and inter coding, where every frame is processed by a single model that is trained to perform intra/inter coding adaptively. Moreover, we propose a simultaneous two-frame compression design to exploit interframe redundancy not only forwardly but also backwardly. Experimental results show that our scheme outperforms DCVC-RT by an average of 12.1% BD-rate reduction, delivers more stable bitrate and quality per frame, and retains real-time encoding/decoding performances. Code and models will be released.

Hui Xiang, Yifan Bian, Li Li +3

University of Science and Technology of China · Tencent Shannon Lab

Learned Image Compression Neural Video Compression Video Compression Neural Network Compression

Sep 26, 2025

Pushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization

Vector quantization, which discretizes a continuous vector space into a finite set of representative vectors (a codebook), has been widely adopted in modern machine learning. Despite its effectiveness, vector quantization poses a fundamental challenge: the non-differentiable quantization step blocks gradient backpropagation. Smoothed vector quantization addresses this issue by relaxing the discrete selection of a codebook vector into a weighted combination of codebook entries, represented as the matrix product of a simplex vector and the codebook. Effective smoothing requires two properties: (1) smoothed code-selection vectors should remain close to a onehot vector, ensuring tight approximation, and (2) all codebook entries should be utilized, preventing code collapse. Existing methods typically address these desiderata separately. By contrast, the present study introduces a simple and intuitive regularization that promotes both simultaneously by minimizing the distance between each simplex vertex and its

K

-nearest smoothed code-selectors. Representative benchmarks on image encoding demonstrate that the proposed method achieves more effective codebook utilization and improves performance over prior approaches.

Takashi Morita

Academy of Emerging Sciences, Chubu University · Institute for Advanced Research, Nagoya University

Learned Image Compression Vector Quantization

Mar 12, 2025

PerCoV2: Ultra-Low Bit-Rate Perceptual Image Compression via Query-Based 1D Multimodal Image Tokens

Despite recent progress in learned image compression, current image codecs still struggle to maintain realistic reconstructions at low bit-rates, often producing structured artifacts such as grid patterns or repetitive textures, even when trained with perceptual or adversarial losses. We introduce PerCoV2, an ultra-low bit-rate perceptual image compression system that unifies semantic tokenization, flow-based generation, and learned entropy modeling within a single framework. Building on the fully open flow-based SANA architecture, PerCoV2 introduces a novel resolution-adaptive 1D query-based tokenizer that produces compact semantic image tokens with a dual role in flow matching: providing a data-dependent reconstruction prior for initialization and a conditioning signal for flow-based refinement. By explicitly decoupling semantic representation from perceptual generation, our dual representation simplifies the flow-based learning objective, leading to more stable optimization and improved perceptual compression performance. PerCoV2 further introduces a dedicated 1D masked entropy model to improve rate efficiency and optional decoder-side multimodal enhancement via a vision-language model (Molmo) without increasing the transmitted bit budget. On MSCOCO-30k, PerCoV2 achieves state-of-the-art statistical fidelity, measured by FID and KID, across ultra-low and extreme bit-rates (0.0015-0.025 bpp). When trained solely on the general-purpose SA-1B dataset, PerCoV2 further demonstrates strong zero-shot generalization to widely adopted high-resolution benchmarks, including DIV2K and CLIC 2020, achieving competitive statistical fidelity with the current leading method, AEIC-ME. Finally, we introduce PerCoV2-distilled, a practical single-step variant derived from multi-step flow matching that accelerates decoding by 5.37x over PerCoV1, while preserving perceptual compression performance.

Nikolai Körber, Eduard Kromer, Andreas Siebert +3

Technical University of Munich · University of Applied Sciences Landshut · TU Wien

Learned Image Compression Generative Image Compression Visual Token Compression

Mar 25, 2023

LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity. Code is available at: https://github.com/xzhang9308/LVQAC.

Xi Zhang, Xiaolin Wu

Shanghai Jiao Tong University · McMaster University

Learned Image Compression Vector Quantization Rate-Distortion Optimization